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 S
Identification of Biological Subtypes of Friedreich Ataxia with Structural MRI-based Machine Learning
G. Pontillo·Sirio Cocozza·Sophia Göricke·W. Nachbauer·Sophia I. Thomopoulos·Chiara Pane·Tobias Lindig·Diane Hutter·Sandro Romanzetti·James M. Joers·Denis Peruzzo·Paul Thompson·Fernando Cendes·Kathrin Reetz·A. Martinez·Marinela Vavla·Benjamin Bender·Mario Mascalchi·Christophe Lenglet·Stefano Diciotti·Andreas Deistung·Louise A. Corben·Filippo Arrigoni·Chiara Marzi·Andrea Martinuzzi·Nellie Georgiou-Karistianis·Matthis Synofzik·Marcondes C. França·Gary F. Egan·Sylvia Boesch·Jorg B. Schulz·Pramod K. Pishardy·David N. Manners·Ian H. Harding·Imis Dogan·Carlos R. Hernandez-Castillo·Arturo Brunetti·Thiago J. R. Rezende·Raffaele Lodi·Martin B. Delatycki·Pierre-Gilles Henry·Dagmar Timmann·Caterina Tonon·Francesco Saccà·Ludger Schoels·Simone Penna·S. Chopra·A. Stefani
