Generalizable brain network markers of major depressive disorder across multiple imaging sites.

ABSTRACT: Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% g

SUBMITTER: Yamashita A 

PROVIDER: S-EPMC7721148 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature