What question did this study set out to answer?

The aim is to identify biologically meaningful subtypes of Friedreich ataxia using machine learning on MRI data.

March 13, 2026Radiology0 citations

Identification of Biological Subtypes of Friedreich Ataxia with Structural MRI-based Machine Learning

Q: What does this research mean for the field?

Three distinct structural MRI-based subtypes of Friedreich ataxia (FRDA) were identified, each with different patterns of brain degeneration and associations with clinical severity. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

GPG. Pontillo SPSimone Penna FAFilippo Arrigoni

Key Points

The aim is to identify biologically meaningful subtypes of Friedreich ataxia using machine learning on MRI data.
Multicenter secondary analysis of prospectively acquired MRI and clinical data.
Used the Subtype and Stage Inference (SuStaIn) algorithm for stratification.
Identified MRI-based subgroups and analyzed their clinical and genetic relevance.
Three subtypes identified: classical, early cerebral, and early cerebellar.
Classical subtype constituted 66.5% (183 of 275 participants).
Early cerebral subtype showed less correlation between disease stage and clinical severity.
More advanced disease stages related to greater symptom duration and severity.

Abstract

Background Friedreich ataxia (FRDA) is an inherited, progressive neurodegenerative disease. Interindividual heterogeneity in the rate and phenotypic profile of disease progression indicates a biologic variability in the pattern and spatial evolution of underlying changes, but the occurrence of possible FRDA subgroups, which could aid in clinical trial design and treatment, are still unknown. Purpose To obtain a structural MRI-based stratification of participants with FRDA using the Subtype and Stage Inference (SuStaIn) algorithm and determine whether these subgroups are biologically meaningful and clinically relevant. Materials and Methods This multicenter secondary analysis of prospectively acquired data included structural MRI and clinical-demographic data from participants from the ENIGMA-Ataxia working group. MRI biomarkers were analyzed using the SuStaIn algorithm to identify subgroups with distinct patterns and disease stages. The clinical and genetic relevance of these subgroups were assessed within a linear model framework. Results This study included 565 participants (mean age, 32 years ± 13.1 SD; 286 women; 275 participants with FRDA and 290 healthy controls). SuStaIn identified three subtypes: (a) a classical subtype (66.5% 183 of 275 participants), characterized by an ascending gradient of damage from brainstem to cerebellar cortex to cerebrum; (b) an early cerebral subtype (25.8% 71 of 275 participants) with cerebral atrophy preceding the involvement of cerebellar cortex; and (c) and an early cerebellar subtype (7.64% 21 of 275 participants) showing cerebellar lobule atrophy before upper brainstem or cerebral involvement. More advanced disease stages (MRI-based SuStaIn stages) correlated with greater symptom duration (unstandardized coefficient B = 0.422, standard error = 0.065, P B = 1.404, standard error = 0.201, P B = -0.925, standard error = 0.410, P = .02). Conclusion Using the SuStaIn algorithm, three distinct structural MRI-based subtypes of FRDA were identified, with different patterns of brain degeneration and associations with clinical severity. © RSNA, 2026 Supplemental material is available for this article.

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