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

Pontillo G, Penna S, Arrigoni F, Bender B, Boesch S, Brunetti A, Cendes F, Chopra S, Corben LA, Deistung A, Delatycki MB, Diciotti S, Dogan I, Egan GF, França MC Jr, Georgiou-Karistianis N, Göricke SL, Henry PG, Hernandez-Castillo CR, Hutter D, Joers JM, Lenglet C, Lindig T, Lodi R, Manners DN, Martinez ARM, Martinuzzi A, Marzi C, Mascalchi M, Nachbauer W, Pane C, Peruzzo D, Pishardy PK, Reetz K, Rezende TJR, Romanzetti S, Saccà F, Schoels L, Schulz JB, Stefani A, Synofzik M, Thomopoulos SI, Thompson PM, Timmann D, Tonon C, Vavla M, Harding IH, Cocozza S. Identification of Biological Subtypes of Friedreich Ataxia with Structural MRI-based Machine Learning. Radiology. 2026 Mar;318(3):e251386. doi: 10.1148/radiol.251386. PMID: 41805414. 

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

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