Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure

There is considerable interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basi...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 220; S. 116611
Hauptverfasser:	Pavlović, Dragana M., Guillaume, Bryan R.L., Towlson, Emma K., Kuek, Nicole M.Y., Afyouni, Soroosh, Vértes, Petra E., Yeo, B.T. Thomas, Bullmore, Edward T., Nichols, Thomas E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 15.10.2020 Elsevier
Schlagworte:	Brain - diagnostic imaging Community detection Computer Simulation Connectome Default Mode Network - diagnostic imaging Firth estimation Humans Individuality Integrated classification likelihood criterion Likelihood ratio Magnetic Resonance Imaging Mixture models Models, Neurological Models, Statistical Modularity Multi-subject network analysis Nerve Net - diagnostic imaging Network analysis Permutation test Schizophrenia - diagnostic imaging Stochastic block model Stochastic blockmodel Variational approximation Wald test Wald test Stochastic block model Community detection Likelihood ratio Firth estimation Modularity Stochastic blockmodel Permutation test Network analysis Mixture models Integrated classification likelihood criterion Variational approximation Multi-subject network analysis
ISSN:	1053-8119, 1095-9572, 1095-9572
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!