ENIGMA for Neurorehabilitation: A Large-Scale Meta-Analysis Approach to Modeling Neuroimaging, Genetics, and Behavior

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ENIGMA for Neurorehabilitation: A Large-Scale Meta-Analysis Approach to Modeling Neuroimaging, Genetics, and Behavior Sook-Lei Liew, PhD, OTR/L Director, Neural Plasticity and Neurorehabilitation

1 ENIGMA for Neurorehabilitation: A Large-Scale Meta-Analysis Approach to Modeling Neuroimaging, Genetics, and Behavior Sook-Lei Liew, PhD, OTR/L Director, Neural Plasticity and Neurorehabilitation Laboratory Assistant Professor Chan Division of Occupational Science & Occupational Therapy Division of Biokinesiology & Physical Therapy Department of Neurology Stevens Neuroimaging and Informatics Institute University of Southern California ASNR Symposium November 11, 2016

2 Big Data to Predict Rehabilitation Outcomes

Clinical If we could predict who M willo*va*on recover and

success for each individual, we could do better at: 1.

3 Clinical If we could predict who M willo*va*on recover and who will not, and what treatments have the best chance of success for each individual, we could do better at: 1. Personalizing our therapeutic interventions to each individual s recovery potential. 2. Driving the discovery of new therapeutic interventions for those who don t respond to anything at present.

4 Precision Medicine Initiative Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. While some advances in precision medicine have been made, the practice is not currently in use for most diseases. That s why on January 20, 2015, President Obama announced the Precision Medicine Initiative (PMI) in his State of the Union address. Through advances in research, technology and policies that empower patients, the PMI will enable a new era of medicine in which researchers, providers and patients work together to develop individualized care.

5 People are not One-Size-Fits-All

7 Rehabilitation is noisy We want to measure brain variables on motor recovery (and maybe genetics too) But other things affect recovery too: State variables motivation, attention, fatigue, depression, family/life events Trait variables personality, time since stroke, age/gender/comorbidities Other sources of noise actual treatment/training time, outcome measurement

8 Prac*cal Issues 1. There is huge heterogeneity in post-stroke recovery and response to treatments à inconsistent results. 2. To overcome heterogeneity, you need a lot of data. 3. The best biomarkers of stroke recovery have been neuroimaging and initial motor behavior scores. 4. To make accurate predictive models, we need really big datasets of neuroimaging and motor behavior in stroke. 5. This costs a lot of time, effort, and money.

9 ENIGMA Center for Worldwide Medicine, Imaging and Genomics Enhancing Neuro Imaging Gene*cs through Meta- Analysis Named a=er ENIGMA Code- Breaking Project (1944) cryptographers

10 Goals of ENIGMA The overall goal of ENIGMA is to unite the brain imaging and genomics communi*es worldwide to solve biomedical problems that no one group could answer alone.

11 ENIGMA Consor*um Largest- Ever Worldwide Analysis of Brain Scans and Gene*c Data 53,000+ people from over 35 countries studying 18 brain diseases

ENIGMA Membership 2016: Worldwide Consor*um 500+ co- authors, 185+ ins*tu*ons, 100+ cohorts; 30+ workgroups / 18 diseases Data is harmonized using robust analysis protocols shared

12 ENIGMA Membership 2016: Worldwide Consor*um 500+ co- authors, 185+ ins*tu*ons, 100+ cohorts; 30+ workgroups / 18 diseases Data is harmonized using robust analysis protocols shared across sites (freely available online: h[p://enigma.ini.usc.edu) Individuals can analyze data at their own sites or can contribute anonymized raw data, which is processed for them

13 ENIGMA Working Groups Psychiatry/Mental Health Depression, Bipolar Disorder, Schizophrenia Stroke Recovery Epilepsy Multiple Sclerosis Parkinson s Disease Traumatic Brain Injury Autism Etc.

14 ENIGMA Stroke Recovery Initial Goals: 1. Examine the relationship between poststroke neuroanatomical changes and motor behavior 2. Establish a reliable infrastructure to support future large-scale analyses (cognition, language, gait; multimodal imaging; stimulation; epigenetics)

15 ENIGMA Stroke Recovery Long Term Goals: 1. Develop accurate, specific, sensitive large-scale predictive models of stroke recovery and response to treatments that can inform clinical decision making. 2. Test and evaluate existing hypotheses regarding neurobiological mechanisms of stroke recovery (reproducibility, reliability) 3. Generate new hypotheses to inform prospective studies (e.g., using machine learning, testing subgroups)

What do ENIGMA members do with their scans? 1.

Anatomical MRI: Cortical+ subcortical volumes; FreeSurfer / FSL DTI: FA, MD for Tracts and ROIs

Mega/Meta-analyses: combine effects across sites: each site s vote depends on the sample size (make

16 What do ENIGMA members do with their scans? 1. Compute brain measures from scans (harmonized protocols for image analysis + QC; 185 institutions) Anatomical MRI: Cortical+ subcortical volumes; FreeSurfer / FSL DTI: FA, MD for Tracts and ROIs Defined on ENIGMA-DTI template 2. Mega/Meta-analyses: combine effects across sites: each site s vote depends on the sample size (make sure effects are reproducible, boosts power to pick up effects no site could pick on its own) 3. GWAS*: Test associations between brain measures and 1,000,000+ SNPs (harmonized protocols for genetic imputation, QC, + analysis)

ENIGMA Stroke Recovery Methods Examples of Cortical and Subcortical

weighted anatomical MRIs Predic-ng motor score: Fugl- Meyer, Wolf Motor

% of max score Covariates: Age, sex, *me since stroke, hemisphere aﬀected,

17 ENIGMA Stroke Recovery Methods Examples of Cortical and Subcortical Segmentations Cortical Regression using regions of interest from T1- weighted anatomical MRIs Predic-ng motor score: Fugl- Meyer, Wolf Motor Func*on Test, Ac*on Research Arm Test, NIHSS, etc. % of max score Covariates: Age, sex, *me since stroke, hemisphere aﬀected, intracranial volume 10,000 permuta*ons were used to obtain a non- parametric es*mate of the sta*s*cal signiﬁcance Subcortical

ENIGMA Stroke Recovery Methods First ENIGMA working group

Manual marking of lesion eﬀects on QCs 2.

Manually hand- trace lesions in n>300-3000 stroke MRIs

tracers Compare accuracy of all exis*ng automated

18 ENIGMA Stroke Recovery Methods First ENIGMA working group with issue of lesion volume 1. Manual marking of lesion eﬀects on QCs 2. ATLAS: Anatomical Tracings of Lesions A=er Stroke Goal: Manually hand- trace lesions in n> stroke MRIs Calculate inter- and intra- rater reliability for all tracers Compare accuracy of all exis*ng automated segmenta*ons Reﬁne current methods with greater training dataset Archive dataset and make available for other researchers to use

19 ENIGMA Stroke Recovery Methods 1. Making data open source through data archiving 2. Automated lesion segmenta*on challenge with online automated evalua*on of algorithm performance

20 Crowd sourcing lesion tracing with BrainBox!

21 Findings Smaller, individual sites yield weak and inconsistent results Pooling data offers greater robustness in identifying neuroanatomical correlates of post-stroke motor behavior Subgroup analyses by lesioned hemisphere offer greater detail into often ignored populations Data discovery - genetic influences: The putamen most strongly correlated with motor outcomes, and was the strongest genetic hit in a separate GWAS (Hibar et al., 2015, Nature). Now setting up a genetic overlap test between ENIGMA GWAS and a stroke GWAS.

22 Future Plans 1. Run first major analyses (n>3000) by early 2017 Develop/test all cross-sectional/longitudinal tools for current planned analyses including regression + machine learning Refine automated lesion tracing (or manually trace all brains) Putamen: Genetic overlap between ENIGMA2 GWAS + Stroke GWAS 2. Establish a reliable infrastructure for future largescale analyses Other forms of recovery - cognition, language, gait Multimodal imaging (diffusion MRI, resting state; CT) Cross-disorder analyses (e.g., depression, epilepsy) Treatment responses (e.g., noninvasive brain stimulation) Genetics/epigenetics Expand beyond stroke into other rehabilitation fields

23 CHECK US OUT ONLINE! enigma.ini.usc.edu Over 30 ENIGMA Working Groups: Major Depression, PTSD (new), Bipolar Disorder, Addictions, Schizophrenia, Autism, and more

Acknowledgements Paul Thompson, PhD Neda Jahanshad, PhD Chris Whelan, PhD

Cramer, MD Catherine Lang, PhD, PT ENIGMA Stroke Recovery Sites Julia

ICT Imaging Genetics Center, Laboratory of NeuroImaging NIH BD2K Center

24 Acknowledgements Paul Thompson, PhD Neda Jahanshad, PhD Chris Whelan, PhD Steve C. Cramer, MD Catherine Lang, PhD, PT ENIGMA Stroke Recovery Sites Julia Anglin, BS Catherine Tran, MS William Nakamura, BS ATLAS team; USC NPNL, ICT Imaging Genetics Center, Laboratory of NeuroImaging NIH BD2K Center Grant 1U54EB to PT for ENIGMA NIH K12 Rehabilitation Research Career Development Award HD055929; AHA NIRG 16IRG to SLL JOIN US!

Large-scale classification of major depressive disorder via distributed Lasso

Large-scale classification of major depressive disorder via distributed Lasso Dajiang Zhu a, Qingyang Li b, Brandalyn C. Riedel a, Neda Jahanshad a, Derrek P. Hibar a, Ilya M. Veer h, Henrik Walter h,