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Email

l.corben@unimelb.edu.au

Credentials


Position
Honorary (Principal Fellow)
Department of Paediatrics
ORCID

0000-0001-8767-4582

Dr Louise Corben

Honorary (Principal Fellow)
Department of Paediatrics

172 Scholarly works
1 Projects

HIGHLIGHTS

  • 2026

    Journal article

    Identification of Biological Subtypes of Friedreich Ataxia with Structural MRI-based Machine Learning
    DOI: 10.1148/radiol.251386
  • 2026

    Research grants (international)

    Using Diffusion-Weighted MRI Metric of the Auditory System as a Biomarker for Freidreich Ataxia Disease Progression
  • 2026

    Journal article

    Peripheral frataxin levels govern long-term clinical progression in Friedreich ataxia
    DOI: 10.1136/bmjno-2026-001561
  • 2025

    Journal article

    Predictive machine learning and multimodal data to develop highly sensitive, composite biomarkers of disease progression in Friedreich ataxia
    DOI: 10.1038/s41598-025-01047-6
  • 2025

    Journal article

    Neuroimaging Biomarkers for Friedreich Ataxia: A Cross-Sectional Analysis of the TRACK-FA Study
    DOI: 10.1002/ana.27237
  • 2025

    Journal article

    Friedreich's ataxia—a rare multisystem disease
    DOI: 10.1016/S1474-4422(25)00175-9
  • 2025

    Journal article

    Goal-Directed Rehabilitation Versus Standard Care for Individuals with Hereditary Cerebellar Ataxia: A Multicenter, Single-Blind, Randomized Controlled Superiority Trial
    DOI: 10.1002/ana.27130
Louise Corben

RECENT SCHOLARLY WORKS

  • 2025

    Journal article

    Omaveloxolone for the Treatment of Friedreich Ataxia: Efficacy, Safety, and Future Perspectives
    DOI: 10.1002/mds.30070
  • 2025

    Journal article

    Enhancing the Objective Assessment of Friedreich Ataxia Severity: A Multiview IMU-Based Approach
    DOI: 10.1109/EMBC58623.2025.11253596
  • 2025

    Journal article

    Machine Learning Approach for Quantifying Hereditary Cerebellar Ataxia Severity and Evaluating Rehabilitation Effectiveness
    DOI: 10.1109/EMBC58623.2025.11253019
  • 2025

    Journal article

    Automated Deep Learning-based Segmentation of the Dentate Nucleus Using Quantitative Susceptibility Mapping MRI
    DOI: 10.1148/ryai.240478

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