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Chang X, Hao P, Zhang S, Dang Y, Liu A, Zheng N, Dong Z, Zhao H. Multi-scale analysis of acupuncture mechanisms for motor and sensory cortex activity based on SEEG data. Cereb Cortex 2024; 34:bhae127. [PMID: 38652551 DOI: 10.1093/cercor/bhae127] [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: 01/08/2024] [Revised: 03/08/2024] [Accepted: 03/09/2024] [Indexed: 04/25/2024] Open
Abstract
Acupuncture, a traditional Chinese therapy, is gaining attention for its impact on the brain. While existing electroencephalogram and functional magnetic resonance image research has made significant contributions, this paper utilizes stereo-electroencephalography data for a comprehensive exploration of neurophysiological effects. Employing a multi-scale approach, channel-level analysis reveals notable $\delta $-band activity changes during acupuncture. At the brain region level, acupuncture modulated connectivity between the paracentral lobule and the precentral gyrus. Whole-brain analysis indicates acupuncture's influence on network organization, and enhancing $E_{glob}$ and increased interaction between the motor and sensory cortex. Brain functional reorganization is an important basis for functional recovery or compensation after central nervous system injury. The use of acupuncture to stimulate peripheral nerve trunks, muscle motor points, acupoints, etc., in clinical practice may contribute to the reorganization of brain function. This multi-scale perspective provides diverse insights into acupuncture's effects. Remarkably, this paper pioneers the introduction of stereo-electroencephalography data, advancing our understanding of acupuncture's mechanisms and potential therapeutic benefits in clinical settings.
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Affiliation(s)
- Xiaoyu Chang
- School of Comeputer and Artificial Intelligence, Beijing Technology and Business University, Beijing, No. 11/33, Fucheng Road, Haidian District, 100048 Beijing, China
| | - Pengliang Hao
- Central Medical Branch of PLA General Hospital, Chinese PLA General Hospital, 21 Andeli North Street, Dongcheng District, 100120 Beijing, China
| | - Shuhua Zhang
- Department of Neurology, International Headache Centre, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, 100853 Beijing, China
| | - Yuanyuan Dang
- Department of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, 100853 Beijing, China
| | - Aijun Liu
- Department of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, 100853 Beijing, China
| | - Nan Zheng
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, 100190 Beijing, China
| | - Zhao Dong
- Department of Neurology, International Headache Centre, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, 100853 Beijing, China
| | - Hulin Zhao
- Department of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, 100853 Beijing, China
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Stergiadis C, Kazis D, Klados MA. Epileptic tissue localization using graph-based networks in the high frequency oscillation range of intracranial electroencephalography. Seizure 2024; 117:28-35. [PMID: 38308906 DOI: 10.1016/j.seizure.2024.01.015] [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: 11/20/2023] [Revised: 01/08/2024] [Accepted: 01/24/2024] [Indexed: 02/05/2024] Open
Abstract
PURPOSE High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding functional networks using graph theory, and we assess their predictive value for automatic electrode classification in a cohort of 20 drug resistant patients. METHODS Coherence-based connectivity analysis was performed on the iEEG recordings, and six different local graph measures were computed in both sub-bands of the HFO frequency range (80-250 Hz and 250-500 Hz). Correlation analysis was implemented between the local graph measures and the ripple and fast ripple rates. Finally, the WEKA software was employed for training and testing different predictive models on the aforementioned local graph measures. RESULTS The ripple rate was significantly correlated with five out of six local graph measures in the functional network. For fast ripples, their rate was also significantly (but negatively) correlated with most of the local metrics. The results from WEKA showed that the Logistic Regression algorithm was able to classify highly HFO-contaminated electrodes with an accuracy of 82.5 % for ripples and 75.4 % for fast ripples. CONCLUSION Functional connectivity networks in the HFO band could represent an alternative to the direct use of distinct HFO events, while also providing important insights about hub epileptic areas that can represent possible surgical targets. Automatic electrode classification through FC-based classifiers can help bypass the burden of manual HFO annotation, providing at the same time similar amount of information about the epileptic tissue.
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Affiliation(s)
- Christos Stergiadis
- Department of Electronic Engineering, University of York, York, YO10 5DD, UK
| | - Dimitrios Kazis
- 3rd Neurological Department, Aristotle University of Thessaloniki Faculty of Health Sciences, Exohi, 57010 Thessaloniki, Greece
| | - Manousos A Klados
- Department of Psychology, University of York Europe Campus, CITY College 24, Proxenou Koromila Street, 546 22 Thessaloniki, Greece; Neuroscience Research Center (NEUREC), University of York Europe Campus, City College, Thessaloniki, Greece.
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Lu D, Wang Y, Yang Y, Zhang H, Fan X, Chen S, Wei P, Shan Y, Zhao G. Thyroid function and epilepsy: a two-sample Mendelian randomization study. Front Hum Neurosci 2024; 17:1295749. [PMID: 38298204 PMCID: PMC10827972 DOI: 10.3389/fnhum.2023.1295749] [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: 09/17/2023] [Accepted: 12/27/2023] [Indexed: 02/02/2024] Open
Abstract
Background Thyroid hormones (THs) play a crucial role in regulating various biological processes, particularly the normal development and functioning of the central nervous system (CNS). Epilepsy is a prevalent neurological disorder with multiple etiologies. Further in-depth research on the role of thyroid hormones in epilepsy is warranted. Methods Genome-wide association study (GWAS) data for thyroid function and epilepsy were obtained from the ThyroidOmics Consortium and the International League Against Epilepsy (ILAE) Consortium cohort, respectively. A total of five indicators of thyroid function and ten types of epilepsy were included in the analysis. Two-sample Mendelian randomization (MR) analyses were conducted to investigate potential causal relations between thyroid functions and various epilepsies. Multiple testing correction was performed using Bonferroni correction. Heterogeneity was calculated with the Cochran's Q statistic test. Horizontal pleiotropy was evaluated by the MR-Egger regression intercept. The sensitivity was also examined by leave-one-out strategy. Results The findings indicated the absence of any causal relationship between abnormalities in thyroid hormone and various types of epilepsy. The study analyzed the odds ratio (OR) between thyroid hormones and various types of epilepsy in five scenarios, including free thyroxine (FT4) on focal epilepsy with hippocampal sclerosis (IVW, OR = 0.9838, p = 0.02223), hyperthyroidism on juvenile absence epilepsy (IVW, OR = 0.9952, p = 0.03777), hypothyroidism on focal epilepsy with hippocampal sclerosis (IVW, OR = 1.0075, p = 0.01951), autoimmune thyroid diseases (AITDs) on generalized epilepsy in all documented cases (weighted mode, OR = 1.0846, p = 0.0346) and on childhood absence epilepsy (IVW, OR = 1.0050, p = 0.04555). After Bonferroni correction, none of the above results showed statistically significant differences. Conclusion This study indicates that there is no causal relationship between thyroid-related disorders and various types of epilepsy. Future research should aim to avoid potential confounding factors that might impact the study.
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Affiliation(s)
- Di Lu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy, Capital Medical University, Beijing, China
| | - Yunming Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy, Capital Medical University, Beijing, China
| | - Yanfeng Yang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy, Capital Medical University, Beijing, China
| | - Huaqiang Zhang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy, Capital Medical University, Beijing, China
| | - Xiaotong Fan
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy, Capital Medical University, Beijing, China
| | - Sichang Chen
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy, Capital Medical University, Beijing, China
| | - Penghu Wei
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy, Capital Medical University, Beijing, China
- Beijing Municipal Geriatric Medical Research Center, Beijing, China
| | - Yongzhi Shan
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy, Capital Medical University, Beijing, China
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy, Capital Medical University, Beijing, China
- Beijing Municipal Geriatric Medical Research Center, Beijing, China
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Doss DJ, Johnson GW, Englot DJ. Imaging and Stereotactic Electroencephalography Functional Networks to Guide Epilepsy Surgery. Neurosurg Clin N Am 2024; 35:61-72. [PMID: 38000842 PMCID: PMC10676462 DOI: 10.1016/j.nec.2023.09.001] [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] [Indexed: 11/26/2023]
Abstract
Epilepsy surgery is a potentially curative treatment of drug-resistant epilepsy that has remained underutilized both due to inadequate referrals and incomplete localization hypotheses. The complexity of patients evaluated for epilepsy surgery has increased, thus new approaches are necessary to treat these patients. The paradigm of epilepsy surgery has evolved to match this challenge, now considering the entire seizure network with the goal of disrupting it through resection, ablation, neuromodulation, or a combination. The network paradigm has the potential to aid in identification of the seizure network as well as treatment selection.
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Affiliation(s)
- Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University, PMB 351631, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue South, Medical Center North AA-1105, Nashville, TN 37232, USA; Vanderbilt Institute for Surgery and Engineering (VISE), 1161 21st Avenue South, MCN S2323, Nashville, TN 37232, USA
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, PMB 351631, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue South, Medical Center North AA-1105, Nashville, TN 37232, USA; Vanderbilt Institute for Surgery and Engineering (VISE), 1161 21st Avenue South, MCN S2323, Nashville, TN 37232, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, PMB 351631, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Vanderbilt University Institute of Imaging Science (VUIIS), 1161 21st Avenue South, Medical Center North AA-1105, Nashville, TN 37232, USA; Vanderbilt Institute for Surgery and Engineering (VISE), 1161 21st Avenue South, MCN S2323, Nashville, TN 37232, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, 1161 21st Avenue South, T4224 Medical Center North, Nashville, TN 37232, USA; Department of Electrical and Computer Engineering, Vanderbilt University, PMB 351824, 2301 Vanderbilt Place, Nashville, TN 37235, USA; Department of Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Avenue South, Nashville, TN 37232, USA.
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Widdess-Walsh P. Resting But Not Idle: Insights Into Epilepsy Network Suppression From Intracranial EEG. Epilepsy Curr 2024; 24:25-27. [PMID: 38327528 PMCID: PMC10846507 DOI: 10.1177/15357597231213247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
The Interictal Suppression Hypothesis in Focal Epilepsy: Network-Level Supporting Evidence Johnson GW, Doss DJ, Morgan VL, Paulo DL, Cai LY, Shless JS, Negi AS, Gummadavelli A, Kang H, Reddy SB, Naftel RP, Bick SK, Williams Roberson S, Dawant BM, Wallace MT, Englot DJ. Brain . 2023;146(7):2828-2845. doi:10.1093/brain/awad016 Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure–function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P -value = 3.13 × 10−13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P -value = 2.5 × 10−3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P -value of 2.6 × 10−12). Structure–function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P -value of 9.76 × 10−21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.
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Novitskaya Y, Dümpelmann M, Schulze-Bonhage A. Physiological and pathological neuronal connectivity in the living human brain based on intracranial EEG signals: the current state of research. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1297345. [PMID: 38107334 PMCID: PMC10723837 DOI: 10.3389/fnetp.2023.1297345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/17/2023] [Indexed: 12/19/2023]
Abstract
Over the past decades, studies of human brain networks have received growing attention as the assessment and modelling of connectivity in the brain is a topic of high impact with potential application in the understanding of human brain organization under both physiological as well as various pathological conditions. Under specific diagnostic settings, human neuronal signal can be obtained from intracranial EEG (iEEG) recording in epilepsy patients that allows gaining insight into the functional organisation of living human brain. There are two approaches to assess brain connectivity in the iEEG-based signal: evaluation of spontaneous neuronal oscillations during ongoing physiological and pathological brain activity, and analysis of the electrophysiological cortico-cortical neuronal responses, evoked by single pulse electrical stimulation (SPES). Both methods have their own advantages and limitations. The paper outlines available methodological approaches and provides an overview of current findings in studies of physiological and pathological human brain networks, based on intracranial EEG recordings.
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Affiliation(s)
- Yulia Novitskaya
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for Basics in NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. EBioMedicine 2023; 97:104848. [PMID: 37898096 PMCID: PMC10630610 DOI: 10.1016/j.ebiom.2023.104848] [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: 06/15/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. METHODS We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. FINDINGS Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p = 0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. INTERPRETATION Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. FUNDING This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia; Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Division of Neurology, Department of Medicine, Queen's University, Kingston, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. ARXIV 2023:arXiv:2304.03192v3. [PMID: 37064531 PMCID: PMC10104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. Methods We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. Findings Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p=0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. Interpretation Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. Funding This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J. Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H. Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A. Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W. McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B. Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
- Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C. Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Division of Neurology, Department of Medicine, Queen’s University, Kingston, Canada
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N. Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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Sinha N, Duncan JS, Diehl B, Chowdhury FA, de Tisi J, Miserocchi A, McEvoy AW, Davis KA, Vos SB, Winston GP, Wang Y, Taylor PN. Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery. Neurology 2023; 101:e1293-e1306. [PMID: 37652703 PMCID: PMC10558161 DOI: 10.1212/wnl.0000000000207661] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 06/02/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Surgery is an effective treatment for drug-resistant epilepsy, which modifies the brain's structure and networks to regulate seizure activity. Our objective was to examine the relationship between brain structure and function to determine the extent to which this relationship affects the success of the surgery in controlling seizures. We hypothesized that a stronger association between brain structure and function would lead to improved seizure control after surgery. METHODS We constructed functional and structural brain networks in patients with drug-resistant focal epilepsy by using presurgery functional data from intracranial EEG (iEEG) recordings, presurgery and postsurgery structural data from T1-weighted MRI, and presurgery diffusion-weighted MRI. We quantified the relationship (coupling) between structural and functional connectivity by using the Spearman rank correlation and analyzed this structure-function coupling at 2 spatial scales: (1) global iEEG network level and (2) individual iEEG electrode contacts using virtual surgeries. We retrospectively predicted postoperative seizure freedom by incorporating the structure-function connectivity coupling metrics and routine clinical variables into a cross-validated predictive model. RESULTS We conducted a retrospective analysis on data from 39 patients who met our inclusion criteria. Brain areas implanted with iEEG electrodes had stronger structure-function coupling in seizure-free patients compared with those with seizure recurrence (p = 0.002, d = 0.76, area under the receiver operating characteristic curve [AUC] = 0.78 [95% CI 0.62-0.93]). Virtual surgeries on brain areas that resulted in stronger structure-function coupling of the remaining network were associated with seizure-free outcomes (p = 0.007, d = 0.96, AUC = 0.73 [95% CI 0.58-0.89]). The combination of global and local structure-function coupling measures accurately predicted seizure outcomes with a cross-validated AUC of 0.81 (95% CI 0.67-0.94). These measures were complementary to other clinical variables and, when included for prediction, resulted in a cross-validated AUC of 0.91 (95% CI 0.82-1.0), accuracy of 92%, sensitivity of 93%, and specificity of 91%. DISCUSSION Our study showed that the strength of structure-function connectivity coupling may play a crucial role in determining the success of epilepsy surgery. By quantitatively incorporating structure-function coupling measures and standard-of-care clinical variables into presurgical evaluations, we may be able to better localize epileptogenic tissue and select patients for epilepsy surgery. CLASSIFICATION OF EVIDENCE This is a Class IV retrospective case series showing that structure-function mapping may help determine the outcome from surgical resection for treatment-resistant focal epilepsy.
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Affiliation(s)
- Nishant Sinha
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada.
| | - John S Duncan
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Beate Diehl
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Fahmida A Chowdhury
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Jane de Tisi
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Anna Miserocchi
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Andrew William McEvoy
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Kathryn A Davis
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Sjoerd B Vos
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Gavin P Winston
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Yujiang Wang
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Peter Neal Taylor
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
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10
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Johnson GW, Doss DJ, Morgan VL, Paulo DL, Cai LY, Shless JS, Negi AS, Gummadavelli A, Kang H, Reddy SB, Naftel RP, Bick SK, Williams Roberson S, Dawant BM, Wallace MT, Englot DJ. The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence. Brain 2023; 146:2828-2845. [PMID: 36722219 PMCID: PMC10316780 DOI: 10.1093/brain/awad016] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/24/2022] [Accepted: 01/08/2023] [Indexed: 02/02/2023] Open
Abstract
Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.
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Affiliation(s)
- Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Danika L Paulo
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Jared S Shless
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Aarushi S Negi
- Department of Neuroscience, Vanderbilt University, Nashville, TN 37232, USA
| | - Abhijeet Gummadavelli
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA
| | - Shilpa B Reddy
- Department of Pediatrics, Vanderbilt Children’s Hospital, Nashville, TN 37232, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sarah K Bick
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Mark T Wallace
- Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychology, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
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11
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Rijal S, Corona L, Perry MS, Tamilia E, Madsen JR, Stone SSD, Bolton J, Pearl PL, Papadelis C. Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy. Sci Rep 2023; 13:9622. [PMID: 37316544 PMCID: PMC10267141 DOI: 10.1038/s41598-023-36551-0] [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: 10/05/2022] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes' nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II-IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47-100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.
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Affiliation(s)
- Sakar Rijal
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - Ludovica Corona
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA.
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA.
- School of Medicine, Texas Christian University, Fort Worth, TX, 76129, USA.
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12
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Corona L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Stufflebeam SM, Pearl PL, Papadelis C. Non-invasive mapping of epileptogenic networks predicts surgical outcome. Brain 2023; 146:1916-1931. [PMID: 36789500 PMCID: PMC10151194 DOI: 10.1093/brain/awac477] [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: 06/15/2022] [Revised: 11/03/2022] [Accepted: 11/30/2022] [Indexed: 02/16/2023] Open
Abstract
Epilepsy is increasingly considered a disorder of brain networks. Studying these networks with functional connectivity can help identify hubs that facilitate the spread of epileptiform activity. Surgical resection of these hubs may lead patients who suffer from drug-resistant epilepsy to seizure freedom. Here, we aim to map non-invasively epileptogenic networks, through the virtual implantation of sensors estimated with electric and magnetic source imaging, in patients with drug-resistant epilepsy. We hypothesize that highly connected hubs identified non-invasively with source imaging can predict the epileptogenic zone and the surgical outcome better than spikes localized with conventional source localization methods (dipoles). We retrospectively analysed simultaneous high-density electroencephalography (EEG) and magnetoencephalography data recorded from 37 children and young adults with drug-resistant epilepsy who underwent neurosurgery. Using source imaging, we estimated virtual sensors at locations where intracranial EEG contacts were placed. On data with and without spikes, we computed undirected functional connectivity between sensors/contacts using amplitude envelope correlation and phase locking value for physiologically relevant frequency bands. From each functional connectivity matrix, we generated an undirected network containing the strongest connections within sensors/contacts using the minimum spanning tree. For each sensor/contact, we computed graph centrality measures. We compared functional connectivity and their derived graph centrality of sensors/contacts inside resection for good (n = 22, ILAE I) and poor (n = 15, ILAE II-VI) outcome patients, tested their ability to predict the epileptogenic zone in good-outcome patients, examined the association between highly connected hubs removal and surgical outcome and performed leave-one-out cross-validation to support their prognostic value. We also compared the predictive values of functional connectivity with those of dipoles. Finally, we tested the reliability of virtual sensor measures via Spearman's correlation with intracranial EEG at population- and patient-level. We observed higher functional connectivity inside than outside resection (P < 0.05, Wilcoxon signed-rank test) for good-outcome patients, on data with and without spikes across different bands for intracranial EEG and electric/magnetic source imaging and few differences for poor-outcome patients. These functional connectivity measures were predictive of both the epileptogenic zone and outcome (positive and negative predictive values ≥55%, validated using leave-one-out cross-validation) outperforming dipoles on spikes. Significant correlations were found between source imaging and intracranial EEG measures (0.4 ≤ rho ≤ 0.9, P < 0.05). Our findings suggest that virtual implantation of sensors through source imaging can non-invasively identify highly connected hubs in patients with drug-resistant epilepsy, even in the absence of frank epileptiform activity. Surgical resection of these hubs predicts outcome better than dipoles.
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Affiliation(s)
- Ludovica Corona
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Steve M Stufflebeam
- Athinoula Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
- School of Medicine, Texas Christian University, Fort Worth, TX 76129, USA
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13
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Nanda A, Johnson GW, Mu Y, Ahrens MB, Chang C, Englot DJ, Breakspear M, Rubinov M. Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena. Cell Rep 2023; 42:112254. [PMID: 36966391 PMCID: PMC10518034 DOI: 10.1016/j.celrep.2023.112254] [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: 02/01/2022] [Revised: 12/22/2022] [Accepted: 02/28/2023] [Indexed: 03/27/2023] Open
Abstract
Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for sampling time series constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for diverse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity and provide stringent tests on future theories that seek to transcend these explanations.
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Affiliation(s)
- Aditya Nanda
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Yu Mu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Misha B Ahrens
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Michael Breakspear
- School of Psychology, University of Newcastle, Callaghan, NSW 2308, Australia; School of Medicine and Public Health, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Mikail Rubinov
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
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Wang A, Fan Z, Zhang Y, Wang J, Zhang X, Wang P, Mu W, Zhan G, Wang M, Zhang L, Gan Z, Kang X. Resting-state SEEG-based brain network analysis for the detection of epileptic area. J Neurosci Methods 2023; 390:109839. [PMID: 36933706 DOI: 10.1016/j.jneumeth.2023.109839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions. NEW METHOD The location coordinates of SEEG in the brain were identified using CT and MRI. Based on undirected brain network connectivity, five functional connectivity measures and data feature vector centrality were calculated. Network connectivity was calculated from multiple perspectives of linear correlation, information theory, phase, and frequency, and the relative influence of nodes on network connectivity was considered. We investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the differences between patients with different surgical outcomes. RESULTS By comparing the centrality of brain network connectivity between epileptic and non-epileptic zones, we found significant differences in the distribution of brain networks between the two zones. There was a significant difference in brain network between patients with good surgical outcomes and those with poor surgical outcomes (p < 0.01). By combining support vector machines with static node importance, we predicted an AUC of 0.94 ± 0.08 for the epilepsy zone. CONCLUSIONS AND SIGNIFICANCE The results illustrated that nodes in epileptic zones are distinct from those in non-epileptic zones. Analysis of resting-state SEEG data and the importance of nodes in the brain network may contribute to identifying the epileptic zone and predicting the outcome.
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Affiliation(s)
- Aiping Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Yuan Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Junkongshuai Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Xueze Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Pengchao Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Wei Mu
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Gege Zhan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Minjie Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China
| | - Lihua Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China; Ji Hua Laboratory, 28 Island Ring South Rd., Foshan City 528200, China
| | - Zhongxue Gan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China; Ji Hua Laboratory, 28 Island Ring South Rd., Foshan City 528200, China
| | - Xiaoyang Kang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, 200433 Shanghai, China; Yiwu Research Institute of Fudan University, Chengbei Road, Yiwu City, 322000 Zhejiang, China; Ji Hua Laboratory, 28 Island Ring South Rd., Foshan City 528200, China; Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou 311100, China.
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Lagarde S, Bénar CG, Wendling F, Bartolomei F. Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG. Brain Connect 2022; 12:850-869. [PMID: 35972755 PMCID: PMC9807250 DOI: 10.1089/brain.2021.0190] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.
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Affiliation(s)
- Stanislas Lagarde
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, Marseille, France.,Address correspondence to: Stanislas Lagarde, Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, 264 Rue Saint-Pierre, 13005 Marseille, France
| | | | | | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,Department of Epileptology and Cerebral Rythmology, APHM, Timone Hospital, Marseille, France
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Jiang H, Kokkinos V, Ye S, Urban A, Bagić A, Richardson M, He B. Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2200887. [PMID: 35545899 PMCID: PMC9218648 DOI: 10.1002/advs.202200887] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Indexed: 05/23/2023]
Abstract
Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.
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Affiliation(s)
- Haiteng Jiang
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
- Department of NeurobiologyAffiliated Mental Health Center & Hangzhou Seventh People's HospitalZhejiang University School of MedicineHangzhou310013P. R. China
- NHC and CAMS Key Laboratory of Medical NeurobiologyMOE Frontier Science Center for Brain Science and Brain‐machine IntegrationSchool of Brain Science and Brain MedicineZhejiang UniversityHangzhou310058P. R. China
| | - Vasileios Kokkinos
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
- Massachusetts General HospitalBostonMA02114USA
| | - Shuai Ye
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
| | - Alexandra Urban
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
| | - Anto Bagić
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
| | - Mark Richardson
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
- Massachusetts General HospitalBostonMA02114USA
| | - Bin He
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
- Neuroscience InstituteCarnegie Mellon UniversityPittsburghPA15213USA
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Paulo DL, Wills KE, Johnson GW, Gonzalez HFJ, Rolston JD, Naftel RP, Reddy SB, Morgan VL, Kang H, Williams Roberson S, Narasimhan S, Englot DJ. SEEG Functional Connectivity Measures to Identify Epileptogenic Zones: Stability, Medication Influence, and Recording Condition. Neurology 2022; 98:e2060-e2072. [PMID: 35338075 PMCID: PMC9162047 DOI: 10.1212/wnl.0000000000200386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 02/01/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Functional connectivity (FC) measures can be used to differentiate epileptogenic zones (EZs) from non-EZs in patients with medically refractory epilepsy. Little work has been done to evaluate the stability of stereo-EEG (SEEG) FC measures over time and their relationship with antiseizure medication (ASM) use, a critical confounder in epilepsy FC studies. We aimed to answer the following questions: Are SEEG FC measures stable over time? Are they influenced by ASMs? Are they affected by patient data collection state? METHODS In 32 patients with medically refractory focal epilepsy, we collected a single 2-minute prospective SEEG resting-state (awake, eyes closed) data set and consecutive 2-minute retrospective pseudo-rest (awake, eyes open) data sets for days 1-7 postimplantation. ASM dosages were recorded for days 1-7 postimplantation and drug load score (DLS) per day was calculated to standardize and compare across patients. FC was evaluated using directed and nondirected measures. Standard clinical interpretation of ictal SEEG was used to classify brain regions as EZs and non-EZs. RESULTS Over 7 days, presumed EZs consistently had higher FC than non-EZs when using between imaginary coherence (ImCoh) and partial directed coherence (PDC) inward strength, without accounting for DLS. These measures were demonstrated to be stable over a short-term period of 3 consecutive days with the same DLS. Between ImCoh FC differences between EZs and non-EZs were reduced with DLS decreases, whereas other measures were not affected by DLS. FC differences between EZs and non-EZs were seen during both resting-state and pseudo-rest conditions; ImCoh values were strongly correlated between the 2 conditions, whereas PDC values were not. DISCUSSION Inward and nondirected SEEG FC is higher in presumed EZs vs non-EZs and measures are stable over time. However, certain measures may be affected by ASM dose, as between ImCoh differences between EZs and non-EZs are less pronounced with lower doses, and other measures such as PDC are poorly correlated across recording conditions. These findings allow novel insight into how SEEG FC measures may aid surgical localization and how they are influenced by ASMs and other factors.
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Affiliation(s)
- Danika L Paulo
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Kristin E Wills
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Graham W Johnson
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Hernan F J Gonzalez
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - John D Rolston
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Robert P Naftel
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Shilpa B Reddy
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Victoria L Morgan
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Hakmook Kang
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Shawniqua Williams Roberson
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Saramati Narasimhan
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
| | - Dario J Englot
- From the Departments of Neurological Surgery (D.L.P., K.E.W., R.P.N., V.L.M., S.N., D.J.E.), Radiology and Radiological Sciences (V.L.M., D.J.E.), Biostatistics (V.L.M., S.W.R., D.J.E.), and Neurology (H.K.), Vanderbilt University Medical Center; Vanderbilt University Institute of Imaging Science (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Vanderbilt Institute for Surgery and Engineering (K.E.W., G.W.J., H.F.J.G., V.L.M., S.N., D.J.E.); Department of Biomedical Engineering (G.W.J., H.F.J.G., V.L.M., S.W.R., S.N., D.J.E.), Vanderbilt University, Nashville, TN; Departments of Neurosurgery and Biomedical Engineering (J.D.R.), University of Utah, Salt Lake City; and Department of Pediatrics (S.B.R.), Vanderbilt Children's Hospital, Nashville, TN
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Taylor PN, Papasavvas CA, Owen TW, Schroeder GM, Hutchings FE, Chowdhury FA, Diehl B, Duncan JS, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Wang Y. Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue. Brain 2022; 145:939-949. [PMID: 35075485 PMCID: PMC9050535 DOI: 10.1093/brain/awab380] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/19/2021] [Accepted: 09/03/2021] [Indexed: 11/14/2022] Open
Abstract
The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localizing epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner. To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 participants (21 598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We proposed that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures postoperatively. We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions that were spared by surgery were more abnormal than those resected only in patients with persistent postoperative seizures (t = -3.6, P = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (area under curve 0.75 P = 0.0003). Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.
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Affiliation(s)
- Peter N Taylor
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Christoforos A Papasavvas
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Thomas W Owen
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Gabrielle M Schroeder
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Frances E Hutchings
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Beate Diehl
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - John S Duncan
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Andrew W McEvoy
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Sjoerd B Vos
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Matthew C Walker
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
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Hermann BP, Struck AF, Busch RM, Reyes A, Kaestner E, McDonald CR. Neurobehavioural comorbidities of epilepsy: towards a network-based precision taxonomy. Nat Rev Neurol 2021; 17:731-746. [PMID: 34552218 PMCID: PMC8900353 DOI: 10.1038/s41582-021-00555-z] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2021] [Indexed: 02/06/2023]
Abstract
Cognitive and behavioural comorbidities are prevalent in childhood and adult epilepsies and impose a substantial human and economic burden. Over the past century, the classic approach to understanding the aetiology and course of these comorbidities has been through the prism of the medical taxonomy of epilepsy, including its causes, course, characteristics and syndromes. Although this 'lesion model' has long served as the organizing paradigm for the field, substantial challenges to this model have accumulated from diverse sources, including neuroimaging, neuropathology, neuropsychology and network science. Advances in patient stratification and phenotyping point towards a new taxonomy for the cognitive and behavioural comorbidities of epilepsy, which reflects the heterogeneity of their clinical presentation and raises the possibility of a precision medicine approach. As we discuss in this Review, these advances are informing the development of a revised aetiological paradigm that incorporates sophisticated neurobiological measures, genomics, comorbid disease, diversity and adversity, and resilience factors. We describe modifiable risk factors that could guide early identification, treatment and, ultimately, prevention of cognitive and broader neurobehavioural comorbidities in epilepsy and propose a road map to guide future research.
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Affiliation(s)
- Bruce P. Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.,
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.,William S. Middleton Veterans Administration Hospital, Madison, WI, USA
| | - Robyn M. Busch
- Epilepsy Center and Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.,Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Anny Reyes
- Department of Psychiatry and Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
| | - Erik Kaestner
- Department of Psychiatry and Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
| | - Carrie R. McDonald
- Department of Psychiatry and Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
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20
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Effects of hyperventilation with face mask on brain network in patients with epilepsy. Epilepsy Res 2021; 176:106741. [PMID: 34418857 DOI: 10.1016/j.eplepsyres.2021.106741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 07/20/2021] [Accepted: 08/12/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVES During the ongoing pandemic of COVID-19, wearing face masks was recommended, including patients with epilepsy doing the hyperventilation (HV) test during electroencephalogram (EEG) examination somewhere. However, evidence was still limited about the effect of HV with face mask on cortical excitability of patients with epilepsy. The motivation of this work is to make use of the graph theory of EEG to characterize the cortical excitability of patients with epilepsy when they did HV under the condition wearing a surgical face mask. METHODS We recruited 19 patients with epilepsy and 17 normal controls. All of participants completed two HV experiments, including HV with face mask (HV+) and HV without a mask (HV). The interval was 30 min and the sequence was random. Each experiment consisted of three segments: resting EEG, EEG of HV, and EEG of post-HV. EEG were recorded successively during each experiment. Participants were asked to evaluate the discomfort degree using a questionnaire when every HV is completed. RESULTS All of the participants felt more uncomfortable after HV + . Moreover, not only HV decreased small-worldness index in patients with epilepsy, but also HV + significantly increased the clustering coefficient in patients with epilepsy. Importantly, the three-way of Mask*HV*Epilepsy showed interaction in the clustering coefficient in the delta band, as well as in the path length and the small-worldness index in the theta band. CONCLUSIONS The results of this study indicated that patients with epilepsy showed the increased excitability of brain network during HV + . We should pay more attention to the adverse effect on brain network excitability caused by HV + in patients with epilepsy. In the clinical practice under the COVID-19 pandemic, it is important that the wearing face mask remain cautious for the individuals with epilepsy when they carried out HV behavior such as exercise (e.g., running, etc.).
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21
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The UK experience of stereoelectroencephalography in children: An analysis of factors predicting the identification of a seizure-onset zone and subsequent seizure freedom. Epilepsia 2021; 62:1883-1896. [PMID: 34165813 DOI: 10.1111/epi.16954] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/13/2021] [Accepted: 05/13/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Stereoelectroencephalography (SEEG) is being used more frequently in the pre-surgical evaluation of children with focal epilepsy. It has been shown to be safe in children, but there are no multicenter studies assessing the rates and factors associated with the identification of a putative seizure-onset zone (SOZ) and subsequent seizure freedom following SEEG-guided epilepsy surgery. METHODS Multicenter retrospective cohort study of all children undergoing SEEG at six of seven UK Children's Epilepsy Surgery Service centers from 2014 to 2019. Demographics, noninvasive evaluation, SEEG, and operative factors were analyzed to identify variables associated with the identification of a putative SOZ and subsequent seizure freedom following SEEG-guided epilepsy surgery. RESULTS One hundred thirty-five patients underwent 139 SEEG explorations using a total of 1767 electrodes. A putative SOZ was identified in 117 patients (85.7%); odds of successfully finding an SOZ were 6.4 times greater for non-motor seizures compared to motor seizures (p = 0.02) and 3.6 times more if four or more seizures were recorded during SEEG (p = 0.03). Of 100 patients undergoing surgical treatment, 47 (47.0%) had an Engel class I outcome at a median follow-up of 1.3 years; the only factor associated with outcome was indication for SEEG (p = 0.03); an indication of "recurrence following surgery/treatment" had a 5.9 times lower odds of achieving seizure freedom (p = 0.002) compared to the "lesion negative" cohort, whereas other indications ("lesion positive, define extent," "lesion positive, discordant noninvasive investigations" and "multiple lesions") were not statistically significantly different. SIGNIFICANCE This large nationally representative cohort illustrates that SEEG-guided surgery can still achieve high rates of seizure freedom. Seizure semiology and the number of seizures recorded during SEEG are important factors in the identification of a putative SOZ, and the indication for SEEG is an important factor in postoperative outcomes.
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22
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Struck AF, Boly M, Hwang G, Nair V, Mathis J, Nencka A, Conant LL, DeYoe EA, Ragahavan M, Prabhakaran V, Binder JR, Meyerand ME, Hermann BP. Regional and global resting-state functional MR connectivity in temporal lobe epilepsy: Results from the Epilepsy Connectome Project. Epilepsy Behav 2021; 117:107841. [PMID: 33611101 PMCID: PMC8035304 DOI: 10.1016/j.yebeh.2021.107841] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/20/2021] [Accepted: 02/01/2021] [Indexed: 12/28/2022]
Abstract
Temporal lobe epilepsy (TLE) has been conceptualized as focal disease with a discrete neurobiological focus and can respond well to targeted resection or ablation. In contrast, the neuro-cognitive deficits resulting from TLE can be widespread involving regions beyond the primary epileptic network. We hypothesize that this seemingly paradoxical findings can be explained by differences in connectivity between the primary epileptic region which is hyper-connected and its secondary influence on global connectome organization. This hypothesis is tested using regional and global graph theory metrics where we anticipate that regional mesial-temporal hyperconnectivity will be found and correlate with seizure frequency while global networks will be disorganized and be more closely associated with neuro-cognitive deficits. Resting-state fMRI was used to examine temporal lobe regional connectivity and global functional connectivity from 102 patients with TLE and 55 controls. Connectivity matrices were calculated for subcortical volumes and cortical parcellations. Graph theory metrics (global clustering coefficient (GCC), degree, closeness) were compared between groups and in relation to neuropsychological profiles and disease covariates using permutation testing and causal analysis. In TLE there was a decrease in GCC (p = 0.0345) associated with a worse neuropsychological profile (p = 0.0134). There was increased connectivity in the left hippocampus/amygdala (degree p = 0.0103, closeness p = 0.0104) and a decrease in connectivity in the right lateral temporal lobe (degree p = 0.0186, closeness p = 0.0122). A ratio between the hippocampus/amygdala and lateral temporal lobe-temporal lobe connectivity ratio (TLCR) revealed differences between TLE and controls for closeness (left p = 0.00149, right p = 0.0494) and for degree on left p = 0.00169; with trend on right p = 0.0567. Causal analysis suggested that "Epilepsy Activity" (seizure frequency, anti-seizure medications) was associated with increase in TLCR but not in GCC, while cognitive decline was associated with decreased GCC. These findings support the hypothesis that in TLE there is hyperconnectivity in the hippocampus/amygdala and hypoconnectivity in the lateral temporal lobe associated with "Epilepsy Activity." While, global connectome disorganization was associated with worse neuropsychological phenotype.
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Affiliation(s)
- Aaron F Struck
- University of Wisconsin-Madison, Department of Neurology, United States; William S. Middleton Veterans Administration Hospital, Madison, WI, United States.
| | - Melanie Boly
- University of Wisconsin-Madison, Department of Neurology
| | - Gyujoon Hwang
- University of Wisconsin-Madison, Department of Medical Physics
| | - Veena Nair
- University of Wisconsin-Madison, Department of Radiology
| | | | - Andrew Nencka
- Medical College of Wisconsin, Department of Radiology
| | - Lisa L Conant
- Medical College of Wisconsin, Department of Neurology
| | - Edgar A DeYoe
- Medical College of Wisconsin, Department of Radiology
| | | | | | | | - Mary E Meyerand
- University of Wisconsin-Madison, Department of Medical Physics
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23
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Gupta K, Grover P, Abel TJ. Current Conceptual Understanding of the Epileptogenic Network From Stereoelectroencephalography-Based Connectivity Inferences. Front Neurol 2020; 11:569699. [PMID: 33324320 PMCID: PMC7724044 DOI: 10.3389/fneur.2020.569699] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 10/13/2020] [Indexed: 11/13/2022] Open
Abstract
Localization of the epileptogenic zone (EZ) is crucial in the surgical treatment of focal epilepsy. Recently, EEG studies have revealed that the EZ exhibits abnormal connectivity, which has led investigators to now consider connectivity as a biomarker to localize the EZ. Further, abnormal connectivity of the EZ may provide an explanation for the impact of focal epilepsy on more widespread brain networks involved in typical cognition and development. Stereo-electroencephalography (sEEG) is a well-established method for localizing the EZ that has recently been applied to examine altered brain connectivity in epilepsy. In this manuscript, we review recent computational methods for identifying the EZ using sEEG connectivity. Findings from previous sEEG studies indicate that during interictal periods, the EZ is prone to seizure generation but concurrently receives inward connectivity preventing seizures. At seizure onset, this control is lost, allowing seizure activity to spread from the EZ. Regulatory areas within the EZ may be important for subsequently ending the seizure. After the seizure, the EZ appears to regain its influence on the network, which may be how it is able to regenerate epileptiform activity. However, more research is needed on the dynamic connectivity of the EZ in order to build a biomarker for EZ localization. Such a biomarker would allow for patients undergoing sEEG to have electrode implantation, localization of the EZ, and resection in a fraction of the time currently needed, preventing patients from having to endure long hospital stays and induced seizures.
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Affiliation(s)
- Kanupriya Gupta
- University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States
| | - Pulkit Grover
- Center for the Neural Basis of Cognition, Carnegie Mellon University/University of Pittsburgh, Pittsburgh, PA, United States.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Taylor J Abel
- University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Carnegie Mellon University/University of Pittsburgh, Pittsburgh, PA, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
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24
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Abstract
Stereo Electroencephalography Versus Subdural Strip Electrode Implantations: Feasibility, Complications, and Outcomes in 500 Intracranial Monitoring Cases for Drug-Resistant Epilepsy Joswig H, Lau JC, Abdallat M, Parrent GP, MacDougall, KW, McLachlan RS, Burneo, JG, Steven, DA. Neurosurgery. 2020;87(1):E23-E30. doi: 10.1093/neuros/nyaa112 Background: Both stereoelectroencephalography (SEEG) and subdural strip electrodes (SSE) are used for intracranial electroencephalographic recordings in the invasive investigation of patients with drug-resistant epilepsy. Objective: To compare SEEG and SSE with respect to feasibility, complications, and outcome in this single-center study. Methods: Patient characteristics, periprocedural parameters, complications, and outcome were acquired from a pro- and retrospectively managed data bank to compare SEEG and SSE cases. Results: A total of 500 intracranial electroencephalographic monitoring cases in 450 patients were analyzed (145 SEEG and 355 SSE). Both groups were of similar age, gender distribution, and duration of epilepsy. Implantation of each SEEG electrode took 13.9 ± 7.6 minutes (20 ± 12 minutes for each SSE; P < .01). Radiation exposure to the patient was 4.3 ± 7.7 seconds to a dose area product of 14.6 ± 27.9 rad·cm2 for SEEG and 9.4 ± 8.9 seconds with 21 ± 22.4 rad·cm2 for SSE (P < .01). There was no difference in the length of stay (12.2 ± 7.2 and 12 ± 6.3 days). The complication rate was low in both groups. No infections were seen in SEEG cases (2.3% after SSE). The rate of hemorrhage was 2.8% for SEEG and 1.4% for SSE. Surgical outcome was similar. Conclusion: stereoelectroencephalography allows targeting deeply situated foci with a noninferior safety profile to SSE and seizure outcome comparable to SSE.
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25
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Narasimhan S, Kundassery KB, Gupta K, Johnson GW, Wills KE, Goodale SE, Haas K, Rolston JD, Naftel RP, Morgan VL, Dawant BM, González HFJ, Englot DJ. Seizure-onset regions demonstrate high inward directed connectivity during resting-state: An SEEG study in focal epilepsy. Epilepsia 2020; 61:2534-2544. [PMID: 32944945 DOI: 10.1111/epi.16686] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 01/06/2023]
Abstract
OBJECTIVE In patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity. METHODS In 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity. RESULTS Ictogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes. SIGNIFICANCE Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation.
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Affiliation(s)
- Saramati Narasimhan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Keshav B Kundassery
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kanupriya Gupta
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Graham W Johnson
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Kristin E Wills
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Sarah E Goodale
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Kevin Haas
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Victoria L Morgan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Hernán F J González
- Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Dario J Englot
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
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26
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Englot DJ. Responsive Neurostimulation in Epilepsy: Wall to Block Seizures or Bridge to Resection? Epilepsy Curr 2020; 20:265-266. [PMID: 34025237 PMCID: PMC7653660 DOI: 10.1177/1535759720935841] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Mesial Temporal Resection Following Long-Term Ambulatory Intracranial EEG
Monitoring With a Direct Brain-Responsive Neurostimulation System Hirsch LJ, Mirro EA, Salanova V, Witt TC, Drees CN, Brown MG, Lee RW, Sadler TL,
Felton EA, Rutecki P, Shin HW, Hadar E, Hegde M, Rao VR, Mnatsakanyan L, Madhavan DS,
Zakaria TJ, Liu AA, Heck CN, Greenwood JE, Bigelow JK, Nair DR, Alexopoulos AV, Mackow
M, Edwards JC, Sotudeh N, Kuzniecky RI, Gwinn RP, Doherty MJ, Geller EB, Morrell MJ.
Epilepsia. 2020;61(3):408-420. doi: 10.1111/epi.16442 Objective: To describe seizure outcomes in patients with medically refractory epilepsy who had
evidence of bilateral mesial temporal lobe (MTL) seizure onsets and underwent MTL
resection based on chronic ambulatory intracranial EEG (ICEEG) data from a direct
brain-responsive neurostimulator (RNS) system. Methods: We retrospectively identified all patients at 17 epilepsy centers with MTL epilepsy
who were treated with the RNS system using bilateral MTL leads, and in whom an MTL
resection was subsequently performed. Presumed lateralization based on routine
presurgical approaches was compared to lateralization determined by RNS system
chronic ambulatory ICEEG recordings. The primary outcome was frequency of disabling
seizures at last 3-month follow-up after MTL resection compared to seizure frequency
3 months before MTL resection. Results: We identified 157 patients treated with the RNS system with bilateral MTL leads due
to presumed bitemporal epilepsy. Twenty-five (16%) patients subsequently had an MTL
resection informed by chronic ambulatory ICEEG (mean = 42 months ICEEG); follow-up
was available for 24 patients. After MTL resection, the median reduction in
disabling seizures at last follow-up was 100% (mean: 94%; range: 50%-100%). Nine
(38%) patients had exclusively unilateral electrographic seizures recorded by
chronic ambulatory ICEEG and all were seizure-free at last follow-up after MTL
resection; 8 of 9 continued RNS system treatment. Fifteen (62%) patients had
bilateral MTL electrographic seizures, had an MTL resection on the more active side,
continued RNS system treatment, and achieved a median clinical seizure reduction of
100% (mean: 90%; range: 50%-100%) at last follow-up, with 8 of 15 seizure-free. For
those with more than 1 year of follow-up (N = 21), 15 (71%) patients were
seizure-free during the most recent year, including all 8 patients with unilateral
onsets and 7 (54%) of 13 patients with bilateral onsets. Significance: Chronic ambulatory ICEEG data provide information about lateralization of MTL
seizures and can identify additional patients who may benefit from MTL
resection.
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27
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Chari A, Thornton RC, Tisdall MM, Scott RC. Microelectrode recordings in human epilepsy: a case for clinical translation. Brain Commun 2020; 2:fcaa082. [PMID: 32954332 PMCID: PMC7472902 DOI: 10.1093/braincomms/fcaa082] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 04/21/2020] [Accepted: 04/28/2020] [Indexed: 12/25/2022] Open
Abstract
With their 'all-or-none' action potential responses, single neurons (or units) are accepted as the basic computational unit of the brain. There is extensive animal literature to support the mechanistic importance of studying neuronal firing as a way to understand neuronal microcircuits and brain function. Although most studies have emphasized physiology, there is increasing recognition that studying single units provides novel insight into system-level mechanisms of disease. Microelectrode recordings are becoming more common in humans, paralleling the increasing use of intracranial electroencephalography recordings in the context of presurgical evaluation in focal epilepsy. In addition to single-unit data, microelectrode recordings also record local field potentials and high-frequency oscillations, some of which may be different to that recorded by clinical macroelectrodes. However, microelectrodes are being used almost exclusively in research contexts and there are currently no indications for incorporating microelectrode recordings into routine clinical care. In this review, we summarize the lessons learnt from 65 years of microelectrode recordings in human epilepsy patients. We cover the electrode constructs that can be utilized, principles of how to record and process microelectrode data and insights into ictal dynamics, interictal dynamics and cognition. We end with a critique on the possibilities of incorporating single-unit recordings into clinical care, with a focus on potential clinical indications, each with their specific evidence base and challenges.
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Affiliation(s)
- Aswin Chari
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | - Rachel C Thornton
- Department of Clinical Neurophysiology, Great Ormond Street Hospital, London WC1N 3JH, UK
| | - Martin M Tisdall
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | - Rodney C Scott
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurological Sciences, University of Vermont, Burlington, VT 05405, USA
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