Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder

Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder

BackgroundA number of mental illness is often re-diagnosed to be bipolar disorder (BD). Furthermore, the prefronto-limbic-striatal regions seem to be associated with the main dysconnectivity of BD. Functional connectivity is potentially an appropriate objective neurobiological marker that can assist...

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Bibliographic Details
Main Authors: Yen-Ling Chen, Pei-Chi Tu, Tzu-Hsuan Huang, Ya-Mei Bai, Tung-Ping Su, Mu-Hong Chen, Yu-Te Wu
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-10-01
Series:Frontiers in Neuroscience
Subjects:
classification
bipolar disorder
functional connectivity
feature selection
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2020.563368/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2020.563368/full

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