Abstract
In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) – a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date – of schizophrenia and major depression – ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1 ENIGMA's genomic screens – now numbering over 30,000 MRI scans – have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants – and genetic variants in general – may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures – from tens of thousands of people – that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.
| Original language | English |
|---|---|
| Pages (from-to) | 389-408 |
| Number of pages | 20 |
| Journal | NeuroImage |
| Volume | 145 |
| DOIs | |
| Publication status | Published - 15 Jan 2017 |
Funding
This work was supported in part by a Consortium grant (U54 EB 020403) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative, including the NIBIB. Funding for individual consortium authors is listed in Hibar et al., Nature , 2015 and in other papers cited here. This paper was collaboratively written on Google Docs by all authors, over a period of several weeks. We thank Josh Faskowitz for making Fig. 3 , the ENIGMA “roadmap”.
| Funders | Funder number |
|---|---|
| National Institutes of Health | |
| National Institute on Aging | RF1AG041915, R01AG022381, R01AG040060, P30AG028740, R01AG018386, R01AG018384, R01AG050595 |
| Medical Research Council | G1001245, G0701120, MR/K026992/1, MR/M013111/1 |
| Biotechnology and Biological Sciences Research Council | BB/F019394/1 |
| Japan Society for the Promotion of Science | 16H05375 |
| National Institute on Alcohol Abuse and Alcoholism | P01AA019072 |
| National Institute of Mental Health | R00MH101367, R01MH104284, K24MH094614 |
| National Institute of Biomedical Imaging and Bioengineering | R01EB015611, U54EB020403 |
| European Commission | 278948 |
| National Center for Advancing Translational Sciences | UL1TR001863 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 17 Partnerships for the Goals
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