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Springer Nature [academic journals on nature.com], Translational Psychiatry, 1(10), 2020

DOI: 10.1038/s41398-020-0842-6

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ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Journal article published in 2020 by Nic van der Wee, Lianne Schmaal ORCID, Elena Pozzi, Tiffany C. Ho, Tiffany Ho, Laura S. van Velzen, Ilya M. Veer, Nils Opel, Eus J. W. Van Someren, Laura K. M. Han ORCID, Lybomir Aftanas, André Aleman, Bernhard T. Baune, Klaus Berger, Tessa F. Blanken ORCID and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

AbstractA key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.