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 #
5U54EB020403-03
Application #
9108711
Study Section
Special Emphasis Panel (ZRG1)
Project Start
Project End
Budget Start
2016-06-01
Budget End
2017-05-31
Support Year
3
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Southern California
Department
Type
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90032
Zhang, Jie; Tu, Yanshuai; Li, Qingyang et al. (2018) MULTI-TASK SPARSE SCREENING FOR PREDICTING FUTURE CLINICAL SCORES USING LONGITUDINAL CORTICAL THICKNESS MEASURES. Proc IEEE Int Symp Biomed Imaging 2018:1406-1410
Walton, E; Hibar, D P; van Erp, T G M et al. (2018) Prefrontal cortical thinning links to negative symptoms in schizophrenia via the ENIGMA consortium. Psychol Med 48:82-94
Logue, Mark W; van Rooij, Sanne J H; Dennis, Emily L et al. (2018) Smaller Hippocampal Volume in Posttraumatic Stress Disorder: A Multisite ENIGMA-PGC Study: Subcortical Volumetry Results From Posttraumatic Stress Disorder Consortia. Biol Psychiatry 83:244-253
Kochunov, Peter; Dickie, Erin W; Viviano, Joseph D et al. (2018) Integration of routine QA data into mega-analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies. Hum Brain Mapp 39:1015-1023
Riedel, Brandalyn C; Daianu, Madelaine; Ver Steeg, Greg et al. (2018) Uncovering Biologically Coherent Peripheral Signatures of Health and Risk for Alzheimer's Disease in the Aging Brain. Front Aging Neurosci 10:390
Hibar, D P; Westlye, L T; Doan, N T et al. (2018) Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol Psychiatry 23:932-942
Savransky, Anya; Chiappelli, Joshua; Fisseha, Feven et al. (2018) Elevated allostatic load early in the course of schizophrenia. Transl Psychiatry 8:246
Dennis, Emily L; Babikian, Talin; Giza, Christopher C et al. (2018) Neuroimaging of the Injured Pediatric Brain: Methods and New Lessons. Neuroscientist 24:652-670
Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh et al. (2018) A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol. Brain Imaging Behav :
Wu, Jianfeng; Zhang, Jie; Shi, Jie et al. (2018) HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS. Proc IEEE Int Symp Biomed Imaging 2018:1555-1559

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