Using machine learning, researchers have identified novel, distinct patterns of coordinated activity between different parts of the brain 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. 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.

 

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