# Big Data Neuroimaging to Predict Motor Behavior After Stroke

> **NIH NIH K01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $133,191

## Abstract

PROJECT SUMMARY
 Stroke is a leading cause of serious long-term adult disability around the world. Despite intensive therapy,
an estimated 2/3 of stroke survivors do not fully recover and are left unable to care for themselves
independently. Growing research suggests that rehabilitation is not “one-size-fits-all”; variability among stroke
survivors in terms of lesion location, age, gender, time since stroke and more may all affect a person's
likelihood of recovery and response to different types of treatments. Personalized rehabilitation medicine to
maximize each individual's recovery potential is thus desperately needed. However, in order to develop
accurate, robust, and specific predictive models that can determine an individual's recovery potential and
response to different treatments, large, heterogeneous datasets are needed. The current best predictors of
stroke outcomes are neuroimaging (MRI) and behavioral biomarkers that look at brain structure/function and
motor performance at baseline. Generating a large enough dataset of MRI and behavioral data is extremely
difficult and expensive for any one site to do on its own. This proposal addresses this problem by generating a
large, diverse dataset using a novel meta-analytic approach that harmonizes post-stroke data collected
worldwide. In partnership with an international consortium comprised of over 500 researchers who produce the
largest-known neuroimaging and genetic studies of over 18 different diseases (ENIGMA Center for Worldwide
Medicine, Imaging, and Genomics), I propose to apply ENIGMA's powerful approach to answer critical
questions in stroke recovery. Under this K01 career development award, I will develop skills in big data
neuroimaging analytics, clinical research, and consortium building through my ENIGMA Stroke Recovery
working group in order to ask questions about stroke recovery using a large dataset approach (goal n>3,000).
This project has four specific aims: Aim 1 will leverage ENIGMA's existing methodology to develop the
infrastructure, optimal methods, and analysis techniques for harmonizing a large dataset of post-stroke MRI
and behavioral data. Aim 2 will use this large dataset to identify neural and behavioral biomarkers predicting
recovery of motor impairment (e.g., actual arm movement ability) and recovery of function (e.g., ability to
perform tasks, such as picking up objects with the affected arm). Aim 3 will use supervised machine learning
to generate and fine-tune highly accurate predictive models of the relationship between these biomarkers and
recovery of impairment versus function. Lastly, Aim 4 will use unsupervised machine learning techniques to
examine shared properties of outliers from the predictive model and determine additional neurobiological
mechanisms that may prevent individuals from recovering. This approach has the potential to revolutionize the
way that rehabilitation research is validated, to ensure robust, reliable, and reproducible results. T...

## Key facts

- **NIH application ID:** 9888377
- **Project number:** 5K01HD091283-04
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Sook-Lei Liew
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $133,191
- **Award type:** 5
- **Project period:** 2017-04-01 → 2020-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9888377

## Citation

> US National Institutes of Health, RePORTER application 9888377, Big Data Neuroimaging to Predict Motor Behavior After Stroke (5K01HD091283-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9888377. Licensed CC0.

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