Paper Info
Title
Temporal Dynamics And Developmental Maturation Of Salience, Default And Central-Executive Network Interactions Revealed By Variational Bayes Hidden Markov Modeling
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 D 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.
Year
DOI
Venue
2016
10.1371/journal.pcbi.1005138
PLOS COMPUTATIONAL BIOLOGY
Field
DocType
Volume
Developmental maturation,Biology,Markov chain,Artificial intelligence,Salience (language),Hidden Markov model,Cognition,Machine learning,Bayes' theorem
Journal
12
Issue
ISSN
Citations 
12
1553-734X
1
PageRank 
References 
Authors
0.36
0
8
Name
Order
Citations
PageRank
Srikanth Ryali11938.62
Kaustubh Supekar256932.77
Tianwen Chen3905.02
John Kochalka460.83
Weidong Cai593886.65
Jonathan Nicholas610.36
Aarthi Padmanabhan761.25
Vinod Menon81028102.30