Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
PMID: 39644878

Abstract

OBJECTIVE: To apply machine learning approaches on EEG event-related oscillations (ERO) to discriminate preclinical Alzheimer's disease (AD) from age- and sex-matched controls.

Authors

  • Ke Liao
    Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States. Electronic address: kliao@kumc.edu.
  • Laura E Martin
    Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States; Department of Population Health, University of Kansas Medical Center, Kansas City, KS, United States.
  • Sodiq Fakorede
    Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, KS, United States.
  • William M Brooks
    Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United States; Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States; University of Kansas Alzheimer's Disease Research Center, University of Kansas Medical Center, Kansas City, KS, United States.
  • Jeffrey M Burns
    Alzheimer's Disease Center, University of Kansas Medical Center, Fairway, KS, USA; Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA.
  • Hannes Devos
    Department of Physical Therapy and Rehabilitation Science, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, USA.

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