Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies...

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Bibliographic Details
Main Authors: Mohammad S. E. Sendi, Elaheh Zendehrouh, Charles A. Ellis, Zhijia Liang, Zening Fu, Daniel H. Mathalon, Judith M. Ford, Adrian Preda, Theo G. M. van Erp, Robyn L. Miller, Godfrey D. Pearlson, Jessica A. Turner, Vince D. Calhoun
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-03-01
Series:Frontiers in Neural Circuits
Subjects:
dynamic functional connectivity
schizophrenia
default mode network
interpretable machine learning
hidden Markov model
Online Access:https://www.frontiersin.org/articles/10.3389/fncir.2021.649417/full

Internet

https://www.frontiersin.org/articles/10.3389/fncir.2021.649417/full

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