Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics

The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM...

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Vydáno v:	NeuroImage (Orlando, Fla.) Ročník 244; s. 118618
Hlavní autoři:	Jeong, Seok-Oh, Kang, Jiyoung, Pae, Chongwon, Eo, Jinseok, Park, Sung Min, Son, Junho, Park, Hae-Jeong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 01.12.2021 Elsevier Limited Elsevier
Témata:	Adolescent Algorithms Approximation Attention Deficit Disorder with Hyperactivity - diagnostic imaging Attention deficit hyperactivity disorder Bayes Theorem Bayesian analysis Brain - diagnostic imaging Brain dynamics Brain mapping Child Child, Preschool Children Computer Simulation Datasets Empirical Bayes Energy Energy landscape Entropy Executive Function Female Functional magnetic resonance imaging Hierarchical Bayesian parameter estimation Humans Information processing Magnetic Resonance Imaging Male Maximum entropy model Neural networks Neuroimaging Nonlinear Dynamics Parameter estimation Random variables Resting state Simulation Time series Variational Bayes Variational expectation-maximization Young Adult Empirical Bayes Resting state Variational Bayes Variational expectation-maximization Brain dynamics Hierarchical Bayesian parameter estimation Energy landscape Maximum entropy model
ISSN:	1053-8119, 1095-9572, 1095-9572
On-line přístup:	Získat plný text
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