Machine Learning Identifies New Brain Network Signature of Major Depression

upstart writes:

Machine learning identifies new brain network signature of major depression: Novel signature could improve understanding of depression and its subtypes, and how to treat them:

While major depression is usually straightforward to diagnose, a better understanding of the brain networks associated with depression could improve treatment strategies. Machine-learning algorithms can be applied to data on brain activity in people with depression in order to find such associations. However, most studies have focused only on specific subtypes of depression, or they have not accounted for the differences in brain imaging protocols between healthcare institutions.

[...] 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.

Journal Reference:
Ayumu Yamashita, Yuki Sakai, Takashi Yamada, et al. Generalizable brain network markers of major depressive disorder across multiple imaging sites, PLOS Biology (DOI: 10.1371/journal.pbio.3000966)

Original Submission

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