Uncovering Predictors of Low Hippocampal Volume: Evidence from a Large-Scale Machine-Learning-Based Study in the UK Biobank.

Journal: Neuroepidemiology
PMID: 38560977

Abstract

INTRODUCTION: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk factors of lower hippocampal volume using information from the world's largest brain imaging study.

Authors

  • Yigizie Yeshaw
    Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia, yigizie.mihiretie@mymail.unisa.edu.au.
  • Iqbal Madakkatel
    Australian Centre for Precision Health, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia. iqbal.madakkatel@unisa.edu.au.
  • Anwar Mulugeta
    Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia.
  • Amanda Lumsden
    Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia.
  • Elina Hyppönen
    Australian Centre for Precision Health, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia. Elina.Hypponen@unisa.edu.au.

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