Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.

ABSTRACT: The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural

SUBMITTER: Gallo S 

PROVIDER: S-EPMC10615764 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature