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

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

2020
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Overview
A 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.