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Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence

ORCID
0000-0002-5798-6961
Affiliation
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Laiou, Petroula;
ORCID
0000-0003-1072-665X
Affiliation
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Biondi, Andrea;
Affiliation
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Bruno, Elisa;
ORCID
0000-0003-0861-8705
Affiliation
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Viana, Pedro F.;
Affiliation
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Winston, Joel S.;
ORCID
0000-0002-6843-9920
Affiliation
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Rashid, Zulqarnain;
Affiliation
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Ranjan, Yatharth;
Affiliation
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Conde, Pauline;
Affiliation
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Stewart, Callum;
Affiliation
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Sun, Shaoxiong;
ORCID
0000-0003-4817-3835
Affiliation
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Zhang, Yuezhou;
ORCID
0000-0002-0333-1927
Affiliation
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Folarin, Amos;
Affiliation
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Dobson, Richard J. B.;
Affiliation
Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
Schulze-Bonhage, Andreas;
ORCID
0000-0002-1476-7777
Affiliation
Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
Dümpelmann, Matthias;
Affiliation
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
Richardson, Mark P.

Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.

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