enigma for neurorehabilitation: a large-scale meta-analysis
TRANSCRIPT
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
ENIGMA for Neurorehabilitation: A Large-Scale Meta-Analysis Approach to Modeling Neuroimaging, Genetics, and Behavior
Big Data to Predict Rehabilitation Outcomes
Clinical Mo*va*on If we could predict who will 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.
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.”
https://www.nih.gov/precision-medicine-initiative-cohort-program
People are not “One-Size-Fits-All”
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
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.
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
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.
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
http://enigma.ini.usc.edu
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
ENIGMA Working Groups • Psychiatry/Mental Health
– Depression, Bipolar Disorder, Schizophrenia • Stroke Recovery • Epilepsy • Multiple Sclerosis • Parkinson’s Disease • Traumatic Brain Injury • Autism • Etc.
ENIGMA Stroke Recovery
Initial Goals: 1. Examine the relationship between post-
stroke neuroanatomical changes and motor behavior
2. Establish a reliable infrastructure to support future large-scale analyses (cognition, language, gait; multimodal imaging; stimulation; epigenetics)
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)
1. Compute brain measures from scans (harmonized protocols for image analysis + QC; 185 institutions)
What do ENIGMA members do with their scans?
3. GWAS*: Test associations between brain measures and 1,000,000+ SNPs
(harmonized protocols for genetic imputation, QC, + analysis)
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)
Anatomical MRI: Cortical+ subcortical volumes; FreeSurfer / FSL
DTI: FA, MD for Tracts and ROIs Defined on ENIGMA-DTI template
ENIGMA Stroke Recovery Methods • 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
Subc
ortic
al
Examples of Cortical and Subcortical Segmentations
Cor
tical
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>300-‐3000 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
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
Crowd sourcing lesion tracing with BrainBox! http://brainbox.pasteur.fr
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.
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 large-scale 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
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
• 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 1U54EB020403-01 to PT for ENIGMA
• NIH K12 Rehabilitation Research Career Development Award HD055929; AHA NIRG 16IRG26960017 to SLL JOIN US!
