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Doss DJ, Johnson GW, Makhoul GS, Rashingkar RV, Shless JS, Bibro CE, Paulo DL, Gummadavelli A, Ball TJ, Reddy SB, Naftel RP, Haas KF, Dawant BM, Constantinidis C, Williams Roberson S, Bick SK, Morgan VL, Englot DJ. Network signatures define consciousness state during focal seizures. Epilepsia 2024. [PMID: 39056406 DOI: 10.1111/epi.18074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 07/12/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024]
Abstract
OBJECTIVE Epilepsy is a common neurological disorder affecting 1% of the global population. Loss of consciousness in focal impaired awareness seizures (FIASs) and focal-to-bilateral tonic-clonic seizures (FBTCSs) can be devastating, but the mechanisms are not well understood. Although ictal activity and interictal connectivity changes have been noted, the network states of focal aware seizures (FASs), FIASs, and FBTCSs have not been thoroughly evaluated with network measures ictally. METHODS We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types. RESULTS We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures. SIGNIFICANCE This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy.
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Affiliation(s)
- Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, USA
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, USA
| | - Ghassan S Makhoul
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, USA
| | - Rohan V Rashingkar
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jared S Shless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Camden E Bibro
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Danika L Paulo
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Abhijeet Gummadavelli
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Tyler J Ball
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shilpa B Reddy
- Department of Pediatrics, Vanderbilt Children's Hospital, Nashville, Tennessee, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kevin F Haas
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Shawniqua Williams Roberson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sarah K Bick
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee, USA
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2
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Jin X, Yang X, Kong W, Zhu L, Tang J, Peng Y, Ding Y, Zhao Q. TSFAN: tensorized spatial-frequency attention network with domain adaptation for cross-session EEG-based biometric recognition. J Neural Eng 2024; 21:046005. [PMID: 38866001 DOI: 10.1088/1741-2552/ad5761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/12/2024] [Indexed: 06/14/2024]
Abstract
Objective.Electroencephalogram (EEG) signals are promising biometrics owning to their invisibility, adapting to the application scenarios with high-security requirements. However, It is challenging to explore EEG identity features without the interference of device and state differences of the subject across sessions. Existing methods treat training sessions as a single domain, affected by the different data distribution among sessions. Although most multi-source unsupervised domain adaptation (MUDA) methods bridge the domain gap between multiple source and target domains individually, relationships among the domain-invariant features of each distribution alignment are neglected.Approach.In this paper, we propose a MUDA method, Tensorized Spatial-Frequency Attention Network (TSFAN), to assist the performance of the target domain for EEG-based biometric recognition. Specifically, significant relationships of domain-invariant features are modeled via a tensorized attention mechanism. It jointly incorporates appropriate common spatial-frequency representations of pairwise source and target but also cross-source domains, without the effect of distribution discrepancy among source domains. Additionally, considering the curse of dimensionality, our TSFAN is approximately represented in Tucker format. Benefiting the low-rank Tucker Network, the TSFAN can scale linearly in the number of domains, providing us the great flexibility to extend TSFAN to the case associated with an arbitrary number of sessions.Main results.Extensive experiments on the representative benchmarks demonstrate the effectiveness of TSFAN in EEG-based biometric recognition, outperforming state-of-the-art approaches, as verified by cross-session validation.Significance.The proposed TSFAN aims to investigate the presence of consistent EEG identity features across sessions. It is achieved by utilizing a novel tensorized attention mechanism that collaborates intra-source transferable information with inter-source interactions, while remaining unaffected by domain shifts in multiple source domains. Furthermore, the electrode selection shows that EEG-based identity features across sessions are distributed across brain regions, and 20 electrodes based on 10-20 standard system are able to extract stable identity information.
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Affiliation(s)
- Xuanyu Jin
- School of Computer Science, Hangzhou Dianzi University, Hangzhou, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, People's Republic of China
| | - Xinyu Yang
- School of Computer Science, Hangzhou Dianzi University, Hangzhou, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, People's Republic of China
| | - Wanzeng Kong
- School of Computer Science, Hangzhou Dianzi University, Hangzhou, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, People's Republic of China
| | - Li Zhu
- School of Computer Science, Hangzhou Dianzi University, Hangzhou, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, People's Republic of China
| | - Jiajia Tang
- School of Computer Science, Hangzhou Dianzi University, Hangzhou, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, People's Republic of China
| | - Yong Peng
- School of Computer Science, Hangzhou Dianzi University, Hangzhou, People's Republic of China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, People's Republic of China
| | - Yu Ding
- Netease Fuxi AI Lab, NetEase, Hangzhou, People's Republic of China
| | - Qibin Zhao
- Tensor Learning Unit, Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
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Hannan S, Ho A, Frauscher B. Clinical Utility of Sleep Recordings During Presurgical Epilepsy Evaluation With Stereo-Electroencephalography: A Systematic Review. J Clin Neurophysiol 2024; 41:430-443. [PMID: 38935657 DOI: 10.1097/wnp.0000000000001057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
SUMMARY Although the role of sleep in modulating epileptic activity is well established, many epileptologists overlook the significance of considering sleep during presurgical epilepsy evaluations in cases of drug-resistant epilepsy. Here, we conducted a comprehensive literature review from January 2000 to May 2023 using the PubMed electronic database and compiled evidence to highlight the need to revise the current clinical approach. All articles were assessed for eligibility by two independent reviewers. Our aim was to shed light on the clinical value of incorporating sleep monitoring into presurgical evaluations with stereo-electroencephalography. We present the latest developments on the important bidirectional interactions between sleep and various forms of epileptic activity observed in stereo-electroencephalography recordings. Specifically, epileptic activity is modulated by different sleep stages, peaking in non-rapid eye movement sleep, while being suppressed in rapid eye movement sleep. However, this modulation can vary across different brain regions, underlining the need to account for sleep to accurately pinpoint the epileptogenic zone during presurgical assessments. Finally, we offer practical solutions, such as automated sleep scoring algorithms using stereo-electroencephalography data alone, to seamlessly integrate sleep monitoring into routine clinical practice. It is hoped that this review will provide clinicians with a readily accessible roadmap to the latest evidence concerning the clinical utility of sleep monitoring in the context of stereo-electroencephalography and aid the development of therapeutic and diagnostic strategies to improve patient surgical outcomes.
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Affiliation(s)
- Sana Hannan
- Department of Biomedical and Life Sciences, Lancaster University, Lancaster, United Kingdom
| | - Alyssa Ho
- Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center, Durham, North Carolina, U.S.A.; and
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4
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Jaber K, Avigdor T, Mansilla D, Ho A, Thomas J, Abdallah C, Chabardes S, Hall J, Minotti L, Kahane P, Grova C, Gotman J, Frauscher B. A spatial perturbation framework to validate implantation of the epileptogenic zone. Nat Commun 2024; 15:5253. [PMID: 38897997 PMCID: PMC11187199 DOI: 10.1038/s41467-024-49470-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.
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Affiliation(s)
- Kassem Jaber
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA
| | - Tamir Avigdor
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, QC, Canada
| | - Daniel Mansilla
- Neurophysiology Unit, Institute of Neurosurgery Dr. Asenjo, Santiago, Chile
| | - Alyssa Ho
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - John Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA
| | - Chifaou Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, QC, Canada
| | - Stephan Chabardes
- Grenoble Institute Neurosciences, Inserm, U1216, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
| | - Jeff Hall
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Lorella Minotti
- Grenoble Institute Neurosciences, Inserm, U1216, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
| | - Philippe Kahane
- Grenoble Institute Neurosciences, Inserm, U1216, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, QC, Canada
- Multimodal Functional Imaging Lab, School of Health, Department of Physics, Concordia University, Montréal, QC, Canada
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jean Gotman
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada.
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA.
- Department of Neurology, Duke University Medical Center, Durham, NC, USA.
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5
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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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6
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Proost R, Heremans E, Lagae L, Van Paesschen W, De Vos M, Jansen K. Automated sleep staging on reduced channels in children with epilepsy. Front Neurol 2024; 15:1390465. [PMID: 38798709 PMCID: PMC11116721 DOI: 10.3389/fneur.2024.1390465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/15/2024] [Indexed: 05/29/2024] Open
Abstract
Objectives This study aimed to validate a sleep staging algorithm using in-hospital video-electroencephalogram (EEG) in children without epilepsy, with well-controlled epilepsy (WCE), and with drug-resistant epilepsy (DRE). Methods Overnight video-EEG, along with electrooculogram (EOG) and chin electromyogram (EMG), was recorded in children between 4 and 18 years of age. Classical sleep staging was performed manually as a ground truth. An end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging (SeqSleepNet) was used to perform automated sleep staging using three channels: C4-A1, EOG, and chin EMG. Results In 176 children sleep stages were manually scored: 47 children without epilepsy, 74 with WCE, and 55 with DRE. The 5-class sleep staging accuracy of the automatic sleep staging algorithm was 84.7% for the children without epilepsy, 83.5% for those with WCE, and 80.8% for those with DRE (Kappa of 0.79, 0.77, and 0.73 respectively). Performance per sleep stage was assessed with an F1 score of 0.91 for wake, 0.50 for N1, 0.83 for N2, 0.84 for N3, and 0.86 for rapid eye movement (REM) sleep. Conclusion We concluded that the tested algorithm has a high accuracy in children without epilepsy and with WCE. Performance in children with DRE was acceptable, but significantly lower, which could be explained by a tendency of more time spent in N1, and by abundant interictal epileptiform discharges and intellectual disability leading to less recognizable sleep stages. REM sleep time, however, significantly affected in children with DRE, can be detected reliably by the algorithm.Clinical trial registration: ClinicalTrials.gov, identifier NCT04584385.
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Affiliation(s)
- Renee Proost
- Pediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Elisabeth Heremans
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Lieven Lagae
- Pediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Wim Van Paesschen
- Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Maarten De Vos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Katrien Jansen
- Pediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
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7
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Conrad EC, Lucas A, Ojemann WK, Aguila CA, Mojena M, LaRocque JJ, Pattnaik AR, Gallagher R, Greenblatt A, Tranquille A, Parashos A, Gleichgerrcht E, Bonilha L, Litt B, Sinha S, Ungar L, Davis KA. Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299907. [PMID: 38168158 PMCID: PMC10760281 DOI: 10.1101/2023.12.13.23299907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data is captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal (between-seizure) intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-center study for model development; two-center study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using interictal EEG. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. 47 patients (30 women; ages 20-69; 20 left-sided, 10 right-sided, and 17 bilateral seizure onsets) were analyzed for model development and internal validation. 19 patients (10 women; ages 23-73; 5 left-sided, 10 right-sided, 4 bilateral) were analyzed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome.
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Affiliation(s)
- Erin C. Conrad
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alfredo Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - William K.S. Ojemann
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Carlos A. Aguila
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marissa Mojena
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joshua J. LaRocque
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Akash R. Pattnaik
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ryan Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Greenblatt
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Ashley Tranquille
- 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
| | - Brian Litt
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Saurabh Sinha
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lyle Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A. Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
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8
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Ramantani G, Westover MB, Gliske S, Sarnthein J, Sarma S, Wang Y, Baud MO, Stacey WC, Conrad EC. Passive and active markers of cortical excitability in epilepsy. Epilepsia 2023; 64 Suppl 3:S25-S36. [PMID: 36897228 PMCID: PMC10512778 DOI: 10.1111/epi.17578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.
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Affiliation(s)
- Georgia Ramantani
- Department of Neuropediatrics and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Data Science, Massachusetts General Hospital McCance Center for Brain Health, Boston, Massachusetts, USA
- Research Affiliate Faculty, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Research Affiliate Faculty, Broad Institute, Cambridge, Massachusetts, USA
| | - Stephen Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sridevi Sarma
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, NeuroTec, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - William C Stacey
- Department of Neurology, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Division of Neurology, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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9
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Bernard C, Frauscher B, Gelinas J, Timofeev I. Sleep, oscillations, and epilepsy. Epilepsia 2023; 64 Suppl 3:S3-S12. [PMID: 37226640 PMCID: PMC10674035 DOI: 10.1111/epi.17664] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/27/2023] [Accepted: 05/23/2023] [Indexed: 05/26/2023]
Abstract
Sleep and wake are defined through physiological and behavioral criteria and can be typically separated into non-rapid eye movement (NREM) sleep stages N1, N2, and N3, rapid eye movement (REM) sleep, and wake. Sleep and wake states are not homogenous in time. Their properties vary during the night and day cycle. Given that brain activity changes as a function of NREM, REM, and wake during the night and day cycle, are seizures more likely to occur during NREM, REM, or wake at a specific time? More generally, what is the relationship between sleep-wake cycles and epilepsy? We will review specific examples from clinical data and results from experimental models, focusing on the diversity and heterogeneity of these relationships. We will use a top-down approach, starting with the general architecture of sleep, followed by oscillatory activities, and ending with ionic correlates selected for illustrative purposes, with respect to seizures and interictal spikes. The picture that emerges is that of complexity; sleep disruption and pathological epileptic activities emerge from reorganized circuits. That different circuit alterations can occur across patients and models may explain why sleep alterations and the timing of seizures during the sleep-wake cycle are patient-specific.
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Affiliation(s)
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jennifer Gelinas
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Igor Timofeev
- Faculté de Médecine, Département de Psychiatrie et de Neurosciences, Centre de Recherche CERVO, Université Laval, Québec, QC G1J2G3, Canada
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10
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Zelmann R, Paulk AC, Tian F, Balanza Villegas GA, Dezha Peralta J, Crocker B, Cosgrove GR, Richardson RM, Williams ZM, Dougherty DD, Purdon PL, Cash SS. Differential cortical network engagement during states of un/consciousness in humans. Neuron 2023; 111:3479-3495.e6. [PMID: 37659409 PMCID: PMC10843836 DOI: 10.1016/j.neuron.2023.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 06/13/2023] [Accepted: 08/08/2023] [Indexed: 09/04/2023]
Abstract
What happens in the human brain when we are unconscious? Despite substantial work, we are still unsure which brain regions are involved and how they are impacted when consciousness is disrupted. Using intracranial recordings and direct electrical stimulation, we mapped global, network, and regional involvement during wake vs. arousable unconsciousness (sleep) vs. non-arousable unconsciousness (propofol-induced general anesthesia). Information integration and complex processing we`re reduced, while variability increased in any type of unconscious state. These changes were more pronounced during anesthesia than sleep and involved different cortical engagement. During sleep, changes were mostly uniformly distributed across the brain, whereas during anesthesia, the prefrontal cortex was the most disrupted, suggesting that the lack of arousability during anesthesia results not from just altered overall physiology but from a disconnection between the prefrontal and other brain areas. These findings provide direct evidence for different neural dynamics during loss of consciousness compared with loss of arousability.
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Affiliation(s)
- Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA.
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Fangyun Tian
- Department of Anesthesia, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick L Purdon
- Department of Anesthesia, Massachusetts General Hospital, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
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11
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Hannan S, Thomas J, Jaber K, El Kosseifi C, Ho A, Abdallah C, Avigdor T, Gotman J, Frauscher B. The Differing Effects of Sleep on Ictal and Interictal Network Dynamics in Drug-Resistant Epilepsy. Ann Neurol 2023. [PMID: 37712215 DOI: 10.1002/ana.26796] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/14/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
OBJECTIVE Sleep has important influences on focal interictal epileptiform discharges (IEDs), and the rates and spatial extent of IEDs are increased in non-rapid eye movement (NREM) sleep. In contrast, the influence of sleep on seizures is less clear, and its effects on seizure topography are poorly documented. We evaluated the influences of NREM sleep on ictal spatiotemporal dynamics and contrasted these with interictal network dynamics. METHODS We included patients with drug-resistant focal epilepsy who underwent continuous intracranial electroencephalography (iEEG) with depth electrodes. Patients were selected if they had 1 to 3 seizures from each vigilance state, wakefulness and NREM sleep, within a 48-hour window, and under the same antiseizure medication. A 10-minute epoch of the interictal iEEG was selected per state, and IEDs were detected automatically. A total of 25 patients (13 women; aged 32.5 ± 7.1 years) were included. RESULTS The seizure onset pattern, duration, spatiotemporal propagation, and latency of ictal high-frequency activity did not differ significantly between wakefulness and NREM sleep (all p > 0.05). In contrast, IED rates and spatial distribution were increased in NREM compared with wakefulness (p < 0.001, Cliff's d = 0.48 and 0.49). The spatial overlap between vigilance states was higher for seizures (57.1 ± 40.1%) than IEDs (41.7 ± 46.2%; p = 0.001, Cliff's d = 0.51). INTERPRETATION In contrast to its effects on IEDs, NREM sleep does not affect ictal spatiotemporal dynamics. This suggests that once the brain surpasses the seizure threshold, it will follow the underlying epileptic network irrespective of the vigilance state. These findings offer valuable insights into neural network dynamics in epilepsy and have important clinical implications for localizing seizure foci. ANN NEUROL 2023.
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Affiliation(s)
- Sana Hannan
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
- Department of Biomedical and Life Sciences, Lancaster University, Lancaster, United Kingdom
| | - John Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Kassem Jaber
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Charbel El Kosseifi
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Alyssa Ho
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Chifaou Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Tamir Avigdor
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, USA
- Analytical Neurophysiology Lab, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
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12
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Lepage KQ, Jain S, Kvavilashvili A, Witcher M, Vijayan S. Unsupervised Multitaper Spectral Method for Identifying REM Sleep in Intracranial EEG Recordings Lacking EOG/EMG Data. Bioengineering (Basel) 2023; 10:1009. [PMID: 37760111 PMCID: PMC10525760 DOI: 10.3390/bioengineering10091009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 09/29/2023] Open
Abstract
A large number of human intracranial EEG (iEEG) recordings have been collected for clinical purposes, in institutions all over the world, but the vast majority of these are unaccompanied by EOG and EMG recordings which are required to separate Wake episodes from REM sleep using accepted methods. In order to make full use of this extremely valuable data, an accurate method of classifying sleep from iEEG recordings alone is required. Existing methods of sleep scoring using only iEEG recordings accurately classify all stages of sleep, with the exception that wake (W) and rapid-eye movement (REM) sleep are not well distinguished. A novel multitaper (Wake vs. REM) alpha-rhythm classifier is developed by generalizing K-means clustering for use with multitaper spectral eigencoefficients. The performance of this unsupervised method is assessed on eight subjects exhibiting normal sleep architecture in a hold-out analysis and is compared against a classical power detector. The proposed multitaper classifier correctly identifies 36±6 min of REM in one night of recorded sleep, while incorrectly labeling less than 10% of all labeled 30 s epochs for all but one subject (human rater reliability is estimated to be near 80%), and outperforms the equivalent statistical-power classical test. Hold-out analysis indicates that when using one night's worth of data, an accurate generalization of the method on new data is likely. For the purpose of studying sleep, the introduced multitaper alpha-rhythm classifier further paves the way to making available a large quantity of otherwise unusable IEEG data.
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Affiliation(s)
- Kyle Q. Lepage
- School of Neuroscience, Sandy Hall, Virginia Tech, 210 Drillfield Drive, Blacksburg, VA 24060, USA; (A.K.); (S.V.)
| | - Sparsh Jain
- Department of Biomedical Engineering and Mechanics, Virginia Tech, 325 Stanger St., Blacksburg, VA 24061, USA;
| | - Andrew Kvavilashvili
- School of Neuroscience, Sandy Hall, Virginia Tech, 210 Drillfield Drive, Blacksburg, VA 24060, USA; (A.K.); (S.V.)
| | - Mark Witcher
- Section of Neurosurgery, Carilion Clinic, Carilion Roanoke Memorial Hospital, 1906 Belleview Ave SE, Roanoke, VA 24014, USA;
| | - Sujith Vijayan
- School of Neuroscience, Sandy Hall, Virginia Tech, 210 Drillfield Drive, Blacksburg, VA 24060, USA; (A.K.); (S.V.)
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13
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Pattnaik AR, Ghosn NJ, Ong IZ, Revell AY, Ojemann WKS, Scheid BH, Georgostathi G, Bernabei JM, Conrad EC, Sinha SR, Davis KA, Sinha N, Litt B. The seizure severity score: a quantitative tool for comparing seizures and their response to therapy. J Neural Eng 2023; 20:10.1088/1741-2552/aceca1. [PMID: 37531949 PMCID: PMC11250994 DOI: 10.1088/1741-2552/aceca1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/01/2023] [Indexed: 08/04/2023]
Abstract
Objective.Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.Approach.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.Main results.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.
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Affiliation(s)
- Akash R Pattnaik
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Nina J Ghosn
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Andrew Y Revell
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - William K S Ojemann
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Brittany H Scheid
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Georgia Georgostathi
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Saurabh R Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- These authors contributed equally to this work
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- These authors contributed equally to this work
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14
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Peter-Derex L, von Ellenrieder N, van Rosmalen F, Hall J, Dubeau F, Gotman J, Frauscher B. Regional variability in intracerebral properties of NREM to REM sleep transitions in humans. Proc Natl Acad Sci U S A 2023; 120:e2300387120. [PMID: 37339200 PMCID: PMC10293806 DOI: 10.1073/pnas.2300387120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/12/2023] [Indexed: 06/22/2023] Open
Abstract
Transitions between wake and sleep states show a progressive pattern underpinned by local sleep regulation. In contrast, little evidence is available on non-rapid eye movement (NREM) to rapid eye movement (REM) sleep boundaries, considered as mainly reflecting subcortical regulation. Using polysomnography (PSG) combined with stereoelectroencephalography (SEEG) in humans undergoing epilepsy presurgical evaluation, we explored the dynamics of NREM-to-REM transitions. PSG was used to visually score transitions and identify REM sleep features. SEEG-based local transitions were determined automatically with a machine learning algorithm using features validated for automatic intra-cranial sleep scoring (10.5281/zenodo.7410501). We analyzed 2988 channel-transitions from 29 patients. The average transition time from all intracerebral channels to the first visually marked REM sleep epoch was 8 s ± 1 min 58 s, with a great heterogeneity between brain areas. Transitions were observed first in the lateral occipital cortex, preceding scalp transition by 1 min 57 s ± 2 min 14 s (d = -0.83), and close to the first sawtooth wave marker. Regions with late transitions were the inferior frontal and orbital gyri (1 min 1 s ± 2 min 1 s, d = 0.43, and 1 min 1 s ± 2 min 5 s, d = 0.43, after scalp transition). Intracranial transitions were earlier than scalp transitions as the night advanced (last sleep cycle, d = -0.81). We show a reproducible gradual pattern of REM sleep initiation, suggesting the involvement of cortical mechanisms of regulation. This provides clues for understanding oneiric experiences occurring at the NREM/REM boundary.
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Affiliation(s)
- Laure Peter-Derex
- Center for Sleep Medicine and Respiratory Diseases, Croix-Rousse Hospital, University Hospital of Lyon, Lyon 1 University, 69004Lyon, France
- Lyon Neuroscience Research Center, CNRS UMR5292/INSERM U1028, Lyon69000, France
| | - Nicolás von Ellenrieder
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QCH3A 2B4, Canada
| | - Frank van Rosmalen
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QCH3A 2B4, Canada
| | - Jeffery Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QCH3A 2B4, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QCH3A 2B4, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QCH3A 2B4, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QCH3A 2B4, Canada
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QCH3A 2B4, Canada
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15
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Huang Y, Wang JB, Parker JJ, Shivacharan R, Lal RA, Halpern CH. Spectro-spatial features in distributed human intracranial activity proactively encode peripheral metabolic activity. Nat Commun 2023; 14:2729. [PMID: 37169738 PMCID: PMC10174612 DOI: 10.1038/s41467-023-38253-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 04/17/2023] [Indexed: 05/13/2023] Open
Abstract
Mounting evidence demonstrates that the central nervous system (CNS) orchestrates glucose homeostasis by sensing glucose and modulating peripheral metabolism. Glucose responsive neuronal populations have been identified in the hypothalamus and several corticolimbic regions. However, how these CNS gluco-regulatory regions modulate peripheral glucose levels is not well understood. To better understand this process, we simultaneously measured interstitial glucose concentrations and local field potentials in 3 human subjects from cortical and subcortical regions, including the hypothalamus in one subject. Correlations between high frequency activity (HFA, 70-170 Hz) and peripheral glucose levels are found across multiple brain regions, notably in the hypothalamus, with correlation magnitude modulated by sleep-wake cycles, circadian coupling, and hypothalamic connectivity. Correlations are further present between non-circadian (ultradian) HFA and glucose levels which are higher during awake periods. Spectro-spatial features of neural activity enable decoding of peripheral glucose levels both in the present and up to hours in the future. Our findings demonstrate proactive encoding of homeostatic glucose dynamics by the CNS.
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Affiliation(s)
- Yuhao Huang
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Jeffrey B Wang
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
- Medical Scientist Training Program, Stanford School of Medicine, Stanford, CA, 94305, USA
| | - Jonathon J Parker
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Rajat Shivacharan
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Rayhan A Lal
- Department of Medicine (Endocrinology), Stanford University Medical Center, Stanford, CA, 94305, USA.
- Department of Pediatrics (Endocrinology), Stanford University Medical Center, Stanford, CA, 94305, USA.
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA.
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16
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Müller PM, Meisel C. Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load. PLoS Comput Biol 2023; 19:e1010919. [PMID: 36867652 PMCID: PMC10027224 DOI: 10.1371/journal.pcbi.1010919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 03/20/2023] [Accepted: 02/03/2023] [Indexed: 03/04/2023] Open
Abstract
The ability of neural circuits to integrate information over time and across different cortical areas is believed an essential ingredient for information processing in the brain. Temporal and spatial correlations in cortex dynamics have independently been shown to capture these integration properties in task-dependent ways. A fundamental question remains if temporal and spatial integration properties are linked and what internal and external factors shape these correlations. Previous research on spatio-temporal correlations has been limited in duration and coverage, thus providing only an incomplete picture of their interdependence and variability. Here, we use long-term invasive EEG data to comprehensively map temporal and spatial correlations according to cortical topography, vigilance state and drug dependence over extended periods of time. We show that temporal and spatial correlations in cortical networks are intimately linked, decline under antiepileptic drug action, and break down during slow-wave sleep. Further, we report temporal correlations in human electrophysiology signals to increase with the functional hierarchy in cortex. Systematic investigation of a neural network model suggests that these dynamical features may arise when dynamics are poised near a critical point. Our results provide mechanistic and functional links between specific measurable changes in the network dynamics relevant for characterizing the brain's changing information processing capabilities.
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Affiliation(s)
- Paul Manuel Müller
- Computational Neurology Lab, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Meisel
- Computational Neurology Lab, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Center for Stroke Research Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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17
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Azeem A, von Ellenrieder N, Royer J, Frauscher B, Bernhardt B, Gotman J. Integration of white matter architecture to stereo-EEG better describes epileptic spike propagation. Clin Neurophysiol 2023; 146:135-146. [PMID: 36379837 DOI: 10.1016/j.clinph.2022.10.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/12/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Stereo-electroencephalography (SEEG)-derived epilepsy networks are used to better understand a patient's epilepsy; however, a unimodal approach provides an incomplete picture. We combine tractography and SEEG to determine the relationship between spike propagation and the white matter architecture and to improve our understanding of spike propagation mechanisms. METHODS Probablistic tractography from diffusion imaging (dMRI) of matched subjects from the Human Connectome Project (HCP) was combined with patient-specific SEEG-derived spike propagation networks. Two regions-of-interest (ROIs) with a significant spike propagation relationship constituted a Propagation Pair. RESULTS In 56 of 59 patients, Propagation Pairs were more often tract-connected as compared to all ROI pairs (p < 0.01; d = -1.91). The degree of spike propagation between tract-connected ROIs was greater (39 ± 21%) compared to tract-unconnected ROIs (31 ± 18%; p < 0.0001). Within the same network, ROIs receiving propagation earlier were more often tract-connected to the source (59.7%) as compared to late receivers (25.4%; p < 0.0001). CONCLUSIONS Brain regions involved in spike propagation are more likely to be connected by white matter tracts. Between nodes, presence of tracts suggests a direct course of propagation, whereas the absence of tracts suggests an indirect course of propagation. SIGNIFICANCE We demonstrate a logical and consistent relationship between spike propagation and the white matter architecture.
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Affiliation(s)
- Abdullah Azeem
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Nicolás von Ellenrieder
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jessica Royer
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Birgit Frauscher
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; Department of Neurology & Neurosurgery, Montreal Neurological Hospital, Montréal, QC, Canada
| | - Boris Bernhardt
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jean Gotman
- Department of Neurology & Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
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