Multimodal MRI and Machine Learning Uncovers Distinct Progression Patterns in Friedreich Ataxia

Multimodal MRI and Machine Learning Uncovers Distinct Progression Patterns in Friedreich Ataxia. Susmita Saha, Nellie Georgiou-Karistianis, Vivienne Teo, David J. Szmulewicz, Lachlan T. Strike, Marcondes C. França Jr., Thiago J.R. Rezende, Ian H. Harding. medRxiv 2026.04.21.26351375; doi:10.64898/2026.04.21.26351375 

Data-driven clustering of longitudinal MRI identified distinct FRDA subtypes with unique co-progression patterns, underscoring genetic burden as a key driver. Recognising such heterogeneity can improve patient stratification, enable personalised monitoring, and guide targeted therapeutic strategies. Future studies should validate these subtypes in larger, more diverse cohorts and integrate additional biomarkers for enhanced precision.

Multimodal MRI and Machine Learning Uncovers Distinct Progression Patterns in Friedreich Ataxia