Analyzing functional brain connectivity by means of commute times: A new approach and its application to track event-related dynamics

Dimitriadis, SI; Laskaris, NA; Tzelepi, A; Economou, G

dc.contributor.author	Dimitriadis, SI	en
dc.contributor.author	Laskaris, NA	en
dc.contributor.author	Tzelepi, A	en
dc.contributor.author	Economou, G	en
dc.date.accessioned	2014-03-01T02:07:39Z
dc.date.available	2014-03-01T02:07:39Z
dc.date.issued	2012	en
dc.identifier.issn	00189294	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29604
dc.subject	Complex networks	en
dc.subject	encephalography	en
dc.subject	graph theory	en
dc.subject	time series analysis	en
dc.subject.other	Brain connectivity	en
dc.subject.other	Cognitive task	en
dc.subject.other	Complex networks	en
dc.subject.other	Connectivity pattern	en
dc.subject.other	EEG signals	en
dc.subject.other	encephalography	en
dc.subject.other	Functional connectivity	en
dc.subject.other	Functional network	en
dc.subject.other	Functional organization	en
dc.subject.other	Neural synchrony	en
dc.subject.other	Random Walk	en
dc.subject.other	Relational data	en
dc.subject.other	Eye movements	en
dc.subject.other	Graph theory	en
dc.subject.other	Time series analysis	en
dc.subject.other	article	en
dc.subject.other	brain function	en
dc.subject.other	commute time	en
dc.subject.other	electroencephalogram	en
dc.subject.other	event related potential	en
dc.subject.other	eye movement	en
dc.subject.other	female	en
dc.subject.other	functional brain connectivity	en
dc.subject.other	human	en
dc.subject.other	male	en
dc.subject.other	nervous system parameters	en
dc.subject.other	Algorithms	en
dc.subject.other	Artificial Intelligence	en
dc.subject.other	Brain	en
dc.subject.other	Brain Mapping	en
dc.subject.other	Electroencephalography	en
dc.subject.other	Eye Movements	en
dc.subject.other	Female	en
dc.subject.other	Humans	en
dc.subject.other	Male	en
dc.subject.other	Models, Neurological	en
dc.subject.other	Nerve Net	en
dc.subject.other	Pattern Recognition, Automated	en
dc.subject.other	Reproducibility of Results	en
dc.subject.other	Signal Processing, Computer-Assisted	en
dc.title	Analyzing functional brain connectivity by means of commute times: A new approach and its application to track event-related dynamics	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TBME.2012.2186568	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TBME.2012.2186568	en
heal.identifier.secondary	6145618	en
heal.publicationDate	2012	en
heal.abstract	There is growing interest in studying the association of functional connectivity patterns with particular cognitive tasks. The ability of graphs to encapsulate relational data has been exploited in many related studies, where functional networks (sketched by different neural synchrony estimators) are characterized by a rich repertoire of graph-related metrics. We introduce commute times (CTs) as an alternative way to capture the true interplay between the nodes of a functional connectivity graph (FCG). CT is a measure of the time taken for a random walk to setout and return between a pair of nodes on a graph. Its computation is considered here as a robust and accurate integration, over the FCG, of the individual pairwise measurements of functional coupling. To demonstrate the benefits from our approach, we attempted the characterization of time evolving connectivity patterns derived from EEG signals recorded while the subject was engaged in an eye-movement task. With respect to standard ways, which are currently employed to characterize connectivity, an improved detection of event-related dynamical changes is noticeable. CTs appear to be a promising technique for deriving temporal fingerprints of the brains dynamic functional organization. © 1964-2012 IEEE.	en
heal.journalName	IEEE Transactions on Biomedical Engineering	en
dc.identifier.doi	10.1109/TBME.2012.2186568	en
dc.identifier.volume	59	en
dc.identifier.issue	5	en
dc.identifier.spage	1302	en
dc.identifier.epage	1309	en