Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach

Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach

IntroductionDeveloping a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC)...

Full description

Bibliographic Details
Main Authors: Baoyu Yan, Xiaopan Xu, Mengwan Liu, Kaizhong Zheng, Jian Liu, Jianming Li, Lei Wei, Binjie Zhang, Hongbing Lu, Baojuan Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-03-01
Series:Frontiers in Neuroscience
Subjects:
sliding window
dynamic brain connectivity
static brain connectivity
resting state
machine learning
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2020.00191/full

Internet

https://www.frontiersin.org/article/10.3389/fnins.2020.00191/full

Similar Items