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Published on: March 4, 2014
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Multimodal Neuroimaging-Guided Stratification in Amyotrophic Lateral Sclerosis Reveals Three Disease Subtypes: A
Tobias R Baumeister1,2,3, Henk-Jan Westeneng4, Leonard van den Berg4
1Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Quebec, Canada.
Human Brain Mapping
|September 24, 2025
Summary
This study uses neuroimaging and computational models to create personalized measures of Amyotrophic Lateral Sclerosis (ALS) progression and subtypes. These advanced tools offer objective insights into disease advancement and individual patient trajectories.
Area of Science:
- Neuroimaging and Computational Biology
- Neurology and Neuroscience
- Biomedical Data Science
Background:
- Amyotrophic Lateral Sclerosis (ALS) presents significant heterogeneity, complicating objective assessment of disease progression and patient subtrajectories.
- Current methods for characterizing ALS progression lack individual objectivity, hindering personalized treatment strategies.
Purpose of the Study:
- To develop personalized, in vivo indices of ALS progression and disease subtrajectories using multimodal neuroimaging data and computational models.
- To correlate neuroimaging-derived progression indices with clinical severity and established staging systems.
- To identify distinct ALS disease subtrajectories based on neuroimaging patterns and their clinical manifestations.
Main Methods:
- Utilized structural and diffusion-weighted imaging data from 691 participants (58% with ALS) across two independent cohorts (North American and Utrecht).
- Extracted regional grey matter density and white matter microstructural integrity metrics.
- Applied contrastive trajectory inference (cTI) to identify latent patterns in neuroimaging features, enabling the generation of subject-specific progression indices and disease subtrajectories.
Main Results:
- Developed a personalized, neuroimaging-based index of ALS disease progression that significantly correlates with clinical symptom severity (p < 0.01) and aligns with the King's College staging system (p < 0.002).
- Identified three distinct ALS subtrajectories characterized by specific patterns of neuroimaging alterations (motor, limbic, and widespread cortical/subcortical) associated with differing clinical symptom presentations.
- Demonstrated that neuroimaging data encodes subject-specific, disease-related patterns that serve as an in vivo proxy for disease progression and potential subtypes.
Conclusions:
- Neuroimaging data, analyzed with computational models, can generate personalized indices for ALS progression and identify distinct disease subtrajectories.
- These findings offer a novel, objective, in vivo approach to characterizing individual ALS patient status and potential disease subtypes.
- The developed methodology holds promise for advancing personalized medicine in ALS management and research.
