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PLoS Comput Biol. 2016 Dec 13;12(12):e1005138. doi: 10.1371/journal.pcbi.1005138. eCollection 2016 Dec.

Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling.

Author information

1
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, United States of America.
2
Department of Neurology and Neurological Sciences, Stanford University, Stanford, United States of America.
3
Stanford Neurosciences Institute, Stanford University, Stanford, United States of America.

Abstract

Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of interactions between salience (SN), default mode (DMN), and central executive (CEN) networks-three brain systems that play a critical role in human cognition. In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN, CEN, and DMN. Furthermore, the three "static" networks occurred in a segregated state only intermittently. Findings were replicated in two adult cohorts from the Human Connectome Project. VB-HMM further revealed immature dynamic interactions between SN, CEN, and DMN in children, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our computational techniques provide new insights into human brain network dynamics and its maturation with development.

PMID:
27959921
PMCID:
PMC5154470
DOI:
10.1371/journal.pcbi.1005138
[Indexed for MEDLINE]
Free PMC Article

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