Article

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

2026-04-22

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Background</h4> Friedreich ataxia (FRDA) is a rare neurodegenerative disorder with substantial heterogeneity in clinical presentation and progression, complicating prognosis and trial design. Neuroimaging offers objective biomarkers to track disease evolution, yet variability in progression patterns remains poorly understood. <h4>Objective</h4> To identify biologically meaningful FRDA prog...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 51d30a0c-827b-583f-8c28-1a6ee18030b5
DOI: 10.64898/2026.04.21.26351375

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Multimodal MRI and Machine Learning Uncovers Distinct Progression Patterns in Friedreich AtaxiaDOI 10.64898/2026.04.21.26351375Read

Predictive machine learning and multimodal data to develop highly sensitive, composite biomarkers of disease progression in Friedreich AtaxiaSemantic proximity Read

Geometric Brain Signatures for Diagnosing Rare Hereditary Ataxias and Predicting FunctionSemantic proximity Read

Phenotypic categorisation of individual subjects with motor neuron disease based on radiological disease burden patterns: A machine-learning approach.Semantic proximity Read

Unveiling genetic architecture of white matter microstructure through unsupervised deep representation learning of fractional anisotropy mapsSemantic proximity Read

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage InferenceSemantic proximity Read

Unveiling genetic architecture of white matter microstructure through unsupervised deep representation learning of fractional anisotropy mapsSemantic proximity Read

Comprehensive Characterization of Clinical Phenotypes and CTG Repeat-Associated Manifestations in Spinocerebellar Ataxia Type 8.Semantic proximity Read

A Multimodal Multiomics Machine Learning (MMM) approach for biomarker discovery and acceleration of clinical trial readiness for childhood-onset neurological disordersSemantic proximity Read

Neuroanatomical subtypes of Long COVID reveal distinct cognitive profiles alongside longitudinal brain changesSemantic proximity Read

Machine-learning MRI stratification of genetic frontotemporal dementia for clinical trial enrichmentSemantic proximity Read

MRI-based classifier to identify close-to-onset cases in <i>C9orf72</i> genetic frontotemporal dementiaSemantic proximity Read

Cerebellar Normative Modeling Identifies Neuroanatomical Biotypes Predicting dTMS Response in Spinocerebellar Ataxia Type 3Semantic proximity Read