Using machine learning, researchers have identified novel, distinct patterns of coordinated activity between brain regions in people with major depressive disorder – even when different protocols are used to detect these brain networks. Ayumu Yamashita of Advanced Telecommunications Research Institutes International in Kyoto, Japan, and colleagues present these findings in the open-access journal PLOS Biology.
The machine-learning method identified key functional connections in the imaging data that could serve as a brain network signature for major depression. Indeed, when the researchers applied that new signature to rs-fMRI data collected at different institutions from 521 other people, they achieved 70 percent accuracy in identifying which of those new people had major depressive disorder.
【MORE】