Identifying resting‐state effective connectivity abnormalities in drug‐naïve major depressive disorder diagnosis via graph convolutional networks

• Published in: Human brain mapping Vol. 41; no. 17; pp. 4997 - 5014
• Main Authors: Jun, Eunji, Na, Kyoung‐Sae, Kang, Wooyoung, Lee, Jiyeon, Suk, Heung‐Il, Ham, Byung‐Joo
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2020
• Subjects: Abnormalities; Artificial neural networks; Brain; Brain mapping; deep learning; Diagnosis; effective connectivity; Functional magnetic resonance imaging; graph convolutional networks (GCNs); Magnetic resonance imaging; major depressive disorder (MDD); Medical imaging; Mental depression; Neural networks; Neuroimaging; Phenotypes; resting‐state functional magnetic resonance imaging (rs‐fMRI); Sensitivity analysis; Signs and symptoms; Sparse Group LASSO; deep learning; Sparse Group LASSO; resting-state functional magnetic resonance imaging (rs-fMRI); graph convolutional networks (GCNs); major depressive disorder (MDD); effective connectivity

Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use...