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Publications

Journal Article Ppublish

PubMed: 41805414 DOI: 10.1148/radiol.251386

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

Radiology 318(3):e251386

Authors: Giuseppe 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, Imis Dogan, Gary F Egan, Marcondes C França, Nellie Georgiou-Karistianis, Sophia L Göricke, Pierre-Gilles Henry, Carlos R Hernandez-Castillo, Diane Hutter, James M Joers, Christophe Lenglet, Tobias Lindig, Raffaele Lodi, David N Manners, Alberto R M Martinez, Andrea Martinuzzi, Chiara Marzi, Mario Mascalchi, Wolfgang Nachbauer, Chiara Pane, Denis Peruzzo, Pramod K Pishardy, Kathrin Reetz, Thiago J R Rezende, Sandro Romanzetti, Francesco Saccà, Ludger Schoels, Jorg B Schulz, Ambra Stefani, Matthis Synofzik, Sophia I Thomopoulos, Paul M Thompson, Dagmar Timmann, Caterina Tonon, Marinela Vavla, Ian H Harding, Sirio Cocozza

Affiliations

  • Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131 Naples, Italy.
  • Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
  • Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
  • Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
  • Enel Group, Sevilla, Spain.
  • Neuroimaging Unit, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy.
  • Department of Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany.
  • Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria.
  • Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil.
  • Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), School of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil.
  • Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.
  • Bruce Lefroy Centre, Murdoch Children's Research Institute, Parkville, VIC, Australia.
  • Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia.
  • University Clinic and Outpatient Clinic for Radiology, Department for Radiation Medicine, University Hospital Halle (Saale), University Medicine Halle, Halle (Saale), Germany.
  • Victorian Clinical Genetics Services, Parkville, VIC, Australia.
  • Department of Electrical, Electronic, and Information Engineering Guglielmo Marconi, University of Bologna, Bologna, Italy.
  • Department of Neurology, RWTH Aachen University, Aachen, Germany.
  • JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Research Center Jülich GmbH, Jülich, Germany.
  • Monash Biomedical Imaging, Monash University, Melbourne, Australia.
  • Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Clayton, VIC, Australia.
  • Institute of Diagnostic and Interventional Radiology and Neuroradiology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen, Germany.
  • Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minn.
  • Faculty of Computer Science, Dalhousie University, Halifax, Canada.
  • Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
  • Functional and Molecular Neuroimaging Unit, IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy.
  • Department for Life Quality Sciences, University of Bologna, Bologna, Italy.
  • Scientific Institute, IRCCS Eugenio Medea, Conegliano-Pieve di Soligo Research Centre, Conegliano, Italy.
  • Department of Statistics, Computer Science, and Applications Giuseppe Parenti, University of Florence, Florence, Italy.
  • Department of Clinical and Experimental Biomedical Sciences Mario Serio, University of Florence, Florence, Italy.
  • Department of Neurosciences and Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.
  • GENESIS Department, University of Naples Federico II, Naples, Italy.
  • Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, Tübingen, Germany.
  • German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
  • Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, Calif.
  • Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen, Germany.
  • Child and Adolescent Neuropsychiatry Unit, Department of Women's and Children's Health, Padua University Hospital, Padua, Italy.
  • QIMR Berghofer Medical Research Institute, Brisbane, Australia.
  • School of Translational Medicine, Monash University, Melbourne, Australia.

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 < .001) and severity (B = 1.404, standard error = 0.201, P < .001), and these relationships were moderated by subtype, with biologic stage progression in the early cerebral subtype mapping less strongly to clinical variables relative to the others (interaction term early cerebral subtype × stage: 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.

MeSH

Atrophy, Brain, Friedreich Ataxia, Algorithms, Radiology, Machine Learning, Brain Stem, Ataxia, Learning, Cerebellar Cortex