Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity

The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In this study, we developed a spatiotemporal graph conv...

Full description

Bibliographic Details
Main Authors:	Gao, S. (Author), Hou, Z. (Author), Kong, Y. (Author), Shu, H. (Author), Xie, C. (Author), Yuan, Y. (Author), Yue, Y. (Author), Zhang, Z. (Author)
Format:	Article
Language:	English
Published:	John Wiley and Sons Inc 2021
Subjects:	adult Adult antidepressant agent Article BOLD signal brain Brain brain region cohort analysis conceptual framework connectome Connectome controlled study convolutional neural network Deep Learning Depressive Disorder, Major diagnostic accuracy diagnostic imaging diagnostic test accuracy study dimensionality reduction functional connectivity functional magnetic resonance imaging graph convolutional network human Humans individual diagnosis Magnetic Resonance Imaging major clinical study major depression major depressive disorder nerve cell network Nerve Net nuclear magnetic resonance imaging pathophysiology predictive value procedures prognosis Prognosis sensitivity and specificity spatiotemporal analysis treatment response treatment response prediction
Online Access:	View Fulltext in Publisher

Internet

View Fulltext in Publisher

Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity

Internet

Similar Items