dc.contributor.author	Dimulescu, Cristiana
dc.contributor.author	Gareayaghi, Serdar
dc.contributor.author	Kamp, Fabian
dc.contributor.author	Fromm, Sophie
dc.contributor.author	Obermayer, Klaus
dc.contributor.author	Metzner, Christoph
dc.date.accessioned	2021-07-26T09:25:51Z
dc.date.available	2021-07-26T09:25:51Z
dc.date.issued	2021-06-28
dc.date.updated	2021-07-12T08:07:20Z
dc.description.abstract	The coordinated dynamic interactions of large-scale brain circuits and networks have been associated with cognitive functions and behavior. Recent advances in network neuroscience have suggested that the anatomical organization of such networks puts fundamental constraints on the dynamical landscape of brain activity, i.e., the different states, or patterns of regional activation, and transition between states the brain can display. Specifically, it has been shown that densely connected, central regions control the transition between states that are “easily” reachable (in terms of expended energy), whereas weakly connected areas control transitions to states that are hard-to-reach. Changes in large-scale brain activity have been hypothesized to underlie many neurological and psychiatric disorders. Evidence has emerged that large-scale dysconnectivity might play a crucial role in the pathophysiology of schizophrenia, especially regarding cognitive symptoms. Therefore, an analysis of graph and control theoretic measures of large-scale brain connectivity in patients offers to give insight into the emergence of cognitive disturbances in the disorder. To investigate these potential differences between patients with schizophrenia (SCZ), patients with schizoaffective disorder (SCZaff) and matched healthy controls (HC), we used structural MRI data to assess the microstructural organization of white matter. We first calculate seven graph measures of integration, segregation, centrality and resilience and test for group differences. Second, we extend our analysis beyond these traditional measures and employ a simplified noise-free linear discrete-time and time-invariant network model to calculate two complementary measures of controllability. Average controllability, which identifies brain areas that can guide brain activity into different, easily reachable states with little input energy and modal controllability, which characterizes regions that can push the brain into difficult-to-reach states, i.e., states that require substantial input energy. We identified differences in standard network and controllability measures for both patient groups compared to HCs. We found a strong reduction of betweenness centrality for both patient groups and a strong reduction in average controllability for the SCZ group again in comparison to the HC group. Our findings of network level deficits might help to explain the many cognitive deficits associated with these disorders.	en
dc.description.sponsorship	DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlin	de
dc.identifier.eissn	1664-0640
dc.identifier.uri	https://depositonce.tu-berlin.de/handle/11303/13445
dc.identifier.uri	http://dx.doi.org/10.14279/depositonce-12231
dc.language.iso	en	en
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	en
dc.subject.ddc	000 Informatik, Informationswissenschaft, allgemeine Werke	de
dc.subject.other	schizophrenia	en
dc.subject.other	connectome	en
dc.subject.other	graph theory	en
dc.subject.other	control theory	en
dc.subject.other	dysconnectivity	en
dc.title	Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective	en
dc.type	Article	en
dc.type.version	publishedVersion	en
dcterms.bibliographicCitation.articlenumber	669783	en
dcterms.bibliographicCitation.doi	10.3389/fpsyt.2021.669783	en
dcterms.bibliographicCitation.journaltitle	Frontiers in Psychiatry	en
dcterms.bibliographicCitation.originalpublishername	Frontiers	en
dcterms.bibliographicCitation.originalpublisherplace	Lausanne	en
dcterms.bibliographicCitation.volume	12	en
tub.accessrights.dnb	free	en
tub.affiliation	Fak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Neuronale Informationsverarbeitung	de
tub.affiliation.faculty	Fak. 4 Elektrotechnik und Informatik	de
tub.affiliation.group	FG Neuronale Informationsverarbeitung	de
tub.affiliation.institute	Inst. Softwaretechnik und Theoretische Informatik	de
tub.publisher.universityorinstitution	Technische Universität Berlin	en

Structural Differences Between Healthy Subjects and Patients With Schizophrenia or Schizoaffective Disorder: A Graph and Control Theoretical Perspective

Files

Original bundle

License bundle

Collections