Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial.

Journal: NeuroImage. Clinical
PMID: 39142216

Abstract

BACKGROUND: In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process.

Authors

  • Mohsen Ghofrani-Jahromi
    Turner Institute for Brain and Mental Health, Monash University, Clayton VIC3800, Australia.
  • Govinda R Poudel
    Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne VIC3000, Australia.
  • Adeel Razi
  • Pubu M Abeyasinghe
    Turner Institute for Brain and Mental Health, Monash University, Clayton VIC3800, Australia.
  • Jane S Paulsen
    Department of Neurology, University of Wisconsin-Madison, 1685 Highland Avenue, Madison, WI, USA.
  • Sarah J Tabrizi
    UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, UK Dementia Research Institute, Department of Neurodegenerative Diseases, University College London, London, UK.
  • Susmita Saha
    IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia.
  • Nellie Georgiou-Karistianis
    Turner Institute for Brain and Mental Health, Monash University, Clayton VIC3800, Australia. Electronic address: nellie.georgiou-karistianis@monash.edu.

Keywords

External Resources

Popular Topics
Recent Journals