Evidence map›Paper›PMID 41113678›Full record

ArticleBrain communications2025

Machine learning integration of MRI and gait reveals mobility phenotypes in multiple sclerosis.

Hernan Inojosa, Wanqi Zhao, Judith Wenk, Dirk Schriefer, Stefanie Fischer, Peter Heisig, Maren Kählig, Hannes Schlieter, Heidi Stölzer-Hutsch, Isabel Voigt and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Hernan InojosaCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.ORCID https://orcid.org/0000-0002-1377-836X
Wanqi ZhaoChair of Software Technology, TUD Dresden University of Technology, Dresden 01062, Germany.
Judith WenkCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.
Dirk SchrieferCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.
Stefanie FischerCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.
Peter HeisigChair of Software Technology, TUD Dresden University of Technology, Dresden 01062, Germany.
Maren KähligChair of Software Technology, TUD Dresden University of Technology, Dresden 01062, Germany.
Hannes SchlieterChair of Software Technology, TUD Dresden University of Technology, Dresden 01062, Germany.
Heidi Stölzer-HutschCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.
Isabel VoigtCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.
Annika KatherCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.
Rocco HaaseCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.
Katja AkgünCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.
Hagen H KitzlerDepartment of Neuroradiology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.
Katrin TrentzschCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.ORCID https://orcid.org/0000-0003-2832-4640
Uwe AßmannChair of Software Technology, TUD Dresden University of Technology, Dresden 01062, Germany.
Karsten WendtChair of Software Technology, TUD Dresden University of Technology, Dresden 01062, Germany.
Tjalf ZiemssenCenter of Clinical Neuroscience, Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden 01307, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mobility impairment is a hallmark of disease worsening in multiple sclerosis (MS), yet its phenotypic diversity and pathophysiology mechanisms are not completely understood. Conventional gait assessments often rely on subjective clinical measures, which may not fully capture the complexity of gait abnormalities. The integration of advanced quantitative gait analysis, quantitative from MRI, and machine learning (ML) may reveal unique mobility phenotypes, potentially reflecting underlying disease mechanisms and heterogeneity. In this study, we aimed to identify and characterize mobility phenotypes among people with MS (pwMS) using a mixed approach with spatiotemporal gait parameters and MRI-derived features, supported by unsupervised ML clustering. 1026 pwMS underwent comprehensive gait assessments and quantitative MRI between 2018 and 2023. Principal component analysis was applied for dimensionality reduction and k-means clustering to identify distinct phenotypes. Clusters were compared using demographic, clinical, and MRI features, with statistical comparisons performed using Kruskal-Wallis and Chi-square tests. Four gait clusters were identified. Cluster 1 (faster stable, 47.8%), demonstrated the most efficient gait features and highest grey matter fractions. Cluster 4 (slow severely unstable, 7.4%) showed profound disability, shortest strides, lowest velocity, and greatest variability. Intermediate clusters 2 (slower stable, 32.3%) and 3 (moderately unstable, 12.6%) had similar velocity but differed in cadence and stride length. Cluster 3, marked by shorter steps and increased cadence, showed higher lesion burden and lower brain parenchymal fraction, suggesting emerging structural impairment and possible compensatory gait. Clinical measures aligned with these findings: unstable Clusters 3 and 4 had the highest proportion of progressive MS, worst disability scores, longest disease duration, and greatest self-reported gait impairment. Integrating quantitative MRI metrics with spatiotemporal gait analysis has the potential to phenotype clinical impairments in pwMS. ML-driven analysis identified a novel intermediate cluster, distinguished by a gait with increased cadence and shorter strides, alongside distinct MRI abnormalities. This pattern may reflect a potential adaptation within the mobility spectrum, not yet conclusively discernible by human raters but detectable through ML.

Indexed as

mobility phenotypingmultimodal analysismultiple sclerosisquantitative MRIunsupervised clustering

Identifiers

PMID41113678
PMCPMC12529069

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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.