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
Credit: Ayumu Yamashita
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.
To address these challenges, Yamashita and colleagues used machine learning to analyze brain network data from 713 people, 149 of whom had major depression. These data had been collected using a technique called resting-state functional MRI (rs-fMRI), which detects brain activity and produces images that reveal coordinated activity, or “functional connections,” between different parts of the brain. The imaging had been performed at different institutions using different protocols.
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.
The researchers hope that their new brain network signature, which can be applied across different imaging protocols, could serve as a foundation for discovering brain network patterns associated with subtypes of depression, and to reveal relationships between depression and other disorders. A better understanding of brain network connections in major depression could help match patients to effective treatments and inform development of new treatments.
Peer reviewed; Experimental; Humans
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Citation: Yamashita A, Sakai Y, Yamada T, Yahata N, Kunimatsu A, Okada N, et al. (2020) Generalizable brain network markers of major depressive disorder across multiple imaging sites. PLoS Biol 18(12): e3000966. https:/
Funding: This study was conducted under the contract research “Brain/MINDS Beyond” Grant Number JP18dm0307008, supported by the Japan Agency for Medical Research and Development (AMED) while using data obtained from the database project supported by”Development of BMI Technologies for Clinical Application” of the Strategic Research Program for Brain Sciences JP17dm0107044 (AMED). This study was also supported by Grant Number JP18dm0307002, JP18dm0307004, and JP19dm0307009 (AMED). M.K., H.I. and A.Y. were partially supported by the ImPACT Program of the Council for Science, Technology and Innovation (Cabinet Office, Government of Japan). K.K. was partially supported by the International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS) and JSPS KAKENHI 16H06280 (Advanced Bioimaging Support). H.I. was partially supported by JSPS KAKENHI 18H01098, 18H05302, and 19H05725. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: M.K., N.Y., R.H., H.I., N.K. and K.K are inventors of a patent owned by Advanced Telecommunications Research (ATR) Institute International related to the present work [PCT/JP2014/061543 (WO2014178322)]. M.K., N.Y., R.H., N.K. and K.K. are inventors of a patent owned by ATR Institute International related to the present work [PCT/JP2014/061544 (WO2014178323)]. M.K. and N.Y. are inventors of a patent application submitted by ATR Institute International related to the present work [JP2015-228970]. A.Y. and M.K. are inventors of a patent application submitted by ATR Institute International related to the present work [JP2018-192842].
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