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Behavioral Clusters and Lesion Distributions in Ischemic Stroke, Based on NIHSS Similarity Network
Louis Fabrice Tshimanga1,2,3, Andrea Zanola1,2, Silvia Facchini1
1Department of Neuroscience, University of Padua, Padua, 35128 Italy.
Journal of Healthcare Informatics Research
|July 29, 2025
Summary
This study developed a new unsupervised method to group stroke patients by symptoms using National Institutes of Health Stroke Scale (NIHSS) scores. The data-driven approach reveals distinct brain lesion patterns corresponding to these symptom subgroups.
Area of Science:
- Neuroscience
- Computational Biology
- Medical Informatics
Background:
- Stroke is a major cause of death and disability, with symptoms varying based on brain lesion location.
- Current analysis methods struggle to fully capture the complex relationship between brain lesions and resulting patient dysfunctions.
- Understanding these connections is crucial for improving patient care, rehabilitation, and fundamental knowledge of brain function.
Purpose of the Study:
- To introduce a novel unsupervised framework for stratifying stroke patients into clinically coherent subgroups based on behavioral symptom profiles.
- To identify the distinct neural correlates associated with these data-driven patient subgroups.
- To validate the framework's ability to uncover clinically relevant lesion-symptom relationships.
Main Methods:
- Modeled National Institutes of Health Stroke Scale (NIHSS) assessments as ordinal feature vectors to capture symptom prevalence, severity, and covariance.
- Utilized Repeated Spectral Clustering on a behavioral similarity network to discover stable patient subgroups.
- Performed voxel-wise lesion analysis to determine the neuroanatomical signatures unique to each identified subgroup.
Main Results:
- The unsupervised framework successfully stratified stroke survivors into clinically coherent subgroups based solely on NIHSS scores.
- Emergent clusters corresponded to well-documented neurological syndromes, validating the data-driven approach.
- Voxel-wise analysis revealed distinct, group-specific anatomical lesion locations, even in cases of overlapping average lesion maps, indicating functional substrate differentiation.
Conclusions:
- The developed framework offers a significant methodological advancement for stroke symptom phenotyping and lesion pattern analysis.
- The method provides robust, clinically relevant insights and demonstrates mathematical transparency, supporting its generalization to other biomarkers.
- Open-source code is provided to ensure reproducibility and encourage broader application in biomedical research.
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