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Shi H, Pattnaik AR, Aguila C, Lucas A, Sinha N, Prager B, Mojena M, Gallagher R, Parashos A, Bonilha L, Gleichgerrcht E, Davis KA, Litt B, Conrad EC. Utility of intracranial EEG networks depends on re-referencing and connectivity choice. Brain Commun 2024; 6:fcae165. [PMID: 38799618 PMCID: PMC11126314 DOI: 10.1093/braincomms/fcae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/02/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.
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Affiliation(s)
- Haoer Shi
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Akash Ranjan Pattnaik
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Carlos Aguila
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alfredo Lucas
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Prager
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marissa Mojena
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ryan Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexandra Parashos
- Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, GA 30325, USA
| | | | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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2
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Young JJ, Chan AHW, Jette N, Bender HA, Saad AE, Saez I, Panov F, Ghatan S, Yoo JY, Singh A, Fields MC, Marcuse LV, Mayberg HS. Elevated phase amplitude coupling as a depression biomarker in epilepsy. Epilepsy Behav 2024; 152:109659. [PMID: 38301454 PMCID: PMC10923038 DOI: 10.1016/j.yebeh.2024.109659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/28/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024]
Abstract
Depression is prevalent in epilepsy patients and their intracranial brain activity recordings can be used to determine the types of brain activity that are associated with comorbid depression. We performed case-control comparison of spectral power and phase amplitude coupling (PAC) in 34 invasively monitored drug resistant epilepsy patients' brain recordings. The values of spectral power and PAC for one-minute segments out of every hour in a patient's study were correlated with pre-operative assessment of depressive symptoms by Beck Depression Inventory-II (BDI). We identified an elevated PAC signal (theta-alpha-beta phase (5-25 Hz)/gamma frequency (80-100 Hz) band) that is present in high BDI scores but not low BDI scores adult epilepsy patients in brain regions implicated in primary depression, including anterior cingulate cortex, amygdala and orbitofrontal cortex. Our results showed the application of PAC as a network-specific, electrophysiologic biomarker candidate for comorbid depression and its potential as treatment target for neuromodulation.
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Affiliation(s)
- James J Young
- Departments of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Andy Ho Wing Chan
- Departments of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Nathalie Jette
- Departments of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Heidi A Bender
- Weill Cornell Medicine, Department of Neurological Surgery, 525 East 68th Street, New York, NY 10021, USA
| | - Adam E Saad
- Departments of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Ignacio Saez
- Departments of Neurocience, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
| | - Fedor Panov
- Departments of Neurosurgery, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Saadi Ghatan
- Departments of Neurosurgery, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Ji Yeoun Yoo
- Departments of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Anuradha Singh
- Departments of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Madeline C Fields
- Departments of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Lara V Marcuse
- Departments of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
| | - Helen S Mayberg
- Departments of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Departments of Neurosurgery, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.
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3
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Moukaddam N, Lamichhane B, Salas R, Goodman W, Sabharwal A. Modeling Suicidality with Multimodal Impulsivity Characterization in Participants with Mental Health Disorder. Behav Neurol 2023; 2023:8552180. [PMID: 37575401 PMCID: PMC10423091 DOI: 10.1155/2023/8552180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/28/2023] [Accepted: 07/29/2023] [Indexed: 08/15/2023] Open
Abstract
Introduction Suicide is one of the leading causes of death across different age groups. The persistence of suicidal ideation and the progression of suicidal ideations to action could be related to impulsivity, the tendency to act on urges with low temporal latency, and little forethought. Quantifying impulsivity could thus help suicidality estimation and risk assessments in ideation-to-action suicidality frameworks. Methods To model suicidality with impulsivity quantification, we obtained questionnaires, behavioral tests, heart rate variability (HRV), and resting state functional magnetic resonance imaging measurements from 34 participants with mood disorders. The participants were categorized into three suicidality groups based on their Mini-International Neuropsychiatric Interview: none, low, and moderate to severe. Results Questionnaire and HRV-based impulsivity measures were significantly different between the suicidality groups with higher subscales of impulsivity associated with higher suicidality. A multimodal system to characterize impulsivity objectively resulted in a classification accuracy of 96.77% in the three-class suicidality group prediction task. Conclusions This study elucidates the relative sensitivity of various impulsivity measures in differentiating participants with suicidality and demonstrates suicidality prediction with high accuracy using a multimodal objective impulsivity characterization in participants with mood disorders.
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Affiliation(s)
- Nidal Moukaddam
- Menninger Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA
| | - Bishal Lamichhane
- Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Ramiro Salas
- Menninger Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Wayne Goodman
- Menninger Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA
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Sonkusare S, Qiong D, Zhao Y, Liu W, Yang R, Mandali A, Manssuer L, Zhang C, Cao C, Sun B, Zhan S, Voon V. Frequency dependent emotion differentiation and directional coupling in amygdala, orbitofrontal and medial prefrontal cortex network with intracranial recordings. Mol Psychiatry 2022; 28:1636-1646. [PMID: 36460724 DOI: 10.1038/s41380-022-01883-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 11/04/2022] [Accepted: 11/10/2022] [Indexed: 12/04/2022]
Abstract
The amygdala, orbitofrontal cortex (OFC) and medial prefrontal cortex (mPFC) form a crucial part of the emotion circuit, yet their emotion induced responses and interactions have been poorly investigated with direct intracranial recordings. Such high-fidelity signals can uncover precise spectral dynamics and frequency differences in valence processing allowing novel insights on neuromodulation. Here, leveraging the unique spatio-temporal advantages of intracranial electroencephalography (iEEG) from a cohort of 35 patients with intractable epilepsy (with 71 contacts in amygdala, 31 in OFC and 43 in mPFC), we assessed the spectral dynamics and interactions between the amygdala, OFC and mPFC during an emotional picture viewing task. Task induced activity showed greater broadband gamma activity in the negative condition compared to positive condition in all the three regions. Similarly, beta activity was increased in the negative condition in the amygdala and OFC while decreased in mPFC. Furthermore, beta activity of amygdala showed significant negative association with valence ratings. Critically, model-based computational analyses revealed unidirectional connectivity from mPFC to the amygdala and bidirectional communication between OFC-amygdala and OFC-mPFC. Our findings provide direct neurophysiological evidence for a much-posited model of top-down influence of mPFC over amygdala and a bidirectional influence between OFC and the amygdala. Altogether, in a relatively large sample size with human intracranial neuronal recordings, we highlight valence-dependent spectral dynamics and dyadic coupling within the amygdala-mPFC-OFC network with implications for potential targeted neuromodulation in emotion processing.
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Affiliation(s)
- Saurabh Sonkusare
- Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Department of Psychiatry, University of Cambridge, Cambridge, UK.,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Ding Qiong
- Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Yijie Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei Liu
- Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruoqi Yang
- Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Alekhya Mandali
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Luis Manssuer
- Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Department of Psychiatry, University of Cambridge, Cambridge, UK.,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Chencheng Zhang
- Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunyan Cao
- Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shikun Zhan
- Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Valerie Voon
- Department of Psychiatry, University of Cambridge, Cambridge, UK. .,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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Ahmad HS, Singh S, Jiao K, Basil GW, Yang AI, Wang MY, Welch WC, Yoon JW. Data-driven phenotyping of preoperative functional decline patterns in patients undergoing lumbar decompression and lumbar fusion using smartphone accelerometry. Neurosurg Focus 2022; 52:E4. [DOI: 10.3171/2022.1.focus21732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/25/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE
Treatment of degenerative lumbar spine pathologies typically escalates to surgical intervention when symptoms begin to significantly impair patients’ functional status. Currently, surgeons rely on subjective patient assessments through patient-reported outcome measures to estimate the decline in patient wellness and quality of life. In this analysis, the authors sought to use smartphone-based accelerometry data to provide an objective, continuous measurement of physical activity that might aid in effective characterization of preoperative functional decline in different lumbar spine surgical indications.
METHODS
Up to 1 year of preoperative activity data (steps taken per day) from 14 patients who underwent lumbar decompression and 15 patients who underwent endoscopic lumbar fusion were retrospectively extracted from patient smartphones. A data-driven algorithm was constructed based on 10,585 unique activity data points to identify and characterize the functional decline of patients preceding surgical intervention. Algorithmic estimation of functional decline onset was compared with reported symptom onset in clinical documentation across patients who presented acutely (≤ 5 months of symptoms) or chronically (> 5 months of symptoms).
RESULTS
The newly created algorithm identified a statistically significant decrease in physical activity during measured periods of functional decline (p = 0.0020). To account for the distinct clinical presentation phenotypes of patients requiring lumbar decompression (71.4% acute and 28.6% chronic) and those requiring lumbar fusion (6.7% acute and 93.3% chronic), a variable threshold for detecting clinically significant reduced physical activity was implemented. The algorithm characterized functional decline (i.e., acute or chronic presentation) in patients who underwent lumbar decompression with 100% accuracy (sensitivity 100% and specificity 100%), while characterization of patients who underwent lumbar fusion was less effective (accuracy 26.7%, sensitivity 21.4%, and specificity 100%). Adopting a less-permissive detection threshold in patients who underwent lumbar fusion, which rendered the algorithm robust to minor fluctuations above or below the chronically decreased level of preoperative activity in most of those patients, increased functional decline classification accuracy of patients who underwent lumbar fusion to 66.7% (sensitivity 64.3% and specificity 100%).
CONCLUSIONS
In this study, the authors found that smartphone-based accelerometer data successfully characterized functional decline in patients with degenerative lumbar spine pathologies. The accuracy and sensitivity of functional decline detection were much lower when using non–surgery-specific detection thresholds, indicating the effectiveness of smartphone-based mobility analysis in characterizing the unique physical activity fingerprints of different lumbar surgical indications. The results of this study highlight the potential of using activity data to detect symptom onset and functional decline in patients, enabling earlier diagnosis and improved prognostication.
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Affiliation(s)
- Hasan S. Ahmad
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Shikha Singh
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Kenneth Jiao
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Gregory W. Basil
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida
| | - Andrew I. Yang
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Michael Y. Wang
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, Florida
| | - William C. Welch
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Jang W. Yoon
- Department of Neurosurgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; and
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6
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de Hemptinne C, Chen W, Racine CA, Seritan AL, Miller AM, Yaroshinsky MS, Wang SS, Gilron R, Little S, Bledsoe I, San Luciano M, Katz M, Chang EF, Dawes HE, Ostrem JL, Starr PA. Prefrontal Physiomarkers of Anxiety and Depression in Parkinson's Disease. Front Neurosci 2021; 15:748165. [PMID: 34744613 PMCID: PMC8568318 DOI: 10.3389/fnins.2021.748165] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/17/2021] [Indexed: 11/19/2022] Open
Abstract
Objective: Anxiety and depression are prominent non-motor symptoms of Parkinson’s disease (PD), but their pathophysiology remains unclear. We sought to understand their neurophysiological correlates from chronic invasive recordings of the prefrontal cortex (PFC). Methods: We studied four patients undergoing deep brain stimulation (DBS) for their motor signs, who had comorbid mild to moderate anxiety and/or depressive symptoms. In addition to their basal ganglia leads, we placed a permanent prefrontal subdural 4-contact lead. These electrodes were attached to an investigational pulse generator with the capability to sense and store field potential signals, as well as deliver therapeutic neurostimulation. At regular intervals over 3–5 months, participants paired brief invasive neural recordings with self-ratings of symptoms related to depression and anxiety. Results: Mean age was 61 ± 7 years, mean disease duration was 11 ± 8 years and a mean Unified Parkinson’s Disease Rating Scale, with part III (UPDRS-III) off medication score of 37 ± 13. Mean Beck Depression Inventory (BDI) score was 14 ± 5 and Beck Anxiety Index was 16.5 ± 5. Prefrontal cortex spectral power in the beta band correlated with patient self-ratings of symptoms of depression and anxiety, with r-values between 0.31 and 0.48. Mood scores showed negative correlation with beta spectral power in lateral locations, and positive correlation with beta spectral power in a mesial recording location, consistent with the dichotomous organization of reward networks in PFC. Interpretation: These findings suggest a physiological basis for anxiety and depression in PD, which may be useful in the development of neurostimulation paradigms for these non-motor disease features.
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Affiliation(s)
- Coralie de Hemptinne
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Witney Chen
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Caroline A Racine
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Andreea L Seritan
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Andrew M Miller
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Maria S Yaroshinsky
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Sarah S Wang
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Roee Gilron
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Simon Little
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Ian Bledsoe
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Marta San Luciano
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Maya Katz
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Heather E Dawes
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Jill L Ostrem
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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Scangos KW, Khambhati AN, Daly PM, Owen LW, Manning JR, Ambrose JB, Austin E, Dawes HE, Krystal AD, Chang EF. Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology. Front Hum Neurosci 2021; 15:746499. [PMID: 34744662 PMCID: PMC8566975 DOI: 10.3389/fnhum.2021.746499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/02/2021] [Indexed: 12/30/2022] Open
Abstract
Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.
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Affiliation(s)
- Katherine Wilson Scangos
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Ankit N. Khambhati
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Patrick M. Daly
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lucy W. Owen
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Jeremy R. Manning
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Josiah B. Ambrose
- Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
| | - Everett Austin
- Kaiser Permanente Redwood City Medical Center, Redwood City, CA, United States
| | - Heather E. Dawes
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Andrew D. Krystal
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Edward F. Chang
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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8
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Balzekas I, Sladky V, Nejedly P, Brinkmann BH, Crepeau D, Mivalt F, Gregg NM, Pal Attia T, Marks VS, Wheeler L, Riccelli TE, Staab JP, Lundstrom BN, Miller KJ, Van Gompel J, Kremen V, Croarkin PE, Worrell GA. Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies. Front Hum Neurosci 2021; 15:702605. [PMID: 34381344 PMCID: PMC8349989 DOI: 10.3389/fnhum.2021.702605] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/29/2021] [Indexed: 01/10/2023] Open
Abstract
Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.
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Affiliation(s)
- Irena Balzekas
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
- Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States
| | - Vladimir Sladky
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czechia
| | - Petr Nejedly
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czechia
| | - Benjamin H. Brinkmann
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Daniel Crepeau
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Filip Mivalt
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Czechia
| | - Nicholas M. Gregg
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Tal Pal Attia
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Victoria S. Marks
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
| | - Lydia Wheeler
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
| | - Tori E. Riccelli
- Mayo Clinic Alix School of Medicine, Rochester, MN, United States
| | - Jeffrey P. Staab
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
- Department of Otorhinolaryngology, Mayo Clinic, Rochester, MN, United States
| | - Brian Nils Lundstrom
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Kai J. Miller
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Jamie Van Gompel
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, United States
| | - Vaclav Kremen
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
| | - Paul E. Croarkin
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Gregory A. Worrell
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
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9
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Scangos KW, Makhoul GS, Sugrue LP, Chang EF, Krystal AD. State-dependent responses to intracranial brain stimulation in a patient with depression. Nat Med 2021; 27:229-231. [PMID: 33462446 DOI: 10.1038/s41591-020-01175-8] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 11/12/2020] [Indexed: 11/09/2022]
Abstract
Deep brain stimulation is a promising treatment for severe depression, but lack of efficacy in randomized trials raises questions regarding anatomical targeting. We implanted multi-site intracranial electrodes in a severely depressed patient and systematically assessed the acute response to focal electrical neuromodulation. We found an elaborate repertoire of distinctive emotional responses that were rapid in onset, reproducible, and context and state dependent. Results provide proof of concept for personalized, circuit-specific medicine in psychiatry.
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Affiliation(s)
- Katherine W Scangos
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA.
| | - Ghassan S Makhoul
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Leo P Sugrue
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew D Krystal
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
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10
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Bormann NL, Trapp NT, Narayanan NS, Boes AD. Developing Precision Invasive Neuromodulation for Psychiatry. J Neuropsychiatry Clin Neurosci 2021; 33:201-209. [PMID: 33985346 PMCID: PMC8576850 DOI: 10.1176/appi.neuropsych.20100268] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Psychiatric conditions are common and often disabling. Although great strides have been made in alleviating symptoms with pharmacotherapy and psychotherapeutic approaches, many patients continue to have severe disease burden despite the best therapies available. One of the pervasive challenges to improving treatment is that present diagnostic and therapeutic strategies lag behind our modern conceptualization of the pathophysiology of these disorders. Psychiatric symptoms manifest through activity in specific neural circuits; thus, therapies capable of modulating these circuits are attractive. The investigators reviewed recent advances that facilitate treating medically refractory psychiatric disorders with intracranial neuromodulation in a way that intervenes more directly with the underlying pathophysiology. Specifically, they reviewed the prospects for using intracranial multielectrode arrays to record brain activity with high spatiotemporal resolution and identify circuit-level electrophysiological correlates of symptoms. A causal relationship of circuit electrophysiology to symptoms could then be established by modulating the circuits to disrupt the symptoms. Personalized therapeutic neuromodulation strategies can then proceed in a rational manner with stimulation protocols informed by the underlying circuit-based pathophysiology of the most bothersome symptoms. This strategy would enhance current methods in neurotherapeutics by identifying individualized anatomical targets with symptom-specific precision, circumventing many of the limitations inherent in modern psychiatric nosology and treatment.
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Affiliation(s)
- Nicholas L. Bormann
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City
| | - Nicholas T. Trapp
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City,Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, Calif
| | | | - Aaron D. Boes
- Departments of Neurology, Psychiatry, and Pediatrics, University of Iowa Carver College of Medicine
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