ArticleBrain communications2024
Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers.
Article in Brain communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.NPJ digital medicine · 2026Article
- Predicting Post-Stroke Aphasia Speech Performance from Multimodal Data with Explainable Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Closed-loop brain-computer interfaces for post-stroke sensorimotor loop restoration.Frontiers in neuroscience · 2026Review
- Integrating individualized connectome with amyloid pathology improves predictive modeling of future cognitive decline.Communications medicine · 2025Article
- Collaborative AI for precision neurorehabilitation: a roadmap.Journal of neuroengineering and rehabilitation · 2025Review
- Taking steps toward open data in motor control, learning, and development.Journal of motor learning and development · 2025Article
Corrections and comments
- Update of
Authors and funding
32 authors.
Funding
Abstract
Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.