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

G Pontillo; S Penna; F Arrigoni; B Bender; S Boesch; A Brunetti; F Cendes; S Chopra; LA Corben; A Deistung; MB Delatycki; S Diciotti; I Dogan; GF Egan; MC França; N Georgiou-Karistianis; SL Göricke; PG Henry; CR Hernandez-Castillo; D Hutter; JM Joers; C Lenglet; T Lindig; R Lodi; DN Manners; ARM Martinez; A Martinuzzi; C Marzi; M Mascalchi; W Nachbauer; C Pane; D Peruzzo; PK Pishardy; K Reetz; TJR Rezende; S Romanzetti; F Saccà; L Schoels; JB Schulz; A Stefani; M Synofzik; SI Thomopoulos; PM Thompson; D Timmann; C Tonon; M Vavla; IH Harding; S Cocozza

Journal article

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

G Pontillo, S Penna, F Arrigoni, B Bender, S Boesch, A Brunetti, F Cendes, S Chopra, LA Corben, A Deistung, MB Delatycki, S Diciotti, I Dogan, GF Egan, MC França, N Georgiou-Karistianis, SL Göricke, PG Henry, CR Hernandez-Castillo, D Hutter Show all

Radiology | Published : 2026

DOI: 10.1148/radiol.251386

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..

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