Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling.
Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling.
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差异贝叶斯隐藏的马尔可夫建模揭示的显着性，默认和中央连续网络相互作用的时间动力和发展成熟。
DOI：
10.1371/journal.pcbi.1005138
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发表时间：
2016-12
影响因子：
4.3
通讯作者：
Menon V
中科院分区：
文献类型：
--
作者：
Ryali S;Supekar K;Chen T;Kochalka J;Cai W;Nicholas J;Padmanabhan A;Menon V
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. Characterizing the temporal dynamics of functional interactions between distributed brain regions is of fundamental importance for understanding human brain organization and its development. Progress in the field has been hampered both by a lack of strong computational techniques to investigate brain dynamics and an inadequate focus on core brain systems involved in higher-order cognition. Here we address these gaps by developing a novel variational Bayesian Hidden Markov Model (VB-HMM) that uncovers non-stationary dynamical functional networks in human fMRI data. In two cohorts of adults, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between the salience (SN), default mode (DMN), and central executive (CEN) networks—three brain systems critical for higher-order cognition. In children, relative to adults, VB-HMM revealed immature dynamic interactions between SN, CEN, and DMN, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our findings suggest that the flexibility of switching between distinct brain states is weaker in childhood, and they provide a novel framework for modeling immature brain network organization in children. More generally, the approach used here may prove useful to the investigation of dynamic brain organization in neurodevelopmental and psychiatric disorders.
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DOI：
10.1523/jneurosci.2825-10.2010
发表时间：
2010-11-17
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Hwang K;Velanova K;Luna B
通讯作者：
Luna B
DOI：
10.1111/ejn.12764
发表时间：
2015-01
期刊：
The European journal of neuroscience
影响因子：
--
作者：
Chen T;Michels L;Supekar K;Kochalka J;Ryali S;Menon V
通讯作者：
Menon V
影响因子：
2.7
作者：
Di X;Biswal BB
通讯作者：
Biswal BB
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
5.3
作者：
Cai, Weidong;Ryali, Srikanth;Menon, Vinod
通讯作者：
Menon, Vinod