气候变化领域集成服务门户(CCPortal): Time-evolving controllability of effective connectivity networks during seizure progression

CCPortal

DOI	10.1073/pnas.2006436118
	Time-evolving controllability of effective connectivity networks during seizure progression
	Scheid B.H.; Ashourvan A.; Stiso J.; Davis K.A.; Mikhail F.; Pasqualetti F.; Litt B.; Bassett D.S.
发表日期	2021
ISSN	00278424
卷号	118 期号:5
英文摘要	Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized stimulation parameters, including when and where to intervene to guarantee a positive patient outcome, is a major open challenge. Network neuroscience and control theory offer useful tools that may guide improvements in parameter selection for control of anomalous neural activity. Here we use a method to characterize dynamic controllability across consecutive effective connectivity (EC) networks based on regularized partial correlations between implanted electrodes during the onset, propagation, and termination regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s time windows of intracranial electrocorticography recordings using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO). Average and modal controllability metrics calculated from each resulting EC network track the time-varying controllability of the brain on an evolving landscape of conditionally dependent network interactions. We show that average controllability increases throughout a seizure and is negatively correlated with modal controllability throughout. Our results support the hypothesis that the energy required to drive the brain to a seizure-free state from an ictal state is smallest during seizure onset, yet we find that applying control energy at electrodes in the seizure onset zone may not always be energetically favorable. Our work suggests that a low-complexity model of time-evolving controllability may offer insights for developing and improving control strategies targeting seizure suppression. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Controllability; Effective connectivity; Epilepsy; GLASSO; Network topology
语种	英语
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180798
作者单位	Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States; Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, United States; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, United States; Department of Mechanical Engineering, University of California, Riverside, CA 92521, United States; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, United States; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, United States; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States; Santa Fe Institute, Santa Fe, NM 87501, United States
推荐引用方式 GB/T 7714	Scheid B.H.,Ashourvan A.,Stiso J.,et al. Time-evolving controllability of effective connectivity networks during seizure progression[J],2021,118(5).
APA	Scheid B.H..,Ashourvan A..,Stiso J..,Davis K.A..,Mikhail F..,...&Bassett D.S..(2021).Time-evolving controllability of effective connectivity networks during seizure progression.Proceedings of the National Academy of Sciences of the United States of America,118(5).
MLA	Scheid B.H.,et al."Time-evolving controllability of effective connectivity networks during seizure progression".Proceedings of the National Academy of Sciences of the United States of America 118.5(2021).