Unsupervised classification of major depression using functional connectivity MRI.

ABSTRACT: The current diagnosis of psychiatric disorders including major depressive disorder based largely on self-reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting-state functional magnetic resonance imaging scans in the absence of clinical information. Twenty-four medication-naive patients with major depression and 29 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting-state functional

SUBMITTER: Zeng LL 

PROVIDER: S-EPMC6869344 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature