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Jimenez AD, Gopaul M, Asbell H, Aydemir S, Basha MM, Batra A, Damien C, Day GS, Eka O, Eschbach K, Fatima S, Fields MC, Foreman B, Gerard EE, Gofton TE, Haider HA, Hantus ST, Hocker S, Jongeling A, Kalkach Aparicio M, Kandula P, Kang P, Kazazian K, Kellogg MA, Kim M, Lee JW, Marcuse LV, McGraw CM, Mohamed W, Orozco J, Pimentel C, Punia V, Ramirez AM, Steriade C, Struck AF, Taraschenko O, Treister AK, Yoo JY, Zafar S, Zhou DJ, Zutshi D, Gaspard N, Hirsch LJ, Hanin A. Comparative analysis of patients with new onset refractory status epilepticus preceded by fever (febrile infection-related epilepsy syndrome) versus without prior fever: An interim analysis. Epilepsia 2024. [PMID: 38625055 DOI: 10.1111/epi.17988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/04/2024] [Accepted: 04/04/2024] [Indexed: 04/17/2024]
Abstract
Febrile infection-related epilepsy syndrome (FIRES) is a subset of new onset refractory status epilepticus (NORSE) that involves a febrile infection prior to the onset of the refractory status epilepticus. It is unclear whether FIRES and non-FIRES NORSE are distinct conditions. Here, we compare 34 patients with FIRES to 30 patients with non-FIRES NORSE for demographics, clinical features, neuroimaging, and outcomes. Because patients with FIRES were younger than patients with non-FIRES NORSE (median = 28 vs. 48 years old, p = .048) and more likely cryptogenic (odds ratio = 6.89), we next ran a regression analysis using age or etiology as a covariate. Respiratory and gastrointestinal prodromes occurred more frequently in FIRES patients, but no difference was found for non-infection-related prodromes. Status epilepticus subtype, cerebrospinal fluid (CSF) and magnetic resonance imaging findings, and outcomes were similar. However, FIRES cases were more frequently cryptogenic; had higher CSF interleukin 6, CSF macrophage inflammatory protein-1 alpha (MIP-1a), and serum chemokine ligand 2 (CCL2) levels; and received more antiseizure medications and immunotherapy. After controlling for age or etiology, no differences were observed in presenting symptoms and signs or inflammatory biomarkers, suggesting that FIRES and non-FIRES NORSE are very similar conditions.
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Affiliation(s)
- Anthony D Jimenez
- Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Margaret Gopaul
- Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Hannah Asbell
- Section of Neurology, Department of Pediatrics, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Seyhmus Aydemir
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Maysaa M Basha
- Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Ayush Batra
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Charlotte Damien
- Department of Neurology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Gregory S Day
- Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Onome Eka
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Krista Eschbach
- Section of Neurology, Department of Pediatrics, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Safoora Fatima
- Department of Neurology, University of Wisconsin, Madison, Wisconsin, USA
| | | | - Brandon Foreman
- Division of Neurocritical Care, Department of Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Elizabeth E Gerard
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Teneille E Gofton
- University Hospital London Health Sciences Center, London, Ontario, Canada
| | - Hiba A Haider
- Epilepsy Center, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Neurology, University of Chicago, Chicago, Illinois, USA
| | - Stephen T Hantus
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Sara Hocker
- Mayo Clinic, Minnesota, Rochester, Minnesota, USA
| | - Amy Jongeling
- NYU Comprehensive Epilepsy Center, NYU Langone Medical Center, New York, New York, USA
| | | | - Padmaja Kandula
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Peter Kang
- Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Karnig Kazazian
- University Hospital London Health Sciences Center, London, Ontario, Canada
| | | | - Minjee Kim
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Lara V Marcuse
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Christopher M McGraw
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wazim Mohamed
- Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Janet Orozco
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Cederic Pimentel
- Neurocritical Care, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Vineet Punia
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Alexandra M Ramirez
- Division of Neurocritical Care, Department of Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Claude Steriade
- NYU Comprehensive Epilepsy Center, NYU Langone Medical Center, New York, New York, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin, Madison, Wisconsin, USA
| | - Olga Taraschenko
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | | | - Ji Yeoun Yoo
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sahar Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Daniel J Zhou
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Deepti Zutshi
- Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Nicolas Gaspard
- Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, New Haven, Connecticut, USA
- Department of Neurology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Lawrence J Hirsch
- Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Aurelie Hanin
- Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, New Haven, Connecticut, USA
- Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, Inserm, CNRS, Assistance Publique- Hôpitaux de Paris, Hôpital de la Pitié-Salpêtrière, Paris, France
- Assistance Publique - Hôpitaux de Paris, Hôpital de la Pitié-Salpêtrière, DMU Neurosciences, Epilepsy Unit and Department of Clinical Neurophysiology, Paris, France
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Fatima S, Krishnamurthy PV, Sun M, Aparicio MK, Gjini K, Struck AF. Estimate of Patients With Missed Seizures Because of Delay in Conventional EEG. J Clin Neurophysiol 2024; 41:230-235. [PMID: 38436390 PMCID: PMC10912745 DOI: 10.1097/wnp.0000000000000957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE There is frequent delay between ordering and placement of conventional EEG. Here we estimate how many patients had seizures during this delay. METHODS Two hundred fifty consecutive adult patients who underwent conventional EEG monitoring at the University of Wisconsin Hospital were retrospectively chart reviewed for demographics, time of EEG order, clinical and other EEG-related information. Patients were stratified by use of anti-seizure medications before EEG and into low-risk, medium-risk, and high-risk groups based on 2HELPS2B score (0, 1, or >1). Monte Carlo simulations (500 trials) were performed to estimate seizures during delay. RESULTS The median delay from EEG order to performing EEG was 2.00 hours (range of 0.5-8.00 hours) in the total cohort. For EEGs ordered after-hours, it was 2.00 hours (range 0.5-8.00 hours), and during business hours, it was 2.00 hours (range 0.5-6.00 hours). The place of EEG, intensive care unit, emergency department, and general floor, did not show significant difference (P = 0.84). Anti-seizure medication did not affect time to first seizure in the low-risk (P = 0.37), medium-risk (P = 0.44), or high-risk (P = 0.12) groups. The estimated % of patients who had a seizure in the delay period for low-risk group (2HELPS2B = 0) was 0.8%, for the medium-risk group (2HELPS2B = 1) was 10.3%, and for the high-risk group (2HELPS2B > 1) was 17.6%, and overall risk was 7.2%. CONCLUSIONS The University of Wisconsin Hospital with 24-hour in-house EEG technologists has a median delay of 2 hours from order to start of EEG, shorter than published reports from other centers. Nonetheless, seizures were likely missed in about 7.2% of patients.
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Affiliation(s)
- Safoora Fatima
- University of Wisconsin-Madison, Department of Neurology
| | | | - Mengzhen Sun
- University of Wisconsin-Madison, Department of Neurology
| | | | - Klevest Gjini
- University of Wisconsin-Madison, Department of Neurology
| | - Aaron F Struck
- University of Wisconsin-Madison, Department of Neurology
- William S Middleton Veterans Hospital, Madison, WI
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Chung MK, Ramos CG, De Paiva FB, Mathis J, Prabhakaran V, Nair VA, Meyerand ME, Hermann BP, Binder JR, Struck AF. Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance. Neuroimage 2023; 284:120436. [PMID: 37931870 PMCID: PMC11074922 DOI: 10.1016/j.neuroimage.2023.120436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/14/2023] [Accepted: 10/30/2023] [Indexed: 11/08/2023] Open
Abstract
Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.
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Affiliation(s)
- Moo K Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA.
| | | | | | | | | | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, USA.
| | - Mary E Meyerand
- Departments of Medical Physics & Biomedical Engineering, University of Wisconsin-Madison, USA.
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin-Madison, USA.
| | | | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, USA.
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4
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Ciliento R, Gjini K, Dabbs K, Hermann B, Riedner B, Jones S, Fatima S, Johnson S, Bendlin B, Lam AD, Boly M, Struck AF. Prevalence and localization of nocturnal epileptiform discharges in mild cognitive impairment. Brain Commun 2023; 5:fcad302. [PMID: 37965047 PMCID: PMC10642616 DOI: 10.1093/braincomms/fcad302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/18/2023] [Accepted: 11/06/2023] [Indexed: 11/16/2023] Open
Abstract
Recent evidence shows that identifying and treating epileptiform abnormalities in patients with Alzheimer's disease could represent a potential avenue to improve clinical outcome. Specifically, animal and human studies have revealed that in the early phase of Alzheimer's disease, there is an increased risk of seizures. It has also been demonstrated that the administration of anti-seizure medications can slow the functional progression of the disease only in patients with EEG signs of cortical hyperexcitability. In addition, although it is not known at what disease stage hyperexcitability emerges, there remains no consensus regarding the imaging and diagnostic methods best able to detect interictal events to further distinguish different phenotypes of Alzheimer's disease. In this exploratory work, we studied 13 subjects with amnestic mild cognitive impairment and 20 healthy controls using overnight high-density EEG with 256 channels. All participants also underwent MRI and neuropsychological assessment. Electronic source reconstruction was also used to better select and localize spikes. We found spikes in six of 13 (46%) amnestic mild cognitive impairment compared with two of 20 (10%) healthy control participants (P = 0.035), representing a spike prevalence similar to that detected in previous studies of patients with early-stage Alzheimer's disease. The interictal events were low-amplitude temporal spikes more prevalent during non-rapid eye movement sleep. No statistically significant differences were found in cognitive performance between amnestic mild cognitive impairment patients with and without spikes, but a trend in immediate and delayed memory was observed. Moreover, no imaging findings of cortical and subcortical atrophy were found between amnestic mild cognitive impairment participants with and without epileptiform spikes. In summary, our exploratory study shows that patients with amnestic mild cognitive impairment reveal EEG signs of hyperexcitability early in the disease course, while no other significant differences in neuropsychological or imaging features were observed among the subgroups. If confirmed with longitudinal data, these exploratory findings could represent one of the first signatures of a preclinical epileptiform phenotype of amnestic mild cognitive impairment and its progression.
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Affiliation(s)
- Rosario Ciliento
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Klevest Gjini
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Brady Riedner
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
| | - Stephanie Jones
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
| | - Safoora Fatima
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Sterling Johnson
- Department of Medicine, University of Wisconsin, Madison, WI 53705, USA
| | - Barbara Bendlin
- Department of Medicine, University of Wisconsin, Madison, WI 53705, USA
| | - Alice D Lam
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02115, USA
| | - Melanie Boly
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
- Department of Neurology, William S. Middleton Veterans Administration Hospital, Madison, WI 53705, USA
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5
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Denis C, Dabbs K, Nair VA, Mathis J, Almane DN, Lakshmanan A, Nencka A, Birn RM, Conant L, Humphries C, Felton E, Raghavan M, DeYoe EA, Binder JR, Hermann B, Prabhakaran V, Bendlin BB, Meyerand ME, Boly M, Struck AF. T1-/T2-weighted ratio reveals no alterations to gray matter myelination in temporal lobe epilepsy. Ann Clin Transl Neurol 2023; 10:2149-2154. [PMID: 37872734 PMCID: PMC10647008 DOI: 10.1002/acn3.51653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/29/2022] [Accepted: 06/09/2022] [Indexed: 10/25/2023] Open
Abstract
Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.
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Affiliation(s)
- Colin Denis
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Kevin Dabbs
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Veena A. Nair
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Jedidiah Mathis
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Dace N. Almane
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | | | - Andrew Nencka
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Rasmus M. Birn
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Lisa Conant
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Colin Humphries
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Elizabeth Felton
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Manoj Raghavan
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Edgar A. DeYoe
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Jeffrey R. Binder
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Bruce Hermann
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Vivek Prabhakaran
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Mary E. Meyerand
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Mélanie Boly
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Aaron F. Struck
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- William S. Middleton Veterans Administration HospitalMadisonWisconsinUSA
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6
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Chung MK, Ramos CG, De Paiva FB, Mathis J, Prabharakaren V, Nair VA, Meyerand E, Hermann BP, Binder JR, Struck AF. Unified Topological Inference for Brain Networks in Temporal Lobe Epilepsy Using the Wasserstein Distance. ArXiv 2023:arXiv:2302.06673v3. [PMID: 36824424 PMCID: PMC9949148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.
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Affiliation(s)
- Moo K Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA
| | | | | | | | | | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, USA
| | - Elizabeth Meyerand
- Departments of Medical Physics & Biomedical Engineering, University of Wisconsin-Madison, USA
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin-Madison, USA
| | | | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, USA
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7
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Chu DY, Adluru N, Nair VA, Choi T, Adluru A, Garcia-Ramos C, Dabbs K, Mathis J, Nencka AS, Gundlach C, Conant L, Binder JR, Meyerand ME, Alexander AL, Struck AF, Hermann B, Prabhakaran V. Association of neighborhood deprivation with white matter connectome abnormalities in temporal lobe epilepsy. Epilepsia 2023; 64:2484-2498. [PMID: 37376741 PMCID: PMC10530287 DOI: 10.1111/epi.17702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 06/29/2023]
Abstract
OBJECTIVE Social determinants of health, including the effects of neighborhood disadvantage, impact epilepsy prevalence, treatment, and outcomes. This study characterized the association between aberrant white matter connectivity in temporal lobe epilepsy (TLE) and disadvantage using a US census-based neighborhood disadvantage metric, the Area Deprivation Index (ADI), derived from measures of income, education, employment, and housing quality. METHODS Participants including 74 TLE patients (47 male, mean age = 39.2 years) and 45 healthy controls (27 male, mean age = 31.9 years) from the Epilepsy Connectome Project were classified into ADI-defined low and high disadvantage groups. Graph theoretic metrics were applied to multishell connectome diffusion-weighted imaging (DWI) measurements to derive 162 × 162 structural connectivity matrices (SCMs). The SCMs were harmonized using neuroCombat to account for interscanner differences. Threshold-free network-based statistics were used for analysis, and findings were correlated with ADI quintile metrics. A decrease in cross-sectional area (CSA) indicates reduced white matter integrity. RESULTS Sex- and age-adjusted CSA in TLE groups was significantly reduced compared to controls regardless of disadvantage status, revealing discrete aberrant white matter tract connectivity abnormalities in addition to apparent differences in graph measures of connectivity and network-based statistics. When comparing broadly defined disadvantaged TLE groups, differences were at trend level. Sensitivity analyses of ADI quintile extremes revealed significantly lower CSA in the most compared to least disadvantaged TLE group. SIGNIFICANCE Our findings demonstrate (1) the general impact of TLE on DWI connectome status is larger than the association with neighborhood disadvantage; however, (2) neighborhood disadvantage, indexed by ADI, revealed modest relationships with white matter structure and integrity on sensitivity analysis in TLE. Further studies are needed to explore this relationship and determine whether the white matter relationship with ADI is driven by social drift or environmental influences on brain development. Understanding the etiology and course of the disadvantage-brain integrity relationship may serve to inform care, management, and policy for patients.
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Affiliation(s)
- Daniel Y Chu
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Nagesh Adluru
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Veena A Nair
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Timothy Choi
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Anusha Adluru
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Camille Garcia-Ramos
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Jedidiah Mathis
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Andrew S Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Carson Gundlach
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Lisa Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- William S. Middleton Veterans Hospital, Madison, Wisconsin, USA
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Vivek Prabhakaran
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
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8
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Garcia-Ramos C, Adluru N, Chu DY, Nair V, Adluru A, Nencka A, Maganti R, Mathis J, Conant LL, Alexander AL, Prabhakaran V, Binder JR, Meyerand ME, Hermann B, Struck AF. Multi-shell connectome DWI-based graph theory measures for the prediction of temporal lobe epilepsy and cognition. Cereb Cortex 2023; 33:8056-8065. [PMID: 37067514 PMCID: PMC10267614 DOI: 10.1093/cercor/bhad098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 04/18/2023] Open
Abstract
Temporal lobe epilepsy (TLE) is the most common epilepsy syndrome that empirically represents a network disorder, which makes graph theory (GT) a practical approach to understand it. Multi-shell diffusion-weighted imaging (DWI) was obtained from 89 TLE and 50 controls. GT measures extracted from harmonized DWI matrices were used as factors in a support vector machine (SVM) analysis to discriminate between groups, and in a k-means algorithm to find intrinsic structural phenotypes within TLE. SVM was able to predict group membership (mean accuracy = 0.70, area under the curve (AUC) = 0.747, Brier score (BS) = 0.264) using 10-fold cross-validation. In addition, k-means clustering identified 2 TLE clusters: 1 similar to controls, and 1 dissimilar. Clusters were significantly different in their distribution of cognitive phenotypes, with the Dissimilar cluster containing the majority of TLE with cognitive impairment (χ2 = 6.641, P = 0.036). In addition, cluster membership showed significant correlations between GT measures and clinical variables. Given that SVM classification seemed driven by the Dissimilar cluster, SVM analysis was repeated to classify Dissimilar versus Similar + Controls with a mean accuracy of 0.91 (AUC = 0.957, BS = 0.189). Altogether, the pattern of results shows that GT measures based on connectome DWI could be significant factors in the search for clinical and neurobehavioral biomarkers in TLE.
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Affiliation(s)
- Camille Garcia-Ramos
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
| | - Nagesh Adluru
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI 53705, United States
| | - Daniel Y Chu
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
| | - Veena Nair
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
| | - Anusha Adluru
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, 9200 W. Wisconsin Ave. Milwaukee, WI 53226, United States
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
| | - Jedidiah Mathis
- Department of Neurology, Medical College of Wisconsin, 9200 W. Wisconsin Ave. Milwaukee, WI 53226, United States
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, 9200 W. Wisconsin Ave. Milwaukee, WI 53226, United States
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI 53705, United States
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave, Rm 1005, Madison, WI 53705-2275, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, 9200 W. Wisconsin Ave. Milwaukee, WI 53226, United States
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave, Rm 1005, Madison, WI 53705-2275, United States
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
- William S. Middleton VA Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States
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Jing J, Ge W, Struck AF, Fernandes MB, Hong S, An S, Fatima S, Herlopian A, Karakis I, Halford JJ, Ng MC, Johnson EL, Appavu BL, Sarkis RA, Osman G, Kaplan PW, Dhakar MB, Jayagopal LA, Sheikh Z, Taraschenko O, Schmitt S, Haider HA, Kim JA, Swisher CB, Gaspard N, Cervenka MC, Rodriguez Ruiz AA, Lee JW, Tabaeizadeh M, Gilmore EJ, Nordstrom K, Yoo JY, Holmes MG, Herman ST, Williams JA, Pathmanathan J, Nascimento FA, Fan Z, Nasiri S, Shafi MM, Cash SS, Hoch DB, Cole AJ, Rosenthal ES, Zafar SF, Sun J, Westover MB. Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs. Neurology 2023; 100:e1737-e1749. [PMID: 36460472 PMCID: PMC10136018 DOI: 10.1212/wnl.0000000000201670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/25/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.
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Affiliation(s)
- Jin Jing
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Wendong Ge
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Aaron F Struck
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Marta Bento Fernandes
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Shenda Hong
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sungtae An
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Safoora Fatima
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Aline Herlopian
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ioannis Karakis
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jonathan J Halford
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Marcus C Ng
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Emily L Johnson
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Brian L Appavu
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Rani A Sarkis
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Gamaleldin Osman
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Peter W Kaplan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Monica B Dhakar
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Lakshman Arcot Jayagopal
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Zubeda Sheikh
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Olga Taraschenko
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sarah Schmitt
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Hiba A Haider
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jennifer A Kim
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Christa B Swisher
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Nicolas Gaspard
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mackenzie C Cervenka
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Andres A Rodriguez Ruiz
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jong Woo Lee
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mohammad Tabaeizadeh
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Emily J Gilmore
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Kristy Nordstrom
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ji Yeoun Yoo
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Manisha G Holmes
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Susan T Herman
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jennifer A Williams
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jay Pathmanathan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Fábio A Nascimento
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ziwei Fan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Samaneh Nasiri
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mouhsin M Shafi
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sydney S Cash
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Daniel B Hoch
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Andrew J Cole
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Eric S Rosenthal
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sahar F Zafar
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jimeng Sun
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - M Brandon Westover
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL.
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Jing J, Ge W, Hong S, Fernandes MB, Lin Z, Yang C, An S, Struck AF, Herlopian A, Karakis I, Halford JJ, Ng MC, Johnson EL, Appavu BL, Sarkis RA, Osman G, Kaplan PW, Dhakar MB, Arcot Jayagopal L, Sheikh Z, Taraschenko O, Schmitt S, Haider HA, Kim JA, Swisher CB, Gaspard N, Cervenka MC, Rodriguez Ruiz AA, Lee JW, Tabaeizadeh M, Gilmore EJ, Nordstrom K, Yoo JY, Holmes MG, Herman ST, Williams JA, Pathmanathan J, Nascimento FA, Fan Z, Nasiri S, Shafi MM, Cash SS, Hoch DB, Cole AJ, Rosenthal ES, Zafar SF, Sun J, Westover MB. Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation. Neurology 2023; 100:e1750-e1762. [PMID: 36878708 PMCID: PMC10136013 DOI: 10.1212/wnl.0000000000207127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/12/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.
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Affiliation(s)
- Jin Jing
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Wendong Ge
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Shenda Hong
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Marta Bento Fernandes
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Zhen Lin
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Chaoqi Yang
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sungtae An
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Aaron F Struck
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Aline Herlopian
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Ioannis Karakis
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jonathan J Halford
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Marcus C Ng
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Emily L Johnson
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Brian L Appavu
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Rani A Sarkis
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Gamaleldin Osman
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Peter W Kaplan
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Monica B Dhakar
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Lakshman Arcot Jayagopal
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Zubeda Sheikh
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Olga Taraschenko
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sarah Schmitt
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Hiba A Haider
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jennifer A Kim
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Christa B Swisher
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Nicolas Gaspard
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Mackenzie C Cervenka
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Andres A Rodriguez Ruiz
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jong Woo Lee
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Mohammad Tabaeizadeh
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Emily J Gilmore
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Kristy Nordstrom
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Ji Yeoun Yoo
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Manisha G Holmes
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Susan T Herman
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jennifer A Williams
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jay Pathmanathan
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Fábio A Nascimento
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Ziwei Fan
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Samaneh Nasiri
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Mouhsin M Shafi
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sydney S Cash
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Daniel B Hoch
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Andrew J Cole
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Eric S Rosenthal
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Sahar F Zafar
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - Jimeng Sun
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA
| | - M Brandon Westover
- From the Department of Neurology (J.J., W.G., M.B.F., M.T., K.N., F.A.N., Z.F., S.N., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), Massachusetts General Hospital, Harvard Medical School, Boston; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., M.T., F.A.N., Z.F., S.N., S.S.C., D.B.H., S.F.Z., M.B.W.), Boston; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; College of Computing (Z.L., C.Y., J.S.), University of Illinois at Urbana-Champaign; College of Computing (S.A.), Georgia Institute of Technology, Atlanta; Department of Neurology (A.F.S.), University of Wisconsin-Madison; William S. Middleton Memorial Veterans Hospital (A.F.S.), Madison, WI; Yale New Haven Hospital (A.H., J.A.K., E.J.G.), Yale University, CT; Emory University School of Medicine (I.K., A.A.R.R.), Atlanta, GA; Medical University of South Carolina (J.J.H., S.S.), Charleston; University of Manitoba (M.C.N.), Winnipeg, Canada; Johns Hopkins School of Medicine (E.L.J., P.W.K., M.C.C.), Baltimore, MD; University of Arizona College of Medicine (B.L.A.), Phoenix; Brigham and Women's Hospital (R.A.S., J.W.L.), Boston, MA; Mayo Clinic (G.O.), Rochester, MN; Warren Alpert School of Medicine (M.B.D.), Brown University, Providence, RI; University of Nebraska Medical Center (L.A.J., O.T.), Omaha; West Virginia University Hospitals (Z.S.), Morgantown; University of Chicago (H.A.H.), IL; Atrium Health (C.B.S.), Charlotte, NC; Hôpital Erasme (N.G.), Université Libre de Bruxelles, Belgium; Icahn School of Medicine (J.Y.Y.), Mount Sinai, NY; NYU Grossman School of Medicine (M.G.H.), New York; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), Philadelphia; and Beth Israel Deaconess Medical Center (M.M.S.), Harvard Medical School, Boston, MA.
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11
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Busch RM, Dalton JE, Jehi L, Ferguson L, Krieger NI, Struck AF, Hermann BP. Association of Neighborhood Deprivation With Cognitive and Mood Outcomes in Adults With Pharmacoresistant Temporal Lobe Epilepsy. Neurology 2023:WNL.0000000000207266. [PMID: 37076308 DOI: 10.1212/wnl.0000000000207266] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/21/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Temporal lobe epilepsy (TLE) is the most common adult form of epilepsy and is associated with high risk for cognitive deficits and depressed mood. However, little is known about the role of environmental factors on cognition and mood in TLE. This cross-sectional study examined the relationship between neighborhood deprivation and neuropsychological function in adults with TLE. METHODS Neuropsychological data were obtained from a clinical registry of patients with TLE and included measures of intelligence, attention, processing speed, language, executive function, visuospatial skills, verbal/visual memory, depression, and anxiety. Home addresses were used to calculate the Area Deprivation Index (ADI) for each individual, which were separated into quintiles (i.e., Quintile 1=least disadvantaged, Quintile 5=most disadvantaged). Kruskal-Wallis tests compared quintile groups on cognitive domain scores as well as mood and anxiety scores. Multivariable regression models, with and without ADI, were estimated for overall cognitive phenotype as well as for mood and anxiety scores. RESULTS 800 patients (median age 38 years-old; 58% female) met all inclusion criteria.Effects of disadvantage (increasing ADI) were observed across nearly all measured cognitive domains as well as with significant increases in symptoms of depression and anxiety. Further, patients in more disadvantaged ADI quintiles had increased odds of a worse cognitive phenotype (P=0.013). Patients who self-identified as members of minoritized groups were over-represented in the most disadvantaged ADI quintiles and were 2.91 (95% confidence interval, CI: 1.87-4.54) times more likely to be in a severe cognitive phenotype than non-Hispanic Whites (P < 0.001). However, accounting for ADI attenuated this relationship, suggesting neighborhood deprivation may account for some of the relationship between race/ethnicity and cognitive phenotype (ADI-adjusted proportional odds ratio [95% CI]: 1.82 [1.37 - 2.42]). DISCUSSION These findings highlight the importance of environmental factors and regional characteristics in neuropsychological studies of epilepsy. There are many potential mechanisms by which neighborhood disadvantage can adversely impact cognition (e.g., fewer educational opportunities, limited access to health care, food insecurity/poor nutrition, greater medical comorbidities). Future research will seek to investigate these potential mechanisms and to determine whether structural and functional alterations in the brain moderate the relationship between ADI and cognition.
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Affiliation(s)
- Robyn M Busch
- Epilepsy Center, Cleveland Clinic, Cleveland, OH
- Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH
| | - Jarrod E Dalton
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH
| | - Lara Jehi
- Epilepsy Center, Cleveland Clinic, Cleveland, OH
- Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH
- Center for Computational Life Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | | | - Nikolas I Krieger
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI
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12
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Struck AF, Garcia-Ramos C, Nair VA, Prabhakaran V, Dabbs K, Boly M, Conant LL, Binder JR, Meyerand ME, Hermann BP. The presence, nature and network characteristics of behavioural phenotypes in temporal lobe epilepsy. Brain Commun 2023; 5:fcad095. [PMID: 37038499 PMCID: PMC10082555 DOI: 10.1093/braincomms/fcad095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/25/2023] [Accepted: 03/29/2023] [Indexed: 04/12/2023] Open
Abstract
The relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature and severity of emotional-behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups. A total of 114 patients and 83 controls from the Epilepsy Connectome Project were administered the Achenbach System of Empirically Based Assessment inventory from which six Diagnostic and Statistical Manual of Mental Disorders-oriented scales were analysed by unsupervised machine learning analytics to identify latent patient groups. Identified clusters were contrasted to controls as well as to each other in order to characterize their association with sociodemographic, clinical epilepsy and morphological and functional imaging network features. The concurrent validity of the behavioural phenotypes was examined through other measures of behaviour and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared with controls. However, cluster analysis identified three latent groups: (i) unaffected, with no scale elevations compared with controls (Cluster 1, 37%); (ii) mild symptomatology characterized by significant elevations across several Diagnostic and Statistical Manual of Mental Disorders-oriented scales compared with controls (Cluster 2, 42%); and (iii) severe symptomatology with significant elevations across all scales compared with controls and the other temporal lobe epilepsy behaviour phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioural phenotype grouping was demonstrated through identical stepwise links to abnormalities on independent measures including the National Institutes of Health Toolbox Emotion Battery and quality of life metrics. There were significant associations between cluster membership and sociodemographic (handedness and education), cognition (processing speed), clinical epilepsy (presence and lifetime number of tonic-clonic seizures) and neuroimaging characteristics (cortical volume and thickness and global graph theory metrics of morphology and resting-state functional MRI). Increasingly dispersed volumetric abnormalities and widespread disruptions in underlying network properties were associated with the most abnormal behavioural phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbour accompanying sociodemographic, clinical and neuroimaging correlates. The underlying neurobiological patterns suggest that the degree of psychopathology is linked to increasingly dispersed abnormal brain networks. Similar to cognition, machine learning approaches support a novel developing taxonomy of the comorbidities of epilepsy.
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Affiliation(s)
- Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
- Department of Neurology, William S. Middleton Veterans Administration Hospital, Madison, WI 53705, USA
| | - Camille Garcia-Ramos
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Vivek Prabhakaran
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Melanie Boly
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53726, USA
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13
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Chen Y, Li S, Ge W, Jing J, Chen HY, Doherty D, Herman A, Kaleem S, Ding K, Osman G, Swisher CB, Smith C, Maciel CB, Alkhachroum A, Lee JW, Dhakar MB, Gilmore EJ, Sivaraju A, Hirsch LJ, Omay SB, Blumenfeld H, Sheth KN, Struck AF, Edlow BL, Westover MB, Kim JA. Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy. J Neurol Neurosurg Psychiatry 2023; 94:245-249. [PMID: 36241423 PMCID: PMC9931627 DOI: 10.1136/jnnp-2022-329542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/26/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1). METHODS We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE1 patients were matched with 63 non-PTE1 patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1 using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities. RESULTS In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1 risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)). CONCLUSIONS Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1 prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.
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Affiliation(s)
- Yilun Chen
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Songlu Li
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Wendong Ge
- Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jin Jing
- Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hsin Yi Chen
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Daniel Doherty
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Alison Herman
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Safa Kaleem
- Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Kan Ding
- Neurology, UT Southwestern Medical Center, Dallas, Texas, USA
| | | | - Christa B Swisher
- Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Christine Smith
- Neurology, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Carolina B Maciel
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Neurology, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Ayham Alkhachroum
- Neurology, University of Miami Miller School of Medicine, Miami, Florida, USA
- Neurology, Jackson Memorial Hospital, Miami, Florida, USA
| | - Jong Woo Lee
- Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Monica B Dhakar
- Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Emily J Gilmore
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | | | | | - Sacit B Omay
- Neurosurgery, Yale School of Medicine, New Haven, Connecticut, USA
| | - Hal Blumenfeld
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kevin N Sheth
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Aaron F Struck
- Neurology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Neurology, William S Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - Brian L Edlow
- Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Jennifer A Kim
- Neurology, Yale School of Medicine, New Haven, Connecticut, USA
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14
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Juan E, Górska U, Kozma C, Papantonatos C, Bugnon T, Denis C, Kremen V, Worrell G, Struck AF, Bateman LM, Merricks EM, Blumenfeld H, Tononi G, Schevon C, Boly M. Distinct signatures of loss of consciousness in focal impaired awareness versus tonic-clonic seizures. Brain 2023; 146:109-123. [PMID: 36383415 PMCID: PMC10582624 DOI: 10.1093/brain/awac291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 05/17/2022] [Accepted: 06/11/2022] [Indexed: 11/17/2022] Open
Abstract
Loss of consciousness is a hallmark of many epileptic seizures and carries risks of serious injury and sudden death. While cortical sleep-like activities accompany loss of consciousness during focal impaired awareness seizures, the mechanisms of loss of consciousness during focal to bilateral tonic-clonic seizures remain unclear. Quantifying differences in markers of cortical activation and ictal recruitment between focal impaired awareness and focal to bilateral tonic-clonic seizures may also help us to understand their different consequences for clinical outcomes and to optimize neuromodulation therapies. We quantified clinical signs of loss of consciousness and intracranial EEG activity during 129 focal impaired awareness and 50 focal to bilateral tonic-clonic from 41 patients. We characterized intracranial EEG changes both in the seizure onset zone and in areas remote from the seizure onset zone with a total of 3386 electrodes distributed across brain areas. First, we compared the dynamics of intracranial EEG sleep-like activities: slow-wave activity (1-4 Hz) and beta/delta ratio (a validated marker of cortical activation) during focal impaired awareness versus focal to bilateral tonic-clonic. Second, we quantified differences between focal to bilateral tonic-clonic and focal impaired awareness for a marker validated to detect ictal cross-frequency coupling: phase-locked high gamma (high-gamma phased-locked to low frequencies) and a marker of ictal recruitment: the epileptogenicity index. Third, we assessed changes in intracranial EEG activity preceding and accompanying behavioural generalization onset and their correlation with electromyogram channels. In addition, we analysed human cortical multi-unit activity recorded with Utah arrays during three focal to bilateral tonic-clonic seizures. Compared to focal impaired awareness, focal to bilateral tonic-clonic seizures were characterized by deeper loss of consciousness, even before generalization occurred. Unlike during focal impaired awareness, early loss of consciousness before generalization was accompanied by paradoxical decreases in slow-wave activity and by increases in high-gamma activity in parieto-occipital and temporal cortex. After generalization, when all patients displayed loss of consciousness, stronger increases in slow-wave activity were observed in parieto-occipital cortex, while more widespread increases in cortical activation (beta/delta ratio), ictal cross-frequency coupling (phase-locked high gamma) and ictal recruitment (epileptogenicity index). Behavioural generalization coincided with a whole-brain increase in high-gamma activity, which was especially synchronous in deep sources and could not be explained by EMG. Similarly, multi-unit activity analysis of focal to bilateral tonic-clonic revealed sustained increases in cortical firing rates during and after generalization onset in areas remote from the seizure onset zone. Overall, these results indicate that unlike during focal impaired awareness, the neural signatures of loss of consciousness during focal to bilateral tonic-clonic consist of paradoxical increases in cortical activation and neuronal firing found most consistently in posterior brain regions. These findings suggest differences in the mechanisms of ictal loss of consciousness between focal impaired awareness and focal to bilateral tonic-clonic and may account for the more negative prognostic consequences of focal to bilateral tonic-clonic.
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Affiliation(s)
- Elsa Juan
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
- Department of Psychology, University of Amsterdam, Amsterdam, 1018 WS, The Netherlands
| | - Urszula Górska
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
- Smoluchowski Institute of Physics, Jagiellonian University, 30-348 Krakow, Poland
| | - Csaba Kozma
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Cynthia Papantonatos
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Tom Bugnon
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
| | - Colin Denis
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, 16000, Czech Republic
| | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Neurology, William S. Middleton Veterans Administration Hospital, Madison, WI 53705, USA
| | - Lisa M Bateman
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Edward M Merricks
- Department of Neurology, Columbia University, New York City, NY 10032, USA
| | - Hal Blumenfeld
- Department of Neurology, Yale School of Medicine, New Haven, CT 06519, USA
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
| | - Catherine Schevon
- Department of Neurology, Columbia University, New York City, NY 10032, USA
| | - Melanie Boly
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
- Department of Neurology, University of Wisconsin-Madison, Madison, WI 53705, USA
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15
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Holla SK, Krishnamurthy PV, Subramaniam T, Dhakar MB, Struck AF. Electrographic Seizures in the Critically Ill. Neurol Clin 2022; 40:907-925. [PMID: 36270698 PMCID: PMC10508310 DOI: 10.1016/j.ncl.2022.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Identifying and treating critically ill patients with seizures can be challenging. In this article, the authors review the available data on patient populations at risk, seizure prognostication with tools such as 2HELPS2B, electrographic seizures and the various ictal-interictal continuum patterns with their latest definitions and associated risks, ancillary testing such as imaging studies, serum biomarkers, and invasive multimodal monitoring. They also illustrate 5 different patient scenarios, their treatment and outcomes, and propose recommendations for targeted treatment of electrographic seizures in critically ill patients.
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Affiliation(s)
- Smitha K Holla
- Department of Neurology, UW Medical Foundation Centennial building, 1685 Highland Avenue, Madison, WI 53705, USA.
| | | | - Thanujaa Subramaniam
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale School of Medicine, 15 York Street, Building LLCI, 10th Floor, Suite 1003 New Haven, CT 06520, USA
| | - Monica B Dhakar
- Department of Neurology, The Warren Alpert Medical School of Brown University, 593 Eddy St, APC 5, Providence, RI 02903, USA
| | - Aaron F Struck
- Department of Neurology, UW Medical Foundation Centennial building, 1685 Highland Avenue, Madison, WI 53705, USA; William S Middleton Veterans Hospital, Madison WI, USA
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16
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Kong THJ, Abdul Azeem M, Naeem A, Allen S, Kim JA, Struck AF. Epileptiform activity predicts epileptogenesis in cerebral hemorrhage. Ann Clin Transl Neurol 2022; 9:1475-1480. [PMID: 36030385 PMCID: PMC9463945 DOI: 10.1002/acn3.51637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/09/2022] [Accepted: 07/14/2022] [Indexed: 12/15/2022] Open
Abstract
This retrospective case-controlled study was performed to evaluate whether Epileptiform Activity, suspected clinical seizures, and/or 2HELPS2B/S after nontraumatic Intraparenchymal Hemorrhage or Subarachnoid Hemorrhage can predict Epilepsy. Hundred and thirty-two patients were included-29 (Epilepsy), 103 (Control Group). After matching, the average effect for all three risk factors was significant as follows: (1) Epileptiform Activity (p = 0.012, odds ratio 3.14), (2) suspected seizures (p = 0.021, odds ratio 3.78), and (3) 2HELPS2B/S score (p < 0.001, odds ratio 4.94). This study shows that Epileptiform Activity, suspected seizures, and particularly, the 2HELPS2B/S score in the acute phase are risk factors for the development of epilepsy after nontraumatic intraparenchymal and subarachnoid hemorrhage.
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Affiliation(s)
| | | | - Ayesha Naeem
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Shawn Allen
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | | | - Aaron F. Struck
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- William S. Middleton Veterans Administration HospitalMadisonWisconsinUSA
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17
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Garcia-Ramos C, Nair V, Maganti R, Mathis J, Conant LL, Prabhakaran V, Binder JR, Meyerand B, Hermann B, Struck AF. Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics. Sci Rep 2022; 12:14407. [PMID: 36002603 PMCID: PMC9402557 DOI: 10.1038/s41598-022-18495-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 08/12/2022] [Indexed: 02/08/2023] Open
Abstract
Machine learning analyses were performed on graph theory (GT) metrics extracted from brain functional and morphological data from temporal lobe epilepsy (TLE) patients in order to identify intrinsic network phenotypes and characterize their clinical significance. Participants were 97 TLE and 36 healthy controls from the Epilepsy Connectome Project. Each imaging modality (i.e., Resting-state functional Magnetic Resonance Imaging (RS-fMRI), and structural MRI) rendered 2 clusters: one comparable to controls and one deviating from controls. Participants were minimally overlapping across the identified clusters, suggesting that an abnormal functional GT phenotype did not necessarily mean an abnormal morphological GT phenotype for the same subject. Morphological clusters were associated with a significant difference in the estimated lifetime number of generalized tonic-clonic seizures and functional cluster membership was associated with age. Furthermore, controls exhibited significant correlations between functional GT metrics and cognition, while for TLE participants morphological GT metrics were linked to cognition, suggesting a dissociation between higher cognitive abilities and GT-derived network measures. Overall, these findings demonstrate the existence of clinically meaningful minimally overlapping phenotypes of morphological and functional GT networks. Functional network properties may underlie variance in cognition in healthy brains, but in the pathological state of epilepsy the cognitive limits might be primarily related to structural cerebral network properties.
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Affiliation(s)
- Camille Garcia-Ramos
- grid.14003.360000 0001 2167 3675Department of Medical Physics, University of Wisconsin-Madison, Madison, USA ,grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Veena Nair
- grid.14003.360000 0001 2167 3675Department of Radiology, University of Wisconsin-Madison, Madison, USA
| | - Rama Maganti
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Jedidiah Mathis
- grid.30760.320000 0001 2111 8460Department of Neurology, Medical College of Wisconsin, Milwaukee, USA
| | - Lisa L. Conant
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Vivek Prabhakaran
- grid.14003.360000 0001 2167 3675Department of Radiology, University of Wisconsin-Madison, Madison, USA
| | - Jeffrey R. Binder
- grid.30760.320000 0001 2111 8460Department of Neurology, Medical College of Wisconsin, Milwaukee, USA
| | - Beth Meyerand
- grid.14003.360000 0001 2167 3675Department of Medical Physics, University of Wisconsin-Madison, Madison, USA
| | - Bruce Hermann
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Aaron F. Struck
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA ,grid.417123.20000 0004 0420 6882William S Middleton VA Hospital, Madison, WI USA
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18
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Torres-Lopez VM, Rovenolt GE, Olcese AJ, Garcia GE, Chacko SM, Robinson A, Gaiser E, Acosta J, Herman AL, Kuohn LR, Leary M, Soto AL, Zhang Q, Fatima S, Falcone GJ, Payabvash MS, Sharma R, Struck AF, Sheth KN, Westover MB, Kim JA. Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports. JAMA Netw Open 2022; 5:e2227109. [PMID: 35972739 PMCID: PMC9382443 DOI: 10.1001/jamanetworkopen.2022.27109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/20/2022] [Indexed: 12/17/2022] Open
Abstract
Importance Clinical text reports from head computed tomography (CT) represent rich, incompletely utilized information regarding acute brain injuries and neurologic outcomes. CT reports are unstructured; thus, extracting information at scale requires automated natural language processing (NLP). However, designing new NLP algorithms for each individual injury category is an unwieldy proposition. An NLP tool that summarizes all injuries in head CT reports would facilitate exploration of large data sets for clinical significance of neuroradiological findings. Objective To automatically extract acute brain pathological data and their features from head CT reports. Design, Setting, and Participants This diagnostic study developed a 2-part named entity recognition (NER) NLP model to extract and summarize data on acute brain injuries from head CT reports. The model, termed BrainNERD, extracts and summarizes detailed brain injury information for research applications. Model development included building and comparing 2 NER models using a custom dictionary of terms, including lesion type, location, size, and age, then designing a rule-based decoder using NER outputs to evaluate for the presence or absence of injury subtypes. BrainNERD was evaluated against independent test data sets of manually classified reports, including 2 external validation sets. The model was trained on head CT reports from 1152 patients generated by neuroradiologists at the Yale Acute Brain Injury Biorepository. External validation was conducted using reports from 2 outside institutions. Analyses were conducted from May 2020 to December 2021. Main Outcomes and Measures Performance of the BrainNERD model was evaluated using precision, recall, and F1 scores based on manually labeled independent test data sets. Results A total of 1152 patients (mean [SD] age, 67.6 [16.1] years; 586 [52%] men), were included in the training set. NER training using transformer architecture and bidirectional encoder representations from transformers was significantly faster than spaCy. For all metrics, the 10-fold cross-validation performance was 93% to 99%. The final test performance metrics for the NER test data set were 98.82% (95% CI, 98.37%-98.93%) for precision, 98.81% (95% CI, 98.46%-99.06%) for recall, and 98.81% (95% CI, 98.40%-98.94%) for the F score. The expert review comparison metrics were 99.06% (95% CI, 97.89%-99.13%) for precision, 98.10% (95% CI, 97.93%-98.77%) for recall, and 98.57% (95% CI, 97.78%-99.10%) for the F score. The decoder test set metrics were 96.06% (95% CI, 95.01%-97.16%) for precision, 96.42% (95% CI, 94.50%-97.87%) for recall, and 96.18% (95% CI, 95.151%-97.16%) for the F score. Performance in external institution report validation including 1053 head CR reports was greater than 96%. Conclusions and Relevance These findings suggest that the BrainNERD model accurately extracted acute brain injury terms and their properties from head CT text reports. This freely available new tool could advance clinical research by integrating information in easily gathered head CT reports to expand knowledge of acute brain injury radiographic phenotypes.
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Affiliation(s)
| | | | - Angelo J. Olcese
- Department of Neurology, Yale University, New Haven, Connecticut
| | | | - Sarah M. Chacko
- Department of Neurology, Yale University, New Haven, Connecticut
| | - Amber Robinson
- Department of Neurology, Yale University, New Haven, Connecticut
| | - Edward Gaiser
- Department of Neurology, Yale University, New Haven, Connecticut
| | - Julian Acosta
- Department of Neurology, Yale University, New Haven, Connecticut
| | - Alison L. Herman
- Department of Neurology, Yale University, New Haven, Connecticut
| | - Lindsey R. Kuohn
- Department of Neurology, Yale University, New Haven, Connecticut
| | - Megan Leary
- Department of Neurology, Yale University, New Haven, Connecticut
| | | | - Qiang Zhang
- Department of Neurology, Yale University, New Haven, Connecticut
| | - Safoora Fatima
- Department of Neurology, University of Wisconsin, Madison
| | - Guido J. Falcone
- Department of Neurology, Yale University, New Haven, Connecticut
| | | | - Richa Sharma
- Department of Neurology, Yale University, New Haven, Connecticut
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin, Madison
- William S Middleton Veterans Hospital, Madison, Wisconsin
| | - Kevin N. Sheth
- Department of Neurology, Yale University, New Haven, Connecticut
| | | | - Jennifer A. Kim
- Department of Neurology, Yale University, New Haven, Connecticut
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19
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Bouyaknouden D, Peddada TN, Ravishankar N, Fatima S, Fong-Isariyawongse J, Gilmore EJ, Lee JW, Struck AF, Gaspard N. Neurological Prognostication After Hypoglycemic Coma: Role of Clinical and EEG Findings. Neurocrit Care 2022; 37:273-280. [PMID: 35437670 DOI: 10.1007/s12028-022-01495-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 03/17/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Hypoglycemic coma (HC) is an uncommon but severe clinical condition associated with poor neurological outcome. There is a dearth of robust neurological prognostic factors after HC. On the other hand, there is an increasing body of literature on reliable prognostic markers in the postanoxic coma, a similar-albeit not identical-situation. The objective of this study was thus to investigate the use and predictive value of these markers in HC. METHODS We conducted a retrospective, multicenter, cohort study within five centers of the Critical Care EEG Monitoring Research Consortium. We queried our electroencephalography (EEG) databases to identify all patients undergoing continuous EEG monitoring after admission to an intensive care unit with HC (defined as Glasgow Coma Scale < 8 on admission and a first blood glucose level < 50 mg/dL or not documented but in an obvious clinical context) between 01/01/2010 and 12/31/2020. We studied the association of findings at neurological examination (Glasgow Coma Scale motor subscale, pupillary light and corneal reflexes) and at continuous EEG monitoring(highly malignant patterns, reactivity, periodic discharges, seizures) with best neurological outcome within 3 months after hospital discharge, defined by the Cerebral Performance Category as favorable (1-3: recovery of consciousness) versus unfavorable (4-5: lack of recovery of consciousness). RESULTS We identified 60 patients (30 [50%] women; age 62 [51-72] years). Thirty-one and 29 patients had a favorable and unfavorable outcome, respectively. The presence of pupillary reflexes (24 [100%] vs. 17 [81%]; p value 0.04) and a motor subscore > 2 (22 [92%] vs. 12 [63%]; p value 0.03) at 48-72 h were associated with a favorable outcome. A highly malignant EEG pattern was observed in 7 of 29 (24%) patients with unfavorable outcome versus 0 of 31 (0%) with favorable outcome, whereas the presence of EEG reactivity was observed in 28 of 31 (90%) patients with favorable outcome versus 13 of 29 (45%) with unfavorable outcome (p < 0.001 for comparison of all background categories). CONCLUSIONS This preliminary study suggests that highly malignant EEG patterns might be reliable prognostic markers of unfavorable outcome after HC. Other EEG findings, including lack of EEG reactivity and seizures and clinical findings appear less accurate. These findings should be replicated in a larger multicenter prospective study.
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Affiliation(s)
- Douaae Bouyaknouden
- Department of Neurology, Hôpital Erasme - Cliniques Universitaires de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Teja N Peddada
- Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Safoora Fatima
- Department of Neurology, University of Wisconsin, Madison, WI, USA
| | | | - Emily J Gilmore
- Department of Neurology, Yale University, New Haven, CT, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin, Madison, WI, USA.,William S. Middleton Veterans Hospital, Madison, WI, USA
| | - Nicolas Gaspard
- Department of Neurology, Hôpital Erasme - Cliniques Universitaires de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium. .,Department of Neurology, Yale University, New Haven, CT, USA.
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20
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Fatima S, Sun M, Gjini K, Struck AF. Association Between Lateralized Periodic Discharge Amplitude and Seizure on Continuous EEG Monitoring in Patients With Structural Brain Abnormality in Critical Illness. Front Neurol 2022; 13:840247. [PMID: 35370885 PMCID: PMC8966838 DOI: 10.3389/fneur.2022.840247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/07/2022] [Indexed: 12/15/2022] Open
Abstract
Objective To investigate the association between lateralized periodic discharge (LPD) amplitude and seizure risk on an individual level in patients with structural brain abnormality. Methods Retrospective case-control study of patients with structural brain abnormality undergoing continuous EEG monitoring was performed. We included 10 patients with LPDs and seizures as cases and 10 controls, patients with LPDs without seizure. Analysis was performed with a mixed-effects model with primary outcome measure of number of seizures per 8-h EEG epoch with fixed effects being variables of interest and random effect being subject ID. Results Epochs with seizures showed a higher absolute amplitude (corrected p = 0.04) and a higher relative amplitude (corrected p = 0.04) of LPDs. Additionally, the number of seizures was higher in epochs that had LPDs with plus features (uncorrected p = 0.002) and LPDs with higher relative amplitude (uncorrected p = 0.005). Conclusion Higher LPD amplitude is associated with increased risk of seizures on an individual patient level. A decreasing amplitude is suggestive of decreasing seizure risk, and may in fact be suggestive of decreasing ictal character of LPDs.
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Affiliation(s)
- Safoora Fatima
- Department of Neurology, Wisconsin Hospital and Clinics, University of Wisconsin, Madison, WI, United States,*Correspondence: Safoora Fatima
| | - Mengzhen Sun
- Department of Neurology, Wisconsin Hospital and Clinics, University of Wisconsin, Madison, WI, United States
| | - Klevest Gjini
- Department of Neurology, Wisconsin Hospital and Clinics, University of Wisconsin, Madison, WI, United States
| | - Aaron F. Struck
- Department of Neurology, Wisconsin Hospital and Clinics, University of Wisconsin, Madison, WI, United States,William S. Middleton Memorial Veterans Hospital, University of Wisconsin, Madison, WI, United States
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21
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Hermann BP, Struck AF, Busch RM, Reyes A, Kaestner E, McDonald CR. Neurobehavioural comorbidities of epilepsy: towards a network-based precision taxonomy. Nat Rev Neurol 2021; 17:731-746. [PMID: 34552218 PMCID: PMC8900353 DOI: 10.1038/s41582-021-00555-z] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2021] [Indexed: 02/06/2023]
Abstract
Cognitive and behavioural comorbidities are prevalent in childhood and adult epilepsies and impose a substantial human and economic burden. Over the past century, the classic approach to understanding the aetiology and course of these comorbidities has been through the prism of the medical taxonomy of epilepsy, including its causes, course, characteristics and syndromes. Although this 'lesion model' has long served as the organizing paradigm for the field, substantial challenges to this model have accumulated from diverse sources, including neuroimaging, neuropathology, neuropsychology and network science. Advances in patient stratification and phenotyping point towards a new taxonomy for the cognitive and behavioural comorbidities of epilepsy, which reflects the heterogeneity of their clinical presentation and raises the possibility of a precision medicine approach. As we discuss in this Review, these advances are informing the development of a revised aetiological paradigm that incorporates sophisticated neurobiological measures, genomics, comorbid disease, diversity and adversity, and resilience factors. We describe modifiable risk factors that could guide early identification, treatment and, ultimately, prevention of cognitive and broader neurobehavioural comorbidities in epilepsy and propose a road map to guide future research.
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Affiliation(s)
- Bruce P. Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.,
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.,William S. Middleton Veterans Administration Hospital, Madison, WI, USA
| | - Robyn M. Busch
- Epilepsy Center and Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.,Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Anny Reyes
- Department of Psychiatry and Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
| | - Erik Kaestner
- Department of Psychiatry and Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
| | - Carrie R. McDonald
- Department of Psychiatry and Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
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22
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Abstract
Secondary brain injury (SBI) is defined as new or worsening injury to the brain after an initial neurologic insult, such as hemorrhage, trauma, ischemic stroke, or infection. It is a common and potentially preventable complication following many types of primary brain injury (PBI). However, mechanistic details about how PBI leads to additional brain injury and evolves into SBI are poorly characterized. In this work, we propose a mechanistic model for the metabolic supply demand mismatch hypothesis (MSDMH) of SBI. Our model, based on the Hodgkin-Huxley model, supplemented with additional dynamics for extracellular potassium, oxygen concentration, and excitotoxity, provides a high-level unified explanation for why patients with acute brain injury frequently develop SBI. We investigate how decreased oxygen, increased extracellular potassium, excitotoxicity, and seizures can induce SBI and suggest three underlying paths for how events following PBI may lead to SBI. The proposed model also helps explain several important empirical observations, including the common association of acute brain injury with seizures, the association of seizures with tissue hypoxia and so on. In contrast to current practices which assume that ischemia plays the predominant role in SBI, our model suggests that metabolic crisis involved in SBI can also be nonischemic. Our findings offer a more comprehensive understanding of the complex interrelationship among potassium, oxygen, excitotoxicity, seizures, and SBI.NEW & NOTEWORTHY We present a novel mechanistic model for the metabolic supply demand mismatch hypothesis (MSDMH), which attempts to explain why patients with acute brain injury frequently develop seizure activity and secondary brain injury (SBI). Specifically, we investigate how decreased oxygen, increased extracellular potassium, excitotoxicity, seizures, all common sequalae of primary brain injury (PBI), can induce SBI and suggest three underlying paths for how events following PBI may lead to SBI.
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Affiliation(s)
- Jiang-Ling Song
- The Medical Big Data Research Center, Northwest University, Xi'an, China.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jennifer A Kim
- Department of Neurology, Yale New Haven Hospital, New Haven, Connecticut
| | - Aaron F Struck
- Departments of Neurology, University of Wisconsin-Madison, Madison, Wisconsin.,William S Middleton Veterans Administration Hospital, Madison, Wisconsin
| | - Rui Zhang
- The Medical Big Data Research Center, Northwest University, Xi'an, China
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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23
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Hermann BP, Struck AF, Dabbs K, Seidenberg M, Jones JE. Behavioral phenotypes of temporal lobe epilepsy. Epilepsia Open 2021; 6:369-380. [PMID: 34033251 PMCID: PMC8166791 DOI: 10.1002/epi4.12488] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/21/2021] [Accepted: 03/28/2021] [Indexed: 12/17/2022] Open
Abstract
Objective To identity phenotypes of self‐reported symptoms of psychopathology and their correlates in patients with temporal lobe epilepsy (TLE). Method 96 patients with TLE and 82 controls were administered the Symptom Checklist 90‐Revised (SCL‐90‐R) to characterize emotional‐behavioral status. The nine symptom scales of the SCL‐90‐R were analyzed by unsupervised machine learning techniques to identify latent TLE groups. Identified clusters were contrasted to controls to characterize their association with sociodemographic, clinical epilepsy, neuropsychological, psychiatric, and neuroimaging factors. Results TLE patients as a group exhibited significantly higher (abnormal) scores across all SCL‐90‐R scales compared to controls. However, cluster analysis identified three latent groups: (1) unimpaired with no scale elevations compared to controls (Cluster 1, 42% of TLE patients), (2) mild‐to‐moderate symptomatology characterized by significant elevations across several SCL‐90‐R scales compared to controls (Cluster 2, 35% of TLE patients), and (3) marked symptomatology with significant elevations across all scales compared to controls and the other TLE phenotype groups (Cluster 3, 23% of TLE patients). There were significant associations between cluster membership and demographic (education), clinical epilepsy (perceived seizure severity, bitemporal lobe seizure onset), and neuropsychological status (intelligence, memory, executive function), but with minimal structural neuroimaging correlates. Concurrent validity of the behavioral phenotype grouping was demonstrated through association with psychiatric (current and lifetime‐to‐date DSM IV Axis 1 disorders and current treatment) and quality‐of‐life variables. Significance Symptoms of psychopathology in patients with TLE are characterized by a series of discrete phenotypes with accompanying sociodemographic, cognitive, and clinical correlates. Similar to cognition in TLE, machine learning approaches suggest a developing taxonomy of the comorbidities of epilepsy.
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Affiliation(s)
- Bruce P Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.,Department of Neurology, William S Middleton Veterans Administration Hospital, Madison, WI, USA
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Mike Seidenberg
- Department of Psychology, Rosalind Franklin University of Science and Medicine, North Chicago, IL, USA
| | - Jana E Jones
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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24
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Garcia-Ramos C, Struck AF, Cook C, Prabhakaran V, Nair V, Maganti R, Binder JR, Meyerand M, Conant LL, Hermann B. Network topology of the cognitive phenotypes of temporal lobe epilepsy. Cortex 2021; 141:55-65. [PMID: 34029858 DOI: 10.1016/j.cortex.2021.03.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 02/04/2021] [Accepted: 03/28/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE The neuropsychological complications of temporal lobe epilepsy are characterized by a spectrum of reproducible cognitive phenotypes that vary in the presence, type and degree of impairment. The nature of the disruptions to the neuropsychological networks that underlie these phenotypes remain to be characterized and represent the subject of this investigation. METHODS Participants included 30 healthy controls and 104 patients with temporal lobe epilepsy who fell into three cognitive phenotypes (intact, focal impairment, generalized impairment). Eighteen neuropsychological measures representing multiple cognitive domains (language, memory, executive function, visuoperception, motor speed) were examined by graph theory techniques within the control and each epilepsy cognitive phenotype group to characterize their global and local network properties. RESULTS Across the control and epilepsy cognitive phenotype groups (intact to focal to generalized impairment), there was: 1) an orderly breakdown in the positive manifold reflected by a stepwise reduction of positive associations among the neuropsychological tests, 2) stepwise abnormal increases in global measures including the normalized clustering coefficient and modularity index, 3) stepwise abnormal decreases in normalized global efficiency, 4) a community structure demonstrating well organized modules within the control group while each epilepsy group showed deviations from controls, and 5) lower strength, compared to controls, across 8 nodes in the focal and generalized impairment groups compared to only 3 nodes in the no-impairment epilepsy group, pointing to the superior integration of local connections in controls. DISCUSSION The cognitive phenotypes of temporal lobe epilepsy are characterized by orderly abnormalities in their underlying neuropsychological networks. These findings inform the network perturbations that underlie the taxonomy of cognitive abnormality in temporal lobe epilepsy and provide a model for examination of similar issues in other focal and generalized epilepsies.
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Affiliation(s)
- Camille Garcia-Ramos
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Middleton Veterans Administration Hospital, Madison, WI, USA
| | - Cole Cook
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Veena Nair
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Rama Maganti
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Marybeth Meyerand
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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25
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Struck AF, Boly M, Hwang G, Nair V, Mathis J, Nencka A, Conant LL, DeYoe EA, Ragahavan M, Prabhakaran V, Binder JR, Meyerand ME, Hermann BP. Regional and global resting-state functional MR connectivity in temporal lobe epilepsy: Results from the Epilepsy Connectome Project. Epilepsy Behav 2021; 117:107841. [PMID: 33611101 PMCID: PMC8035304 DOI: 10.1016/j.yebeh.2021.107841] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/20/2021] [Accepted: 02/01/2021] [Indexed: 12/28/2022]
Abstract
Temporal lobe epilepsy (TLE) has been conceptualized as focal disease with a discrete neurobiological focus and can respond well to targeted resection or ablation. In contrast, the neuro-cognitive deficits resulting from TLE can be widespread involving regions beyond the primary epileptic network. We hypothesize that this seemingly paradoxical findings can be explained by differences in connectivity between the primary epileptic region which is hyper-connected and its secondary influence on global connectome organization. This hypothesis is tested using regional and global graph theory metrics where we anticipate that regional mesial-temporal hyperconnectivity will be found and correlate with seizure frequency while global networks will be disorganized and be more closely associated with neuro-cognitive deficits. Resting-state fMRI was used to examine temporal lobe regional connectivity and global functional connectivity from 102 patients with TLE and 55 controls. Connectivity matrices were calculated for subcortical volumes and cortical parcellations. Graph theory metrics (global clustering coefficient (GCC), degree, closeness) were compared between groups and in relation to neuropsychological profiles and disease covariates using permutation testing and causal analysis. In TLE there was a decrease in GCC (p = 0.0345) associated with a worse neuropsychological profile (p = 0.0134). There was increased connectivity in the left hippocampus/amygdala (degree p = 0.0103, closeness p = 0.0104) and a decrease in connectivity in the right lateral temporal lobe (degree p = 0.0186, closeness p = 0.0122). A ratio between the hippocampus/amygdala and lateral temporal lobe-temporal lobe connectivity ratio (TLCR) revealed differences between TLE and controls for closeness (left p = 0.00149, right p = 0.0494) and for degree on left p = 0.00169; with trend on right p = 0.0567. Causal analysis suggested that "Epilepsy Activity" (seizure frequency, anti-seizure medications) was associated with increase in TLCR but not in GCC, while cognitive decline was associated with decreased GCC. These findings support the hypothesis that in TLE there is hyperconnectivity in the hippocampus/amygdala and hypoconnectivity in the lateral temporal lobe associated with "Epilepsy Activity." While, global connectome disorganization was associated with worse neuropsychological phenotype.
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Affiliation(s)
- Aaron F Struck
- University of Wisconsin-Madison, Department of Neurology, United States; William S. Middleton Veterans Administration Hospital, Madison, WI, United States.
| | - Melanie Boly
- University of Wisconsin-Madison, Department of Neurology
| | - Gyujoon Hwang
- University of Wisconsin-Madison, Department of Medical Physics
| | - Veena Nair
- University of Wisconsin-Madison, Department of Radiology
| | | | - Andrew Nencka
- Medical College of Wisconsin, Department of Radiology
| | - Lisa L Conant
- Medical College of Wisconsin, Department of Neurology
| | - Edgar A DeYoe
- Medical College of Wisconsin, Department of Radiology
| | | | | | | | - Mary E Meyerand
- University of Wisconsin-Madison, Department of Medical Physics
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26
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Ge W, Jing J, An S, Herlopian A, Ng M, Struck AF, Appavu B, Johnson EL, Osman G, Haider HA, Karakis I, Kim JA, Halford JJ, Dhakar MB, Sarkis RA, Swisher CB, Schmitt S, Lee JW, Tabaeizadeh M, Rodriguez A, Gaspard N, Gilmore E, Herman ST, Kaplan PW, Pathmanathan J, Hong S, Rosenthal ES, Zafar S, Sun J, Brandon Westover M. Deep active learning for Interictal Ictal Injury Continuum EEG patterns. J Neurosci Methods 2021; 351:108966. [PMID: 33131680 PMCID: PMC8135050 DOI: 10.1016/j.jneumeth.2020.108966] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/16/2020] [Accepted: 10/01/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVES Seizures and seizure-like electroencephalography (EEG) patterns, collectively referred to as "ictal interictal injury continuum" (IIIC) patterns, are commonly encountered in critically ill patients. Automated detection is important for patient care and to enable research. However, training accurate detectors requires a large labeled dataset. Active Learning (AL) may help select informative examples to label, but the optimal AL approach remains unclear. METHODS We assembled >200,000 h of EEG from 1,454 hospitalized patients. From these, we collected 9,808 labeled and 120,000 unlabeled 10-second EEG segments. Labels included 6 IIIC patterns. In each AL iteration, a Dense-Net Convolutional Neural Network (CNN) learned vector representations for EEG segments using available labels, which were used to create a 2D embedding map. Nearest-neighbor label spreading within the embedding map was used to create additional pseudo-labeled data. A second Dense-Net was trained using real- and pseudo-labels. We evaluated several strategies for selecting candidate points for experts to label next. Finally, we compared two methods for class balancing within queries: standard balanced-based querying (SBBQ), and high confidence spread-based balanced querying (HCSBBQ). RESULTS Our results show: 1) Label spreading increased convergence speed for AL. 2) All query criteria produced similar results to random sampling. 3) HCSBBQ query balancing performed best. Using label spreading and HCSBBQ query balancing, we were able to train models approaching expert-level performance across all pattern categories after obtaining ∼7000 expert labels. CONCLUSION Our results provide guidance regarding the use of AL to efficiently label large EEG datasets in critically ill patients.
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Affiliation(s)
- Wendong Ge
- Massachusetts General Hospital, Department of Neurology, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Jin Jing
- Massachusetts General Hospital, Department of Neurology, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Sungtae An
- Georgia Institute of Technology, College of Computing, Atlanta, GA, Georgia
| | | | | | - Aaron F Struck
- University of Wisconsin Madison Department of Neurology, United States
| | - Brian Appavu
- University of Arizona College of Medicine, Phoenix, United States
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Nicolas Gaspard
- Université Libre de Bruxelles, Hôpital Erasme and Yale University, Belgium
| | - Emily Gilmore
- Yale University, Yale New Haven Hospital, United States
| | - Susan T Herman
- Barrow Neurological Institute, Phoenix, AZ, United States
| | | | | | - Shenda Hong
- Georgia Institute of Technology, College of Computing, Atlanta, GA, Georgia
| | - Eric S Rosenthal
- Massachusetts General Hospital, Department of Neurology, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Sahar Zafar
- Massachusetts General Hospital, Department of Neurology, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Jimeng Sun
- University of Illinois at Urbana-Champaign, College of Computing, Champaign, IL, United States
| | - M Brandon Westover
- Massachusetts General Hospital, Department of Neurology, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
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Moffet EW, Verhagen R, Jones B, Findlay G, Juan E, Bugnon T, Mensen A, Aparicio MK, Maganti R, Struck AF, Tononi G, Boly M. Local Sleep Slow-Wave Activity Colocalizes With the Ictal Symptomatogenic Zone in a Patient With Reflex Epilepsy: A High-Density EEG Study. Front Syst Neurosci 2020; 14:549309. [PMID: 33192347 PMCID: PMC7609881 DOI: 10.3389/fnsys.2020.549309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 09/17/2020] [Indexed: 11/21/2022] Open
Abstract
Background: Slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep reflects synaptic potentiation during preceding wakefulness. Epileptic activity may induce increases in state-dependent SWA in human brains, therefore, localization of SWA may prove useful in the presurgical workup of epileptic patients. We analyzed high-density electroencephalography (HDEEG) data across vigilance states from a reflex epilepsy patient with a clearly localizable ictal symptomatogenic zone to provide a proof-of-concept for the testability of this hypothesis. Methods: Overnight HDEEG recordings were obtained in the patient during REM sleep, NREM sleep, wakefulness, and during a right facial motor seizure then compared to 10 controls. After preprocessing, SWA (i.e., delta power; 1–4 Hz) was calculated at each channel. Scalp level and source reconstruction analyses were computed. We assessed for statistical differences in maximum SWA between the patient and controls within REM sleep, NREM sleep, wakefulness, and seizure. Then, we completed an identical statistical comparison after first subtracting intrasubject REM sleep SWA from that of NREM sleep, wakefulness, and seizure SWA. Results: The topographical analysis revealed greater left hemispheric SWA in the patient vs. controls in all vigilance states except REM sleep (which showed a right hemispheric maximum). Source space analysis revealed increased SWA in the left inferior frontal cortex during NREM sleep and wakefulness. Ictal data displayed poor source-space localization. Comparing each state to REM sleep enhanced localization accuracy; the most clearly localizing results were observed when subtracting REM sleep from wakefulness. Conclusion: State-dependent SWA during NREM sleep and wakefulness may help to identify aspects of the potential epileptogenic zone. Future work in larger cohorts may assess the clinical value of sleep SWA to help presurgical planning.
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Affiliation(s)
- Eric W Moffet
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Ruben Verhagen
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.,Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Benjamin Jones
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Graham Findlay
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Elsa Juan
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.,Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Tom Bugnon
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Armand Mensen
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | | | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Melanie Boly
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
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28
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Rivera Bonet CN, Hwang G, Hermann B, Struck AF, J Cook C, A Nair V, Mathis J, Allen L, Almane DN, Arkush K, Birn R, Conant LL, DeYoe EA, Felton E, Maganti R, Nencka A, Raghavan M, Shah U, Sosa VN, Ustine C, Prabhakaran V, Binder JR, Meyerand ME. Neuroticism in temporal lobe epilepsy is associated with altered limbic-frontal lobe resting-state functional connectivity. Epilepsy Behav 2020; 110:107172. [PMID: 32554180 PMCID: PMC7483612 DOI: 10.1016/j.yebeh.2020.107172] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/11/2020] [Accepted: 05/11/2020] [Indexed: 11/18/2022]
Abstract
Neuroticism, a core personality trait characterized by a tendency towards experiencing negative affect, has been reported to be higher in people with temporal lobe epilepsy (TLE) compared with healthy individuals. Neuroticism is a known predictor of depression and anxiety, which also occur more frequently in people with TLE. The purpose of this study was to identify abnormalities in whole-brain resting-state functional connectivity in relation to neuroticism in people with TLE and to determine the degree of unique versus shared patterns of abnormal connectivity in relation to elevated symptoms of depression and anxiety. Ninety-three individuals with TLE (55 females) and 40 healthy controls (18 females) from the Epilepsy Connectome Project (ECP) completed measures of neuroticism, depression, and anxiety, which were all significantly higher in people with TLE compared with controls. Resting-state functional connectivity was compared between controls and groups with TLE with high and low neuroticism using analysis of variance (ANOVA) and t-test. In secondary analyses, the same analytics were performed using measures of depression and anxiety and the unique variance in resting-state connectivity associated with neuroticism independent of symptoms of depression and anxiety identified. Increased neuroticism was significantly associated with hyposynchrony between the right hippocampus and Brodmann area (BA) 9 (region of prefrontal cortex (PFC)) (p < 0.005), representing a unique relationship independent of symptoms of depression and anxiety. Hyposynchrony of connection between the right hippocampus and BA47 (anterior frontal operculum) was associated with high neuroticism and with higher depression and anxiety scores (p < 0.05), making it a shared abnormal connection for the three measures. In conclusion, increased neuroticism exhibits both unique and shared patterns of abnormal functional connectivity with depression and anxiety symptoms between regions of the mesial temporal and frontal lobe.
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Affiliation(s)
| | - Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin-Madison, United States of America
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Cole J Cook
- Department of Medical Physics, University of Wisconsin-Madison, United States of America
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, United States of America
| | - Jedidiah Mathis
- Department of Radiology Froedtert & Medical College of Wisconsin, United States of America
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Dace N Almane
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Karina Arkush
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, United States of America
| | - Rasmus Birn
- Neuroscience Training Program, University of Wisconsin-Madison, United States of America; Department of Medical Physics, University of Wisconsin-Madison, United States of America; Department of Psychiatry, University of Wisconsin-Madison, United States of America
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Edgar A DeYoe
- Department of Radiology Froedtert & Medical College of Wisconsin, United States of America; Department of Biophysics, Medical College of Wisconsin, United States of America
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Andrew Nencka
- Department of Radiology Froedtert & Medical College of Wisconsin, United States of America
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Umang Shah
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, United States of America
| | - Veronica N Sosa
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, United States of America
| | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Vivek Prabhakaran
- Neuroscience Training Program, University of Wisconsin-Madison, United States of America; Department of Neurology, University of Wisconsin-Madison, United States of America; Department of Radiology, University of Wisconsin-Madison, United States of America
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, United States of America; Department of Biophysics, Medical College of Wisconsin, United States of America
| | - Mary E Meyerand
- Neuroscience Training Program, University of Wisconsin-Madison, United States of America; Department of Medical Physics, University of Wisconsin-Madison, United States of America; Department of Radiology, University of Wisconsin-Madison, United States of America
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29
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Singla S, Garcia GE, Rovenolt GE, Soto AL, Gilmore EJ, Hirsch LJ, Blumenfeld H, Sheth KN, Omay SB, Struck AF, Westover MB, Kim JA. Detecting Seizures and Epileptiform Abnormalities in Acute Brain Injury. Curr Neurol Neurosci Rep 2020; 20:42. [PMID: 32715371 DOI: 10.1007/s11910-020-01060-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Acute brain injury (ABI) is a broad category of pathologies, including traumatic brain injury, and is commonly complicated by seizures. Electroencephalogram (EEG) studies are used to detect seizures or other epileptiform patterns. This review seeks to clarify EEG findings relevant to ABI, explore practical barriers limiting EEG implementation, discuss strategies to leverage EEG monitoring in various clinical settings, and suggest an approach to utilize EEG for triage. RECENT FINDINGS Current literature suggests there is an increased morbidity and mortality risk associated with seizures or patterns on the ictal-interictal continuum (IIC) due to ABI. Further, increased use of EEG is associated with better clinical outcomes. However, there are many logistical barriers to successful EEG implementation that prohibit its ubiquitous use. Solutions to these limitations include the use of rapid EEG systems, non-expert EEG analysis, machine learning algorithms, and the incorporation of EEG data into prognostic models.
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Affiliation(s)
- Shobhit Singla
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Gabriella E Garcia
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Grace E Rovenolt
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Alexandria L Soto
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Emily J Gilmore
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Lawrence J Hirsch
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Hal Blumenfeld
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Kevin N Sheth
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - S Bulent Omay
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Jennifer A Kim
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA.
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30
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Hermann B, Conant LL, Cook CJ, Hwang G, Garcia-Ramos C, Dabbs K, Nair VA, Mathis J, Bonet CNR, Allen L, Almane DN, Arkush K, Birn R, DeYoe EA, Felton E, Maganti R, Nencka A, Raghavan M, Shah U, Sosa VN, Struck AF, Ustine C, Reyes A, Kaestner E, McDonald C, Prabhakaran V, Binder JR, Meyerand ME. Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy. Neuroimage Clin 2020; 27:102341. [PMID: 32707534 PMCID: PMC7381697 DOI: 10.1016/j.nicl.2020.102341] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 06/10/2020] [Accepted: 07/03/2020] [Indexed: 01/14/2023]
Abstract
This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis. The resulting cognitive subgroups were compared in regard to sociodemographic and clinical epilepsy characteristics as well as variations in brain structure and functional connectivity. Three cognitive subgroups were identified (intact, language/memory/executive function impairment, generalized impairment) which differed significantly, in a systematic fashion, across multiple features. The generalized impairment group was characterized by an earlier age at medication initiation (P < 0.05), fewer patient (P < 0.001) and parental years of education (P < 0.05), greater racial diversity (P < 0.05), and greater number of lifetime generalized seizures (P < 0.001). The three groups also differed in an orderly manner across total intracranial (P < 0.001) and bilateral cerebellar cortex volumes (P < 0.01), and rate of bilateral hippocampal atrophy (P < 0.014), but minimally in regional measures of cortical volume or thickness. In contrast, large-scale patterns of cortical-subcortical covariance networks revealed significant differences across groups in global and local measures of community structure and distribution of hubs. Resting-state fMRI revealed stepwise anomalies as a function of cluster membership, with the most abnormal patterns of connectivity evident in the generalized impairment group and no significant differences from controls in the cognitively intact group. Overall, the distinct underlying cognitive phenotypes of temporal lobe epilepsy harbor systematic relationships with clinical, sociodemographic and neuroimaging correlates. Cognitive phenotype variations in patient and familial education and ethnicity, with linked variations in total intracranial volume, raise the question of an early and persisting socioeconomic-status related neurodevelopmental impact, with additional contributions of clinical epilepsy factors (e.g., lifetime generalized seizures). The neuroimaging features of cognitive phenotype membership are most notable for disrupted large scale cortical-subcortical networks and patterns of functional connectivity with bilateral hippocampal and cerebellar atrophy. The cognitive taxonomy of temporal lobe epilepsy appears influenced by features that reflect the combined influence of socioeconomic, neurodevelopmental and neurobiological risk factors.
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Affiliation(s)
- Bruce Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Cole J Cook
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Camille Garcia-Ramos
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Veena A Nair
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jedidiah Mathis
- Department of Radiology Froedtert & Medical College of Wisconsin, Milwaukee, WI, USA
| | - Charlene N Rivera Bonet
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dace N Almane
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Karina Arkush
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, Milwaukee, WI, USA
| | - Rasmus Birn
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Edgar A DeYoe
- Department of Radiology Froedtert & Medical College of Wisconsin, Milwaukee, WI, USA; Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Rama Maganti
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Andrew Nencka
- Department of Radiology Froedtert & Medical College of Wisconsin, Milwaukee, WI, USA
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Umang Shah
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, Milwaukee, WI, USA
| | - Veronica N Sosa
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, Milwaukee, WI, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Anny Reyes
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Erik Kaestner
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Carrie McDonald
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Vivek Prabhakaran
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA; Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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31
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Struck AF, Tabaeizadeh M, Schmitt SE, Ruiz AR, Swisher CB, Subramaniam T, Hernandez C, Kaleem S, Haider HA, Cissé AF, Dhakar MB, Hirsch LJ, Rosenthal ES, Zafar SF, Gaspard N, Westover MB. Assessment of the Validity of the 2HELPS2B Score for Inpatient Seizure Risk Prediction. JAMA Neurol 2020; 77:500-507. [PMID: 31930362 DOI: 10.1001/jamaneurol.2019.4656] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Importance Seizure risk stratification is needed to boost inpatient seizure detection and to improve continuous electroencephalogram (cEEG) cost-effectiveness. 2HELPS2B can address this need but requires validation. Objective To use an independent cohort to validate the 2HELPS2B score and develop a practical guide for its use. Design, Setting, and Participants This multicenter retrospective medical record review analyzed clinical and EEG data from patients 18 years or older with a clinical indication for cEEG and an EEG duration of 12 hours or longer who were receiving consecutive cEEG at 6 centers from January 2012 to January 2019. 2HELPS2B was evaluated with the validation cohort using the mean calibration error (CAL), a measure of the difference between prediction and actual results. A Kaplan-Meier survival analysis was used to determine the duration of EEG monitoring to achieve a seizure risk of less than 5% based on the 2HELPS2B score calculated on first- hour (screening) EEG. Participants undergoing elective epilepsy monitoring and those who had experienced cardiac arrest were excluded. No participants who met the inclusion criteria were excluded. Main Outcomes and Measures The main outcome was a CAL error of less than 5% in the validation cohort. Results The study included 2111 participants (median age, 51 years; 1113 men [52.7%]; median EEG duration, 48 hours) and the primary outcome was met with a validation cohort CAL error of 4.0% compared with a CAL of 2.7% in the foundational cohort (P = .13). For the 2HELPS2B score calculated on only the first hour of EEG in those without seizures during that hour, the CAL error remained at less than 5.0% at 4.2% and allowed for stratifying patients into low- (2HELPS2B = 0; <5% risk of seizures), medium- (2HELPS2B = 1; 12% risk of seizures), and high-risk (2HELPS2B, ≥2; risk of seizures, >25%) groups. Each of the categories had an associated minimum recommended duration of EEG monitoring to achieve at least a less than 5% risk of seizures, a 2HELPS2B score of 0 at 1-hour screening EEG, a 2HELPS2B score of 1 at 12 hours, and a 2HELPS2B score of 2 or greater at 24 hours. Conclusions and Relevance In this study, 2HELPS2B was validated as a clinical tool to aid in seizure detection, clinical communication, and cEEG use in hospitalized patients. In patients without prior clinical seizures, a screening 1-hour EEG that showed no epileptiform findings was an adequate screen. In patients with any highly epileptiform EEG patterns during the first hour of EEG (ie, a 2HELPS2B score of ≥2), at least 24 hours of recording is recommended.
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Affiliation(s)
- Aaron F Struck
- Department of Neurology, University of Wisconsin, Madison
| | - Mohammad Tabaeizadeh
- Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts
| | - Sarah E Schmitt
- Department of Neurology, Medical University of South Carolina, Charleston
| | | | | | | | | | - Safa Kaleem
- Department of Neurology, Duke University, Durham, North Carolina
| | - Hiba A Haider
- Department of Neurology, Emory University, Atlanta, Georgia
| | - Abbas Fodé Cissé
- Hôpital Erasme, Département de Neurologie, Université Libre de Bruxelles, Bruxelles, Belgium
| | | | | | - Eric S Rosenthal
- Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts
| | - Sahar F Zafar
- Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts
| | - Nicholas Gaspard
- Hôpital Erasme, Département de Neurologie, Université Libre de Bruxelles, Bruxelles, Belgium
| | - M Brandon Westover
- Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts
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Cissé FA, Osman GM, Legros B, Depondt C, Hirsch LJ, Struck AF, Gaspard N. Validation of an algorithm of time-dependent electro-clinical risk stratification for electrographic seizures (TERSE) in critically ill patients. Clin Neurophysiol 2020; 131:1956-1961. [PMID: 32622337 DOI: 10.1016/j.clinph.2020.05.031] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 05/05/2020] [Accepted: 05/20/2020] [Indexed: 01/04/2023]
Abstract
OBJECTIVE The clinical implementation of continuous electroencephalography (CEEG) monitoring in critically ill patients is hampered by the substantial burden of work that it entails for clinical neurophysiologists. Solutions that might reduce this burden, including by shortening the duration of EEG to be recorded, would help its widespread adoption. Our aim was to validate a recently described algorithm of time-dependent electro-clinical risk stratification for electrographic seizure (ESz) (TERSE) based on simple clinical and EEG features. METHODS We retrospectively reviewed the medical records and EEG recordings of consecutive patients undergoing CEEG between October 1, 2015 and September, 30 2016 and assessed the sensitivity of TERSE for seizure detection, as well as the reduction in EEG time needed to be reviewed. RESULTS In a cohort of 407 patients and compared to full CEEG review, the model allowed the detection of 95% of patients with ESz and 97% of those with electrographic status epilepticus. The amount of CEEG to be recorded to detect ESz was reduced by two-thirds, compared to the duration of CEEG taht was actually recorded. CONCLUSIONS TERSE allowed accurate time-dependent ESz risk stratification with a high sensitivity for ESz detection, which could substantially reduce the amount of CEEG to be recorded and reviewed, if applied prospectively in clinical practice. SIGNIFICANCE Time-dependent electro-clinical risk stratification, such as TERSE, could allow more efficient practice of CEEG and its more widespread adoption. Future studies should aim to improve risk stratification in the subgroup of patients with acute brain injury and absence of clinical seizures.
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Affiliation(s)
- F A Cissé
- Department of Neurology, Université Libre de Bruxelles - Hôpital Erasme, Bruxelles, Belgium; Department of Neurology, CHU de Conakry, Conakry, Guinea
| | - G M Osman
- Department of Neurology, Henry Ford Hospital, Detroit, MI, USA; Department of Neurology and Comprehensive Epilepsy Center, Yale University, New Haven, CT, USA
| | - B Legros
- Department of Neurology, Université Libre de Bruxelles - Hôpital Erasme, Bruxelles, Belgium
| | - C Depondt
- Department of Neurology, Université Libre de Bruxelles - Hôpital Erasme, Bruxelles, Belgium
| | - L J Hirsch
- Department of Neurology and Comprehensive Epilepsy Center, Yale University, New Haven, CT, USA
| | - A F Struck
- Department of Neurology, University of Wisconsin, Madison, WI, USA
| | - N Gaspard
- Department of Neurology, Université Libre de Bruxelles - Hôpital Erasme, Bruxelles, Belgium; Department of Neurology and Comprehensive Epilepsy Center, Yale University, New Haven, CT, USA.
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Hermann BP, Struck AF, Stafstrom CE, Hsu DA, Dabbs K, Gundlach C, Almane D, Seidenberg M, Jones JE. Behavioral phenotypes of childhood idiopathic epilepsies. Epilepsia 2020; 61:1427-1437. [PMID: 32557544 DOI: 10.1111/epi.16569] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 05/11/2020] [Accepted: 05/11/2020] [Indexed: 01/10/2023]
Abstract
OBJECTIVE To characterize the presence and nature of discrete behavioral phenotypes and their correlates in a cohort of youth with new and recent onset focal and generalized epilepsies. METHODS The parents of 290 youth (age = 8-18 years) with epilepsy (n = 183) and typically developing participants (n = 107) completed the Child Behavior Checklist for children aged 6-18 from the Achenbach System of Empirically Based Assessment. The eight behavior problem scales were subjected to hierarchical clustering analytics to identify behavioral subgroups. To characterize the external validity and co-occurring comorbidities of the identified subgroups, we examined demographic features (age, gender, handedness), cognition (language, perception, attention, executive function, speed), academic problems (present/absent), clinical epilepsy characteristics (epilepsy syndrome, medications), familial factors (parental intelligence quotient, education, employment), neuroimaging features (cortical thickness), parent-observed day-to-day executive function, and number of lifetime-to-date Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) diagnoses. RESULTS Hierarchical clustering identified three behavioral phenotypes, which included no behavioral complications (Cluster 1, 67% of epilepsy cohort [n = 122]), nonexternalizing problems (Cluster 2, 11% of cohort [n = 21]), and combined internalizing and externalizing problems (Cluster 3, 22% of cohort [n = 40]). These behavioral phenotypes were characterized by orderly differences in personal characteristics, neuropsychological status, history of academic problems, parental status, cortical thickness, daily executive function, and number of lifetime-to-date DSM-IV diagnoses. Cluster 1 was most similar to controls across most metrics, whereas Cluster 3 was the most abnormal compared to controls. Epilepsy syndrome was not a predictor of cluster membership. SIGNIFICANCE Youth with new and recent onset epilepsy fall into three distinct behavioral phenotypes associated with a variety of co-occurring features and comorbidities. This approach identifies important phenotypes of behavior problem presentations and their accompanying factors that serve to advance clinical and theoretical understanding of the behavioral complications of children with epilepsy and the complex conditions with which they co-occur.
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Affiliation(s)
- Bruce P Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Carl E Stafstrom
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA
| | - David A Hsu
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Carson Gundlach
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Dace Almane
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Michael Seidenberg
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois, USA
| | - Jana E Jones
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
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Zafar SF, Subramaniam T, Osman G, Herlopian A, Struck AF. Electrographic seizures and ictal-interictal continuum (IIC) patterns in critically ill patients. Epilepsy Behav 2020; 106:107037. [PMID: 32222672 DOI: 10.1016/j.yebeh.2020.107037] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/07/2020] [Accepted: 03/07/2020] [Indexed: 02/06/2023]
Abstract
Critical care long-term continuous electroencephalogram (cEEG) monitoring has expanded dramatically in the last several decades spurned by technological advances in EEG digitalization and several key clinical findings: 1-Seizures are relatively common in the critically ill-large recent observational studies suggest that around 20% of critically ill patients placed on cEEG have seizures. 2-The majority (~75%) of patients who have seizures have exclusively "electrographic seizures", that is, they have no overt ictal clinical signs. Along with the discovery of the unexpectedly high incidence of seizures was the high prevalence of EEG patterns that share some common features with archetypical electrographic seizures but are not uniformly considered to be "ictal". These EEG patterns include lateralized periodic discharges (LPDs) and generalized periodic discharges (GPDs)-patterns that at times exhibit ictal-like behavior and at other times behave more like an interictal finding. Dr. Hirsch and colleagues proposed a conceptual framework to describe this spectrum of patterns called the ictal-interictal continuum (IIC). In the following years, investigators began to answer some of the key pragmatic clinical concerns such as which patients are at risk of seizures and what is the optimal duration of cEEG use. At the same time, investigators have begun probing the core questions for critical care EEG-what is the underlying pathophysiology of these patterns, at what point do these patterns cause secondary brain injury, what are the optimal treatment strategies, and how do these patterns affect clinical outcomes such as neurological disability and the development of epilepsy. In this review, we cover recent advancements in both practical concerns regarding cEEG use, current treatment strategies, and review the evidence associating IIC/seizures with poor clinical outcomes.
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Affiliation(s)
- Sahar F Zafar
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States of America
| | - Thanujaa Subramaniam
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States of America
| | - Gamaleldin Osman
- Department of Neurology, Henry Ford Hospital, Detroit, MI, United States of America
| | - Aline Herlopian
- Department of Neurology, Yale University, New Haven, CT, United States of America
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States of America.
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Elmer J, Coppler PJ, Solanki P, Westover MB, Struck AF, Baldwin ME, Kurz MC, Callaway CW. Sensitivity of Continuous Electroencephalography to Detect Ictal Activity After Cardiac Arrest. JAMA Netw Open 2020; 3:e203751. [PMID: 32343353 PMCID: PMC7189220 DOI: 10.1001/jamanetworkopen.2020.3751] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
IMPORTANCE Epileptiform electroencephalographic (EEG) patterns are common after resuscitation from cardiac arrest, are associated with patient outcome, and may require treatment. It is unknown whether continuous EEG monitoring is needed to detect these patterns or if brief intermittent monitoring is sufficient. If continuous monitoring is required, the necessary duration of observation is unknown. OBJECTIVE To quantify the time-dependent sensitivity of continuous EEG for epileptiform event detection, and to compare continuous EEG to several alternative EEG-monitoring strategies for post-cardiac arrest outcome prediction. DESIGN, SETTING, AND PARTICIPANTS This observational cohort study was conducted in 2 academic medical centers between September 2010 and January 2018. Participants included 759 adults who were comatose after being resuscitated from cardiac arrest and who underwent 24 hours or more of EEG monitoring. MAIN OUTCOMES AND MEASURES Epileptiform EEG patterns associated with neurological outcome at hospital discharge, such as seizures likely to cause secondary injury. RESULTS Overall, 759 patients were included in the analysis; 281 (37.0%) were female, and the mean (SD) age was 58 (17) years. Epileptiform EEG activity was observed in 414 participants (54.5%), of whom only 26 (3.4%) developed potentially treatable seizures. Brief intermittent EEG had an estimated 66% (95% CI, 62%-69%) to 68% (95% CI, 66%-70%) sensitivity for detection of prognostic epileptiform events. Depending on initial continuity of the EEG background, 0 to 51 hours of monitoring were needed to achieve 95% sensitivity for the detection of prognostic epileptiform events. Brief intermittent EEG had a sensitivity of 7% (95% CI, 4%-12%) to 8% (95% CI, 4%-12%) for the detection of potentially treatable seizures, and 0 to 53 hours of continuous monitoring were needed to achieve 95% sensitivity for the detection of potentially treatable seizures. Brief intermittent EEG results yielded similar information compared with continuous EEG results when added to multivariable models predicting neurological outcome. CONCLUSIONS AND RELEVANCE Compared with continuous EEG monitoring, brief intermittent monitoring was insensitive for detection of epileptiform events. Monitoring EEG results significantly improved multimodality prediction of neurological outcome, but continuous monitoring appeared to add little additional information compared with brief intermittent monitoring.
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Affiliation(s)
- Jonathan Elmer
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Patrick J. Coppler
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Pawan Solanki
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | | | | | - Maria E. Baldwin
- Department of Neurology, Pittsburgh VA Medical Center, Pittsburgh, Pennsylvania
| | - Michael C. Kurz
- Department of Emergency Medicine, University of Alabama at Birmingham School of Medicine
| | - Clifton W. Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
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Moffet EW, Subramaniam T, Hirsch LJ, Gilmore EJ, Lee JW, Rodriguez-Ruiz AA, Haider HA, Dhakar MB, Jadeja N, Osman G, Gaspard N, Struck AF. Validation of the 2HELPS2B Seizure Risk Score in Acute Brain Injury Patients. Neurocrit Care 2020; 33:701-707. [PMID: 32107733 DOI: 10.1007/s12028-020-00939-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
BACKGROUND AND OBJECTIVE Seizures are common after traumatic brain injury (TBI), aneurysmal subarachnoid hemorrhage (aSAH), subdural hematoma (SDH), and non-traumatic intraparenchymal hemorrhage (IPH)-collectively defined herein as acute brain injury (ABI). Most seizures in ABI are subclinical, meaning that they are only detectable with EEG. A method is required to identify patients at greatest risk of seizures and thereby in need of prolonged continuous EEG monitoring. 2HELPS2B is a simple point system developed to address this need. 2HELPS2B estimates seizure risk for hospitalized patients using five EEG findings and one clinical finding (pre-EEG seizure). The initial 2HELPS2B study did not specifically assess the ABI subpopulation. In this study, we aim to validate the 2HELPS2B score in ABI and determine its relative predictive accuracy compared to a broader set of clinical and electrographic factors. METHODS We queried the Critical Care EEG Monitoring Research Consortium database for ABI patients age ≥ 18 with > 6 h of continuous EEG monitoring; data were collected between February 2013 and November 2018. The primary outcome was electrographic seizure. Clinical factors considered were age, coma, encephalopathy, ABI subtype, and acute suspected or confirmed pre-EEG clinical seizure. Electrographic factors included 18 EEG findings. Predictive accuracy was assessed using a machine-learning paradigm with area under the receiver operator characteristic (ROC) curve as the primary outcome metric. Three models (clinical factors alone, EEG factors alone, EEG and clinical factors combined) were generated using elastic-net logistic regression. Models were compared to each other and to the 2HELPS2B model. All models were evaluated by calculating the area under the curve (AUC) of a ROC analysis and then compared using permutation testing of AUC with bootstrapping to generate confidence intervals. RESULTS A total of 1528 ABI patients were included. Total seizure incidence was 13.9%. Seizure incidence among ABI subtype varied: IPH 17.2%, SDH 19.1%, aSAH 7.6%, TBI 9.2%. Age ≥ 65 (p = 0.015) and pre-cEEG acute clinical seizure (p < 0.001) positively affected seizure incidence. Clinical factors AUC = 0.65 [95% CI 0.60-0.71], EEG factors AUC = 0.82 [95% CI 0.77-0.87], and EEG and clinical factors combined AUC = 0.84 [95% CI 0.80-0.88]. 2HELPS2B AUC = 0.81 [95% CI 0.76-0.85]. The 2HELPS2B AUC did not differ from EEG factors (p = 0.51), or EEG and clinical factors combined (p = 0.23), but was superior to clinical factors alone (p < 0.001). CONCLUSIONS Accurate seizure risk forecasting in ABI requires the assessment of EEG markers of pathologic electro-cerebral activity (e.g., sporadic epileptiform discharges and lateralized periodic discharges). The 2HELPS2B score is a reliable and simple method to quantify these EEG findings and their associated risk of seizure.
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Affiliation(s)
- Eric W Moffet
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, 7131 MFCB, 600 Highland Avenue, Madison, WI, 53705, USA.,Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Thanujaa Subramaniam
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, 7131 MFCB, 600 Highland Avenue, Madison, WI, 53705, USA
| | - Lawrence J Hirsch
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Emily J Gilmore
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Hiba A Haider
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Monica B Dhakar
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Neville Jadeja
- Department of Neurology, UMass Memorial Medical Center, Worcester, MA, USA
| | - Gamaledin Osman
- Department of Neurology, Henry Ford Hospital, Detroit, MI, USA
| | - Nicolas Gaspard
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA.,Département de Neurologie, Université Libre de Bruxelles, Hôspital Erasme, Brussels, Belgium
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, 7131 MFCB, 600 Highland Avenue, Madison, WI, 53705, USA.
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Kang X, Boly M, Findlay G, Jones B, Gjini K, Maganti R, Struck AF. Quantitative spatio-temporal characterization of epileptic spikes using high density EEG: Differences between NREM sleep and REM sleep. Sci Rep 2020; 10:1673. [PMID: 32015406 PMCID: PMC6997449 DOI: 10.1038/s41598-020-58612-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 01/17/2020] [Indexed: 12/13/2022] Open
Abstract
In this study, we applied high-density EEG recordings (HD-EEG) to quantitatively characterize the fine-grained spatiotemporal distribution of inter-ictal epileptiform discharges (IEDs) across different sleep stages. We quantified differences in spatial extent and duration of IEDs at the scalp and cortical levels using HD-EEG source-localization, during non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep, in six medication-refractory focal epilepsy patients during epilepsy monitoring unit admission. Statistical analyses were performed at single subject level and group level across different sleep stages for duration and distribution of IEDs. Tests were corrected for multiple comparisons across all channels and time points. Compared to NREM sleep, IEDs during REM sleep were of significantly shorter duration and spatially more restricted. Compared to NREM sleep, IEDs location in REM sleep also showed a higher concordance with electrographic ictal onset zone from scalp EEG recording. This study supports the localizing value of REM IEDs over NREM IEDs and suggests that HD-EEG may be of clinical utility in epilepsy surgery work-up.
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Affiliation(s)
- Xuan Kang
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA
| | - Melanie Boly
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA.,University of Wisconsin-Madison Department of Psychiatry, Madison, Wisconsin, 53705, USA
| | - Graham Findlay
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA.,University of Wisconsin-Madison Department of Psychiatry, Madison, Wisconsin, 53705, USA
| | - Benjamin Jones
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA.,University of Wisconsin-Madison Department of Psychiatry, Madison, Wisconsin, 53705, USA
| | - Klevest Gjini
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA
| | - Rama Maganti
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA
| | - Aaron F Struck
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA.
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Hwang G, Hermann B, Nair VA, Conant LL, Dabbs K, Mathis J, Cook CJ, Rivera-Bonet CN, Mohanty R, Zhao G, Almane DN, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Humphries CJ, Raghavan M, DeYoe EA, Bendlin BB, Prabhakaran V, Binder JR, Meyerand ME. Brain aging in temporal lobe epilepsy: Chronological, structural, and functional. Neuroimage Clin 2020; 25:102183. [PMID: 32058319 PMCID: PMC7016276 DOI: 10.1016/j.nicl.2020.102183] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 12/03/2019] [Accepted: 01/13/2020] [Indexed: 10/26/2022]
Abstract
The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients' brain ages. It was found that TLE patients on average have both older structural (+6.6 years) and functional (+8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional - chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.
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Affiliation(s)
- Gyujoon Hwang
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
| | - Bruce Hermann
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Veena A Nair
- Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Lisa L Conant
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Kevin Dabbs
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jed Mathis
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Cole J Cook
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Rosaleena Mohanty
- Electrical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Gengyan Zhao
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Dace N Almane
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew Nencka
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Aaron F Struck
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Rasmus Birn
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Rama Maganti
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Manoj Raghavan
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Edgar A DeYoe
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Vivek Prabhakaran
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Mary E Meyerand
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
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Struck AF, Rodriguez-Ruiz AA, Osman G, Gilmore EJ, Haider HA, Dhakar MB, Schrettner M, Lee JW, Gaspard N, Hirsch LJ, Westover MB. Comparison of machine learning models for seizure prediction in hospitalized patients. Ann Clin Transl Neurol 2019; 6:1239-1247. [PMID: 31353866 PMCID: PMC6649418 DOI: 10.1002/acn3.50817] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 05/21/2019] [Accepted: 05/23/2019] [Indexed: 12/19/2022] Open
Abstract
Objective To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1‐h screening EEG to identify low‐risk patients (<5% seizures risk in 48 h). Methods The Critical Care EEG Monitoring Research Consortium (CCEMRC) multicenter database contains 7716 continuous EEGs (cEEG). Neural networks (NN), elastic net logistic regression (EN), and sparse linear integer model (RiskSLIM) were trained to predict seizures. RiskSLIM was used previously to generate 2HELPS2B model of seizure predictions. Data were divided into training (60% for model fitting) and evaluation (40% for model evaluation) cohorts. Performance was measured using area under the receiver operating curve (AUC), mean risk calibration (CAL), and negative predictive value (NPV). A secondary analysis was performed using Monte Carlo simulation (MCS) to normalize all EEG recordings to 48 h and use only the first hour of EEG as a “screening EEG” to generate predictions. Results RiskSLIM recreated the 2HELPS2B model. All models had comparable AUC: evaluation cohort (NN: 0.85, EN: 0.84, 2HELPS2B: 0.83) and MCS (NN: 0.82, EN; 0.82, 2HELPS2B: 0.81) and NPV (absence of seizures in the group that the models predicted to be low risk): evaluation cohort (NN: 97%, EN: 97%, 2HELPS2B: 97%) and MCS (NN: 97%, EN: 99%, 2HELPS2B: 97%). 2HELPS2B model was able to identify the largest proportion of low‐risk patients. Interpretation For seizure risk stratification of hospitalized patients, the RiskSLIM generated 2HELPS2B model compares favorably to the complex NN and EN generated models. 2HELPS2B is able to accurately and quickly identify low‐risk patients with only a 1‐h screening EEG.
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Affiliation(s)
- Aaron F Struck
- Department of Neurology, University of Wisconsin, Madison, Wisconsin
| | | | - Gamaledin Osman
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
| | - Emily J Gilmore
- Department of Neurology, Yale University, New Haven, Connecticut
| | - Hiba A Haider
- Department of Neurology, Emory University, Atlanta, Georgia
| | | | - Matthew Schrettner
- Department of Neurology, University of South Carolina Greenville, Greenville, South Carolina
| | - Jong W Lee
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Nicolas Gaspard
- Department of Neurology, Yale University, New Haven, Connecticut.,Département de Neurologie, Université Libre de Bruxelles, Hôpital Erasme, Bruxelles, Belgium
| | | | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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Hwang G, Dabbs K, Conant L, Nair VA, Mathis J, Almane DN, Nencka A, Birn R, Humphries C, Raghavan M, DeYoe EA, Struck AF, Maganti R, Binder JR, Meyerand E, Prabhakaran V, Hermann B. Cognitive slowing and its underlying neurobiology in temporal lobe epilepsy. Cortex 2019; 117:41-52. [PMID: 30927560 DOI: 10.1016/j.cortex.2019.02.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 12/06/2018] [Accepted: 02/23/2019] [Indexed: 11/17/2022]
Abstract
Cognitive slowing is a known but comparatively under-investigated neuropsychological complication of the epilepsies in relation to other known cognitive comorbidities such as memory, executive function and language. Here we focus on a novel metric of processing speed, characterize its relative salience compared to other cognitive difficulties in epilepsy, and explore its underlying neurobiological correlates. Research participants included 55 patients with temporal lobe epilepsy (TLE) and 58 healthy controls from the Epilepsy Connectome Project (ECP) who were administered a battery of tests yielding 14 neuropsychological measures, including selected tests from the NIH Toolbox-Cognitive Battery, and underwent 3T MRI and resting state fMRI. TLE patients exhibited a pattern of generalized cognitive impairment with very few lateralized abnormalities. Using the neuropsychological measures, machine learning (Support Vector Machine binary classification model) classified the TLE and control groups with 74% accuracy with processing speed (NIH Toolbox Pattern Comparison Processing Speed Test) the best predictor. In TLE, slower processing speed was associated predominantly with decreased local gyrification in regions including the rostral and caudal middle frontal gyrus, inferior precentral cortex, insula, inferior parietal cortex (angular and supramarginal gyri), lateral occipital cortex, rostral anterior cingulate, and medial orbital frontal regions, as well as three small regions of the temporal lobe. Slower processing speed was also associated with decreased connectivity between the primary visual cortices in both hemispheres and the left supplementary motor area, as well as between the right parieto-occipital sulcus and right middle insular area. Overall, slowed processing speed is an important cognitive comorbidity of TLE associated with altered brain structure and connectivity.
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Affiliation(s)
- Gyujoon Hwang
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Kevin Dabbs
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Lisa Conant
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Veena A Nair
- Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jed Mathis
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dace N Almane
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew Nencka
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Rasmus Birn
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Manoj Raghavan
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Edgar A DeYoe
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Aaron F Struck
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Rama Maganti
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Elizabeth Meyerand
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Vivek Prabhakaran
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Bruce Hermann
- Neurology, University of Wisconsin-Madison, Madison, WI, USA.
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41
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Hwang G, Nair VA, Mathis J, Cook CJ, Mohanty R, Zhao G, Tellapragada N, Ustine C, Nwoke OO, Rivera-Bonet C, Rozman M, Allen L, Forseth C, Almane DN, Kraegel P, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Conant LL, Humphries CJ, Hermann B, Raghavan M, DeYoe EA, Binder JR, Meyerand E, Prabhakaran V. Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning. Brain Connect 2019; 9:184-193. [PMID: 30803273 PMCID: PMC6484357 DOI: 10.1089/brain.2018.0601] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.
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Affiliation(s)
- Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Veena A. Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jed Mathis
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Cole J. Cook
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rosaleena Mohanty
- Department of Electrical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Gengyan Zhao
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | | | - Megan Rozman
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Courtney Forseth
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Dace N. Almane
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Peter Kraegel
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rasmus Birn
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Lisa L. Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Colin J. Humphries
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Edgar A. DeYoe
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jeffrey R. Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Vivek Prabhakaran
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
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Subramaniam T, Jain A, Hall LT, Cole AJ, Westover MB, Rosenthal ES, Struck AF. Lateralized periodic discharges frequency correlates with glucose metabolism. Neurology 2019; 92:e670-e674. [PMID: 30635488 DOI: 10.1212/wnl.0000000000006903] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Accepted: 11/14/2018] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE To investigate the correlation between characteristics of lateralized periodic discharges (LPDs) and glucose metabolism measured by 18F-fluorodeoxyglucose (FDG)-PET. METHODS We retrospectively reviewed medical records to identify patients who underwent FDG-PET during EEG monitoring with LPDs present during the FDG uptake period. Two blinded board-certified neurophysiologists independently interpreted EEGs. FDG uptake was measured using standardized uptake value (SUV). Structural images were fused with PET images to aid with localization of SUV. Two PET readers independently measured maximum SUV. Relative SUV values were obtained by normalization of the maximum SUV to the SUV of pons (SUVRpons). LPD frequency was analyzed both as a categorical variable and as a continuous measure. Other secondary variables included duration, amplitude, presence of structural lesion, and "plus" EEG features such as rhythmic or fast sharp activity. RESULTS Nine patients were identified and 7 had a structural etiology for LPDs. Analysis using frequency as a categorical variable and continuous variable showed an association between increased LPD frequency and increased ipsilateral SUVRpons (p = 0.02). Metabolism associated with LPDs (0.5 Hz as a baseline) increased by a median of 100% at 1 Hz and for frequencies >1 Hz increased by a median of 309%. There were no statistically significant differences in SUVRpons for other factors including duration (p = 0.10), amplitude (p = 0.80), structural etiology (p = 0.55), or "plus" features such as rhythmic or fast sharp activity (p = 0.84). CONCLUSIONS Metabolic activity increases monotonically with LPD frequency. LPD frequency should be a measure of interest when developing neuroprotection strategies in critical neurologic illness.
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Affiliation(s)
- Thanujaa Subramaniam
- From the Departments of Neurology (T.S., A.F.S.) and Radiology (A.J., L.T.H.), University of Wisconsin-Madison; and Department of Neurology (A.J.C., M.B.W., E.R.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - Aditya Jain
- From the Departments of Neurology (T.S., A.F.S.) and Radiology (A.J., L.T.H.), University of Wisconsin-Madison; and Department of Neurology (A.J.C., M.B.W., E.R.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - Lance T Hall
- From the Departments of Neurology (T.S., A.F.S.) and Radiology (A.J., L.T.H.), University of Wisconsin-Madison; and Department of Neurology (A.J.C., M.B.W., E.R.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - Andrew J Cole
- From the Departments of Neurology (T.S., A.F.S.) and Radiology (A.J., L.T.H.), University of Wisconsin-Madison; and Department of Neurology (A.J.C., M.B.W., E.R.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - M Brandon Westover
- From the Departments of Neurology (T.S., A.F.S.) and Radiology (A.J., L.T.H.), University of Wisconsin-Madison; and Department of Neurology (A.J.C., M.B.W., E.R.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - Eric S Rosenthal
- From the Departments of Neurology (T.S., A.F.S.) and Radiology (A.J., L.T.H.), University of Wisconsin-Madison; and Department of Neurology (A.J.C., M.B.W., E.R.), Massachusetts General Hospital, Harvard Medical School, Boston
| | - Aaron F Struck
- From the Departments of Neurology (T.S., A.F.S.) and Radiology (A.J., L.T.H.), University of Wisconsin-Madison; and Department of Neurology (A.J.C., M.B.W., E.R.), Massachusetts General Hospital, Harvard Medical School, Boston.
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43
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van Leeuwen KG, Sun H, Tabaeizadeh M, Struck AF, van Putten MJAM, Westover MB. Detecting abnormal electroencephalograms using deep convolutional networks. Clin Neurophysiol 2018; 130:77-84. [PMID: 30481649 DOI: 10.1016/j.clinph.2018.10.012] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 09/22/2018] [Accepted: 10/27/2018] [Indexed: 12/14/2022]
Abstract
OBJECTIVES Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of contextual factors, including age and sleep stage. Our objectives were to validate prior work on an independent data set suggesting that deep learning methods can discriminate between normal vs abnormal EEGs, to understand whether age and sleep stage information can improve discrimination, and to understand what factors lead to errors. METHODS We train a deep convolutional neural network on a heterogeneous set of 8522 routine EEGs from the Massachusetts General Hospital. We explore several strategies for optimizing model performance, including accounting for age and sleep stage. RESULTS The area under the receiver operating characteristic curve (AUC) on an independent test set (n = 851) is 0.917 marginally improved by including age (AUC = 0.924), and both age and sleep stages (AUC = 0.925), though not statistically significant. CONCLUSIONS The model architecture generalizes well to an independent dataset. Adding age and sleep stage to the model does not significantly improve performance. SIGNIFICANCE Insights learned from misclassified examples, and minimal improvement by adding sleep stage and age suggest fruitful directions for further research.
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Affiliation(s)
- K G van Leeuwen
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; University of Twente, Enschede, the Netherlands
| | - H Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - M Tabaeizadeh
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - A F Struck
- Department of Neurology, Wisconsin Hospital and Clinics, Madison, WI, USA
| | - M J A M van Putten
- University of Twente, Enschede, the Netherlands; Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - M B Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
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44
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Struck AF, Ustun B, Ruiz AR, Lee JW, LaRoche SM, Hirsch LJ, Gilmore EJ, Vlachy J, Haider HA, Rudin C, Westover MB. Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients. JAMA Neurol 2017; 74:1419-1424. [PMID: 29052706 DOI: 10.1001/jamaneurol.2017.2459] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Importance Continuous electroencephalography (EEG) use in critically ill patients is expanding. There is no validated method to combine risk factors and guide clinicians in assessing seizure risk. Objective To use seizure risk factors from EEG and clinical history to create a simple scoring system associated with the probability of seizures in patients with acute illness. Design, Setting, and Participants We used a prospective multicenter (Emory University Hospital, Brigham and Women's Hospital, and Yale University Hospital) database containing clinical and electrographic variables on 5427 continuous EEG sessions from eligible patients if they had continuous EEG for clinical indications, excluding epilepsy monitoring unit admissions. We created a scoring system model to estimate seizure risk in acutely ill patients undergoing continuous EEG. The model was built using a new machine learning method (RiskSLIM) that is designed to produce accurate, risk-calibrated scoring systems with a limited number of variables and small integer weights. We validated the accuracy and risk calibration of our model using cross-validation and compared its performance with models built with state-of-the-art logistic regression methods. The database was developed by the Critical Care EEG Research Consortium and used data collected over 3 years. The EEG variables were interpreted using standardized terminology by certified reviewers. Exposures All patients had more than 6 hours of uninterrupted EEG recordings. Main Outcomes and Measures The main outcome was the average risk calibration error. Results There were 5427 continuous EEGs performed on 4772 participants (2868 men, 49.9%; median age, 61 years) performed at 3 institutions, without further demographic stratification. Our final model, 2HELPS2B, had an area under the curve of 0.819 and average calibration error of 2.7% (95% CI, 2.0%-3.6%). It included 6 variables with the following point assignments: (1) brief (ictal) rhythmic discharges (B[I]RDs) (2 points); (2) presence of lateralized periodic discharges, lateralized rhythmic delta activity, or bilateral independent periodic discharges (1 point); (3) prior seizure (1 point); (4) sporadic epileptiform discharges (1 point); (5) frequency greater than 2.0 Hz for any periodic or rhythmic pattern (1 point); and (6) presence of "plus" features (superimposed, rhythmic, sharp, or fast activity) (1 point). The probable seizure risk of each score was 5% for a score of 0, 12% for a score of 1, 27% for a score of 2, 50% for a score of 3, 73% for a score of 4, 88% for a score of 5, and greater than 95% for a score of 6 or 7. Conclusions and Relevance The 2HELPS2B model is a quick accurate tool to aid clinical judgment of the risk of seizures in critically ill patients.
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Affiliation(s)
- Aaron F Struck
- Department of Neurology, University of Wisconsin, Madison
| | - Berk Ustun
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge
| | | | - Jong Woo Lee
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Suzette M LaRoche
- Emory University School of Medicine, Atlanta, Georgia.,Mission Health, Asheville, North Carolina
| | | | | | - Jan Vlachy
- Georgia Institute of Technology, Atlanta
| | | | - Cynthia Rudin
- Departments of Computer Science & Electrical and Computer Engineering, Duke University, Durham, North Carolina
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45
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Struck AF, Osman G, Rampal N, Biswal S, Legros B, Hirsch LJ, Westover MB, Gaspard N. Time-dependent risk of seizures in critically ill patients on continuous electroencephalogram. Ann Neurol 2017; 82:177-185. [PMID: 28681492 DOI: 10.1002/ana.24985] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 06/12/2017] [Accepted: 06/19/2017] [Indexed: 01/06/2023]
Abstract
OBJECTIVE Find the optimal continuous electroencephalographic (CEEG) monitoring duration for seizure detection in critically ill patients. METHODS We analyzed prospective data from 665 consecutive CEEGs, including clinical factors and time-to-event emergence of electroencephalographic (EEG) findings over 72 hours. Clinical factors were selected using logistic regression. EEG risk factors were selected a priori. Clinical factors were used for baseline (pre-EEG) risk. EEG findings were used for the creation of a multistate survival model with 3 states (entry, EEG risk, and seizure). EEG risk state is defined by emergence of epileptiform patterns. RESULTS The clinical variables of greatest predictive value were coma (31% had seizures; odds ratio [OR] = 1.8, p < 0.01) and history of seizures, either remotely or related to acute illness (34% had seizures; OR = 3.0, p < 0.001). If there were no epileptiform findings on EEG, the risk of seizures within 72 hours was between 9% (no clinical risk factors) and 36% (coma and history of seizures). If epileptiform findings developed, the seizure incidence was between 18% (no clinical risk factors) and 64% (coma and history of seizures). In the absence of epileptiform EEG abnormalities, the duration of monitoring needed for seizure risk of <5% was between 0.4 hours (for patients who are not comatose and had no prior seizure) and 16.4 hours (comatose and prior seizure). INTERPRETATION The initial risk of seizures on CEEG is dependent on history of prior seizures and presence of coma. The risk of developing seizures on CEEG decays to <5% by 24 hours if no epileptiform EEG abnormalities emerge, independent of initial clinical risk factors. Ann Neurol 2017;82:177-185.
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Affiliation(s)
- Aaron F Struck
- Department of Neurology, University of Wisconsin, Madison, WI
| | - Gamaleldin Osman
- Department of Neurology, Yale University School of Medicine, New Haven, CT
| | - Nishi Rampal
- Department of Neurology, Yale University School of Medicine, New Haven, CT
| | | | - Benjamin Legros
- Department of Neurology, Free University of Brussels, Brussels, Belgium
| | - Lawrence J Hirsch
- Department of Neurology, Yale University School of Medicine, New Haven, CT
| | | | - Nicolas Gaspard
- Department of Neurology, Free University of Brussels, Brussels, Belgium
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46
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Herlopian A, Rosenthal ES, Chu CJ, Cole AJ, Struck AF. Extreme delta brush evolving into status epilepticus in a patient with anti-NMDA encephalitis. Epilepsy Behav Case Rep 2016; 7:69-71. [PMID: 28616386 PMCID: PMC5459970 DOI: 10.1016/j.ebcr.2016.09.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 09/05/2016] [Accepted: 09/07/2016] [Indexed: 11/19/2022]
Abstract
Extreme delta brush (EDB) is an EEG pattern unique to anti-NMDA encephalitis. It is correlated with seizures and status epilepticus in patients who have a prolonged course of illness. The etiology of the underlying association between EDB and seizures is not understood. We present a patient with anti-NMDA encephalitis who developed status epilepticus evolving from the high frequency activity of the extreme delta brush. This case demonstrates that EDB is not only a marker for a greater propensity for seizures but also directly implicated in seizure generation.
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Affiliation(s)
- Aline Herlopian
- Department of Neurology, Division of Clinical Neurophysiology, Massachusetts General Hospital, WACC, Fruit St., Boston, MA 02114, United States
- Corresponding author.
| | - Eric S Rosenthal
- Massachusetts General Hospital, Department of Neurology, Division of Clinical Neurophysiology, Division of Neurocritical Care and Emergency Neurology, WACC 739-L, 55 Fruit St., Boston, MA 02114, United States
| | - Catherine J Chu
- Department of Neurology, Divisions of Child Neurology and Neurophysiology, Massachusetts General Hospital for Children, Suite 340, 175 Cambridge St., Boston, MA 02114, United States
| | - Andrew J Cole
- Massachusetts General Hospital, WACC 739-L, Fruit St., Boston, MA 02114, United States
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin, Madison, 1685 Highland Avenue Madison, WI 53705-2281 United State
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Hotan GC, Struck AF, Bianchi MT, Eskandar EN, Cole AJ, Westover MB. Decision analysis of intracranial monitoring in non-lesional epilepsy. Seizure 2016; 40:59-70. [PMID: 27348062 DOI: 10.1016/j.seizure.2016.06.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 06/10/2016] [Accepted: 06/11/2016] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Up to one third of epilepsy patients develop pharmacoresistant seizures and many benefit from resective surgery. However, patients with non-lesional focal epilepsy often require intracranial monitoring to localize the seizure focus. Intracranial monitoring carries operative morbidity risk and does not always succeed in localizing the seizures, making the benefit of this approach less certain. We performed a decision analysis comparing three strategies for patients with non-lesional focal epilepsy: (1) intracranial monitoring, (2) vagal nerve stimulator (VNS) implantation and (3) medical management to determine which strategy maximizes the expected quality-adjusted life years (QALYs) for our base cases. METHOD We constructed two base cases using parameters reported in the medical literature: (1) a young, otherwise healthy patient and (2) an elderly, otherwise healthy patient. We constructed a decision tree comprising strategies for the treatment of non-lesional epilepsy and two clinical outcomes: seizure freedom and no seizure freedom. Sensitivity analyses of probabilities at each branch were guided by data from the medical literature to define decision thresholds across plausible parameter ranges. RESULTS Intracranial monitoring maximizes the expected QALYs for both base cases. The sensitivity analyses provide estimates of the values of key variables, such as the surgical risk or the chance of localizing the focus, at which intracranial monitoring is no longer favored. CONCLUSION Intracranial monitoring is favored over VNS and medical management in young and elderly patients over a wide, clinically-relevant range of pertinent model variables such as the chance of localizing the seizure focus and the surgical morbidity rate.
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Affiliation(s)
- G C Hotan
- Massachusetts Institute of Technology Department of Brain and Cognitive Sciences, Cambridge, MA, USA
| | - A F Struck
- Massachusetts General Hospital Department of Neurology, Boston, MA, USA.
| | - M T Bianchi
- Massachusetts General Hospital Department of Neurology, Boston, MA, USA
| | - E N Eskandar
- Massachusetts General Hospital Department of Neurosurgery, Boston, MA, USA
| | - A J Cole
- Massachusetts General Hospital Department of Neurology, Boston, MA, USA
| | - M B Westover
- Massachusetts General Hospital Department of Neurology, Boston, MA, USA
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Ibrahim N, Kusmirek J, Struck AF, Floberg JM, Perlman SB, Gallagher C, Hall LT. The sensitivity and specificity of F-DOPA PET in a movement disorder clinic. Am J Nucl Med Mol Imaging 2016; 6:102-109. [PMID: 27069770 PMCID: PMC4749509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 12/04/2015] [Indexed: 06/05/2023]
Abstract
Idiopathic Parkinson's disease (PD) is the second most common neurodegenerative disorder. Early PD may present a diagnostic challenge with broad differential diagnoses that are not associated with nigral degeneration or striatal dopamine deficiency. Therefore, the early clinical diagnosis alone may not be accurate and this reinforces the importance of functional imaging targeting the pathophysiology of the disease process. (18)F-DOPA L-6-[(18)F] fluoro-3,4-dihydroxyphenylalnine ((18)F-DOPA) is a positron emission tomography (PET) agent that measures the uptake of dopamine precursors for assessment of presynaptic dopaminergic integrity and has been shown to accurately reflect the monoaminergic disturbances in PD. In this study, we aim to illustrate our local experience to determine the accuracy of (18)F-DOPA PET for diagnosis of PD. We studied a total of 27 patients. A retrospective analysis was carried out for all patients that underwent (18)F-DOPA PET brain scan for motor symptoms suspicious for PD between 2001-2008. Both qualitative and semi-quantitative analyses of the scans were performed. The patient's medical records were then assessed for length of follow-up, response to levodopa, clinical course of illness, and laterality of symptoms at time of (18)F-DOPA PET. The eventual diagnosis by the referring neurologist, movement disorder specialist, was used as the reference standard for further analysis. Of the 28 scans, we found that one was a false negative, 20 were true positives, and 7 were true negatives. The resultant values are Sensitivity 95.4% (95% CI: 100%-75.3%), Specificity 100% (95% CI: 100%-59.0%), PPV 100% (95% CI 100%-80.7%), and NPV 87.5% (95% CI: 99.5%-50.5%).
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Affiliation(s)
- Nevein Ibrahim
- Department of Radiology, University of Wisconsin School of Medicine and Public HealthUSA
| | - Joanna Kusmirek
- Department of Radiology, University of Wisconsin School of Medicine and Public HealthUSA
| | - Aaron F Struck
- Department of Radiology, University of Wisconsin School of Medicine and Public HealthUSA
- Department of Neurology, William S. Middleton VA HospitalUSA
| | - John M Floberg
- Department of Medical Physics, University of WisconsinUSA
- Transitional Year Residency Program, Hennepin County Medical CenterUSA
| | - Scott B Perlman
- Department of Radiology, University of Wisconsin School of Medicine and Public HealthUSA
| | | | - Lance T Hall
- Department of Radiology, University of Wisconsin School of Medicine and Public HealthUSA
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Abstract
OBJECTIVE The purpose of this study was to develop a quantitative framework to estimate the likelihood of multifocal epilepsy based on the number of unifocal seizures observed in the epilepsy monitoring unit (EMU). METHODS Patient records from the EMU at Massachusetts General Hospital (MGH) from 2012 to 2014 were assessed for the presence of multifocal seizures as well the presence of multifocal interictal discharges and multifocal structural imaging abnormalities during the course of the EMU admission. Risk factors for multifocal seizures were assessed using sensitivity and specificity analysis. A Kaplan-Meier survival analysis was used to estimate the risk of multifocal epilepsy for a given number of consecutive seizures. To overcome the limits of the Kaplan-Meier analysis, a parametric survival function was fit to the EMU subjects with multifocal seizures and this was used to develop a Bayesian model to estimate the risk of multifocal seizures during an EMU admission. RESULTS Multifocal interictal discharges were a significant predictor of multifocal seizures within an EMU admission with a p < 0.01, albeit with only modest sensitivity 0.74 and specificity 0.69. Multifocal potentially epileptogenic lesions on MRI were not a significant predictor p = 0.44. Kaplan-Meier analysis was limited by wide confidence intervals secondary to significant patient dropout and concern for informative censoring. The Bayesian framework provided estimates for the number of unifocal seizures needed to predict absence of multifocal seizures. To achieve 90% confidence for the absence of multifocal seizure, three seizures are needed when the pretest probability for multifocal epilepsy is 20%, seven seizures for a pretest probability of 50%, and nine seizures for a pretest probability of 80%. SIGNIFICANCE These results provide a framework to assist clinicians in determining the utility of trying to capture a specific number of seizures in EMU evaluations of candidates for epilepsy surgery.
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Affiliation(s)
- Aaron F Struck
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A
| | - Andrew J Cole
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A
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Abstract
PURPOSE Neuroimaging is critical in deciding candidacy for epilepsy surgery. Currently imaging is primarily assessed qualitatively, which may affect patient selection and outcomes. METHOD The epilepsy surgery database at MGH was reviewed for temporal lobectomy patients from the last 10 years. Radiology reports for MRI and FDG-PET were compared to the epilepsy conference consensus. First, specific findings of ipsi/contra hippocampal atrophy and T2 signal changes were directly compared. Next the overall impression of presence of hippocampal sclerosis (HS) for MRI and temporal hypometabolism for PET was used for sensitivity/specificity analysis. To assess predictive power of imaging findings logistic regression was used. RESULTS 104 subjects were identified. 70% of subjects were ILAE class I at 1-year. Radiology reports and the conference consensus differed in 31% of FDG-PET studies and 41% of MRIs. For PET most disagreement (50%) stemmed for discrepancy regarding contralateral temporal hypometabolism. For MRI discrepancy in ipsilateral hippocampal atrophy/T2 signal accounted for 59% of disagreements. When overall impression of the image was used the overall reliability between groups was high with only MRI sensitivity to detect HS (0.75 radiology, 0.91 conference, p=0.02) was significantly different between groups. On logistic regression MRI was a significant predictor of HS, but still 36% of patients with normal MRI as read by both groups had HS on pathology. CONCLUSION Despite some difference in specific radiologic findings, overall accuracy for MRI and PET is similar in clinical practice between radiology and conference; nonetheless there are still cases of hippocampal pathology not detected by standard imaging methods.
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Affiliation(s)
- Aaron F Struck
- Massachusetts General Hospital, Harvard Medical School, Division of Epilepsy, Wang 735, 55 Fruit St. Boston, MA 02114, United States.
| | - Michael B Westover
- Massachusetts General Hospital, Harvard Medical School, Division of Epilepsy, Wang 735, 55 Fruit St. Boston, MA 02114, United States
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