dc.contributor.author	Chinichian, Narges
dc.contributor.author	Kruschwitz, Johann D.
dc.contributor.author	Reinhardt, Pablo
dc.contributor.author	Palm, Maximilian
dc.contributor.author	Wellan, Sarah A.
dc.contributor.author	Erk, Susanne
dc.contributor.author	Heinz, Andreas
dc.contributor.author	Walter, Henrik
dc.contributor.author	Veer, Ilya M.
dc.date.accessioned	2023-03-17T12:18:22Z
dc.date.available	2023-03-17T12:18:22Z
dc.date.issued	2023-02-09
dc.date.updated	2023-02-23T17:06:59Z
dc.description.abstract	Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks.
dc.description.sponsorship	DFG, 313856816, SPP 2041: Computational Connectomics
dc.description.sponsorship	DFG, 337619223, GRK 2386: Extrospektion. Externer Zugang zu höheren kognitiven Prozessen
dc.description.sponsorship	TU Berlin, Open-Access-Mittel - 2023
dc.identifier.eissn	1662-453X
dc.identifier.issn	1662-4548
dc.identifier.uri	https://depositonce.tu-berlin.de/handle/11303/18328
dc.identifier.uri	https://doi.org/10.14279/depositonce-17136
dc.language.iso	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject.ddc	600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.subject.other	task-based fMRI
dc.subject.other	dynamic functional connectivity
dc.subject.other	network neuroscience
dc.subject.other	template-based flexibility
dc.subject.other	community detection
dc.subject.other	dynamical network analysis
dc.subject.other	modular structure
dc.title	A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility
dc.type	Article
dc.type.version	publishedVersion
dcterms.bibliographicCitation.articlenumber	1025428
dcterms.bibliographicCitation.doi	10.3389/fnins.2023.1025428
dcterms.bibliographicCitation.journaltitle	Frontiers in Neuroscience
dcterms.bibliographicCitation.originalpublishername	Frontiers
dcterms.bibliographicCitation.originalpublisherplace	Lausanne
dcterms.bibliographicCitation.volume	17
dcterms.rightsHolder.reference	Creative-Commons-Lizenz
tub.accessrights.dnb	free
tub.affiliation	Fak. 2 Mathematik und Naturwissenschaften::Inst. Theoretische Physik::FG Nichtlineare Dynamik und Kontrolle
tub.publisher.universityorinstitution	Technische Universität Berlin

A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility

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