# A global effort to benchmark predictive models and reveal mechanistic diversity in long-term stroke outcomes

## TL;DR

This paper presents a global collaboration to improve stroke outcome predictions using diverse data and models, highlighting key factors like brain imaging and demographics.

## Contribution

The study introduces a collaborative benchmarking framework for stroke outcome prediction, revealing mechanistic diversity across cognitive, motor, and emotional domains.

## Key findings

- Key predictors of stroke outcomes include infarct characteristics, T1-weighted MRI, and demographic factors.
- FLAIR imaging and white matter tract analysis improve predictions for cognitive and motor outcomes, respectively.
- Collaborative data science can lead to personalized care strategies for stroke survivors.

## Abstract

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover ‘optimal recipes’ for predicting each domain. The differences in these ‘optimal recipes’ reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com.