Our comprehensive Data Science Research program is organized into 4 complementary Algorithm Research Cores: (1) Imaging Genomics, (2) Connectomics, (3) Machine Learning &Clinical Prediction, and (4) ENIGMA Disease Working Groups. World leaders in each field lead each Core, tackling computational questions on a scale not previously imagined or attempted. BigData tools will fuel ENIGMA'S worldwide scientific discoveries. We combine innovations in mathematics, machine learning, genomics, consortium science, and expertise from >20 countries and >125 institutions. ENIGMA is not a project, it is a scientific movement of rapidly and constantly Interacting collaborations that support each other. ENIGMA cohorts boost each other's power with gigantic datasets, tools and expertise to maximally exploit each other's data, performing some of the world's largest disease studies, beyond what any one site could perform alone. ENIGMA is distributed computation at its best, drawing on gigantic datasets and expertise. We create massive economic savings - drawing on worldwide computational and infrastructural resources vastly beyond what any one site in any one country would apply to a targeted biomedical goal. We bring BigData Science and the ENIGMA Consortium together to advance Worldwide Medicine. In Cores 1 and 2 we mine images and connectomes for genetic markers that re-wire the brain or boost brain tissue loss, using new mathematics to prioritize and organize trillions of computations, jointly searching images and genomes. In Core 3, we unleash multi-task sparse learning to predict diagnosis and prognosis from vast high-dimensional biomarker data in the largest neuroimaging genetics datasets ever. In Core 4 we use these tools in a massive distributed computation: a vast, mutually interacting set of 9 Worldwide Working Groups, led by experts in 9 major diseases of the brain - schizophrenia, bipolar, major depression, ADHD, autism, OCD, 22q deletion syndrome, HIV/AIDS, and addictions. We will discover what genes, medications, and lifestyle factors promote or resist brain disease worldwide. Our ENIGMA Center is a worldwide movement in mutually supportive discovery in medicine - spurred on by tools to perform gigantic computations never before Imagined.

Public Health Relevance

ENIGMA'S Data Science Research brings together 125 institutions to investigate and compare 9 major brain diseases. ENIGMA'S worldwide reach brings together biomedical data and experts from over 20 countries to analyze Big Data in a consistent way. Our harmonized work brings unequalled power to discover genes, medications, and lifestyle factors affecting brain disease worldwide. New algorithms probe genomes, connectomes, and biomarker data using deep searches, multi-task learning, and combinatorics.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54EB020403-01
Application #
8905093
Study Section
Special Emphasis Panel (ZRG1-BST-N (52))
Project Start
Project End
Budget Start
2014-09-29
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
$1,414,307
Indirect Cost
$414,915
Name
University of Southern California
Department
Type
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
Jia, Tianye; Macare, Christine; Desrivières, Sylvane et al. (2016) Neural basis of reward anticipation and its genetic determinants. Proc Natl Acad Sci U S A 113:3879-84
Zhang, Jie; Stonnington, Cynthia; Li, Qingyang et al. (2016) APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE. Proc IEEE Int Symp Biomed Imaging 2016:646-650
Boedhoe, Premika S W; Schmaal, Lianne; Abe, Yoshinari et al. (2016) Distinct Subcortical Volume Alterations in Pediatric and Adult OCD: A Worldwide Meta- and Mega-Analysis. Am J Psychiatry :appiajp201616020201
Cameron Craddock, R; S Margulies, Daniel; Bellec, Pierre et al. (2016) Brainhack: a collaborative workshop for the open neuroscience community. Gigascience 5:16
Everaerd, Daphne; Klumpers, Floris; Zwiers, Marcel et al. (2016) Childhood abuse and deprivation are associated with distinct sex-dependent differences in brain morphology. Neuropsychopharmacology 41:1716-23
Kochunov, Peter; Thompson, Paul M; Winkler, Anderson et al. (2016) The common genetic influence over processing speed and white matter microstructure: Evidence from the Old Order Amish and Human Connectome Projects. Neuroimage 125:189-97
Colom, Roberto; Hua, Xue; Martínez, Kenia et al. (2016) Brain structural changes following adaptive cognitive training assessed by Tensor-Based Morphometry (TBM). Neuropsychologia 91:77-85
Hasan, Khader M; Mwangi, Benson; Cao, Bo et al. (2016) Entorhinal Cortex Thickness across the Human Lifespan. J Neuroimaging 26:278-82
Hibar, D P; Westlye, L T; van Erp, T G M et al. (2016) Subcortical volumetric abnormalities in bipolar disorder. Mol Psychiatry 21:1710-1716
Shen, Kai-Kai; Doré, Vincent; Rose, Stephen et al. (2016) Heritability and genetic correlation between the cerebral cortex and associated white matter connections. Hum Brain Mapp 37:2331-47

Showing the most recent 10 out of 163 publications