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

Radiology 2026

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Authors: G. Pontillo, Simone Penna, Filippo Arrigoni, Benjamin Bender, Sylvia Boesch, Arturo Brunetti, Fernando Cendes, Sidhant Chopra, Louise A. Corben, Andreas Deistung, Martin B. Delatycki, Stefano Diciotti
+36 more Imis Dogan, Gary F. Egan, Marcondes C. França, Nellie Georgiou-Karistianis, Sophia Göricke, Pierre-Gilles Henry, Carlos R. Hernandez-Castillo, Diane Hutter, James M. Joers, Christophe Lenglet, Tobias Lindig, Raffaele Lodi, David N. Manners, A. Martinez, Andrea Martinuzzi, Chiara Marzi, Mario Mascalchi, W. Nachbauer, Chiara Pane, Denis Peruzzo, Pramod K. Pishardy, Kathrin Reetz, Thiago J. R. Rezende, Sandro Romanzetti, Francesco Saccà, Ludger Schoels, Jorg B. Schulz, A. Stefani, Matthis Synofzik, Sophia I. Thomopoulos, Paul M. Thompson, Dagmar Timmann, Caterina Tonon, Marinela Vavla, Ian H. Harding, Sirio Cocozza
DOI: 10.1148/radiol.251386
Cited by: 2
References: 40

Abstract

= .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

Fields of Study & Subjects

Genetic Neurodegenerative Diseases Parkinson's Disease Mechanisms and Treatments Amyotrophic Lateral Sclerosis Research

Publication details

Journal / source
Radiology
Publisher
Radiological Society of North America (RSNA)
Volume
318
Issue
3
Pages
e251386–e251386
Publication date
1 March 2026
Language
EN