Multimodal Neuroimaging-Guided Stratification in Amyotrophic Lateral Sclerosis Reveals Three Disease Subtypes: A Multi-Cohort Analysis

Abstract

Amyotrophic lateral sclerosis (ALS) is a multisystem disease with marked pathophysiological and clinical heterogeneity, making individual and objective characterization of the degree of disease progression and disease-related subtrajectories challenging. Here, we use in vivo multimodal neuroimaging data and computational models to generate personalized indices of ALS progression and subtrajectory. We used structural and diffusion weighted imaging of 691 participants (58% ALS) from two independent ALS data sets (North American and Utrecht cohorts) to extract regional values of grey matter (DM) density and white matter (WM) microstructural integrity. Contrastive trajectory inference (cTI) allowed us to identify and separate latent, multivariate patterns in neuroimaging features highlighting ALS-associated pathological processes, which were used to generate subject-specific indices of disease progression and subtrajectory. Disease subtrajectories were based on distinct patterns of alterations in neuroimaging data considering subjects at different disease progression levels. The neuroimaging-based, personalized index of disease progression is indicative of clinical symptom severity (North American: p < 0.01 and Utrecht: p < 0.01) and displays alignment with the King's College staging system (p = 0.001 and p = 0.002). Three ALS subtrajectories were identified that displayed distinct alterations in the motor, limbic system, and widespread cortical and subcortical changes that also differed in clinical symptom manifestation. Our analysis has shown that neuroimaging data encodes subject-specific, disease-related patterns that can be leveraged to obtain an in vivo proxy of disease progression and putative disease subtype.