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Related Experiment Video

Updated: Jun 9, 2026

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

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Published on: November 4, 2025

Data-driven subtyping and staging of ALS: A multicenter, longitudinal, deformation-based morphometry study.

Isabelle Lajoie^1,2, Sanjay Kalra^3,4, Mahsa Dadar^1,2

¹Douglas Mental Health University Health Centre, Montreal, Quebec, Canada.

Imaging Neuroscience (Cambridge, Mass.)

|June 8, 2026

Summary

This summary is machine-generated.

This study identifies four distinct subtypes of Amyotrophic Lateral Sclerosis (ALS) using advanced imaging analysis. These subtypes show different disease progression, brain atrophy patterns, and survival rates, aiding in personalized patient care.

Keywords:

longitudinal machine learning progression modeling subtype and stage inference

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Area of Science:

Neuroscience
Medical Imaging
Biostatistics

Background:

Amyotrophic lateral sclerosis (ALS) is a complex neurodegenerative disease with significant heterogeneity.
Existing imaging-based subtyping methods for ALS often lack longitudinal validation and clear links to clinical outcomes.
Understanding ALS heterogeneity is crucial for developing targeted therapies and improving prognostication.

Purpose of the Study:

To identify and validate distinct ALS subtypes and disease stages using deformation-based morphometry (DBM) and the Subtype and Stage Inference (SuStaIn) model.
To characterize the cross-sectional and longitudinal imaging, clinical, cognitive, and survival profiles of these identified subtypes.
To establish a robust, longitudinally validated model for ALS heterogeneity.

Main Methods:

Utilized DBM to analyze regional brain atrophy in 198 ALS patients and 144 healthy controls from the CALSNIC cohort.
Applied the SuStaIn model to infer subtypes and stages based on baseline DBM w-scores.
Assessed longitudinal consistency of subtype/stage assignments and compared imaging/clinical trajectories using mixed-effects models and survival analyses.

Main Results:

SuStaIn identified a normal-appearing group (S0) and three distinct ALS atrophy subtypes (S1, S2, S3).
Subtypes differed in atrophy patterns (e.g., motor-dominant, limbic-onset, fronto-parietal), clinical progression (ALSFRS-R decline, FVC reduction), and survival.
SuStaIn stage strongly correlated with overall brain atrophy and disease progression, demonstrating high longitudinal consistency (>90%).

Conclusions:

The SuStaIn model provides a robust, longitudinally validated framework for understanding ALS heterogeneity.
Identified ALS subtypes represent distinct disease trajectories, enabling better patient stratification and prognostication.
This approach supports the use of SuStaIn for biologically informed patient stratification and clinical trial enrichment in ALS research.