Speaker
Description
The increasing availability of large clinical neuroimaging datasets has enabled the discovery of objective, data-driven clinical phenotypes. However, current approaches typically rely on structural or static functional metrics, thereby overlooking the rich information embedded in brain dynamics. To overcome this limitation, we developed a computational framework combining Gaussian Hidden Markov Model (HMM) and Optimal Transport (OT) theory. By modeling brain activity as a sequence of recurrent latent brain states, the HMM defines a shared state space with state-specific mean activation and connectivity patterns. Each subject is represented by an occupancy distribution over these states and by the probabilities of transitioning between them. OT is then used to estimate the cost of transforming one subject's occupancy distribution into another's, with the transformation constrained by subject-specific transition probabilities. This yields a pairwise dissimilarity measure that captures inter-individual variability in brain dynamics.
To demonstrate clinical utility, we applied this framework to multiple sclerosis (MS), where substantial clinical heterogeneity challenges traditional classification. We analyzed resting-state fMRI data from 122 relapsing-remitting MS patients and 97 matched healthy controls. Although patients and controls differed in fractional occupancies and dwell times across states, we focused on characterizing heterogeneity within the MS cohort. Clustering patients according to OT-based dissimilarities revealed two subgroups with distinct clinical disability and structural pathology profiles. Crucially, this stratification was not detectable when applying clustering procedures to static functional connectivity matrices, demonstrating the added value of dynamical features.
The broader applicability of the framework was further assessed in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. In this independent dataset, our approach identified subgroups of Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients with distinct structural and cognitive profiles.
Together, these results support a generalizable methodological framework for extracting meaningful subtypes from temporal brain dynamics, with potential applications across phenotypically heterogeneous neurological conditions.
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