Machine Learning for Missing Data Imputation in Alzheimer's Research: Predicting Medial Temporal Lobe Flexibility.

Journal: bioRxiv : the preprint server for biology
Published Date:

Abstract

BACKGROUND: Alzheimer's disease (AD) begins years before symptoms appear, making early detection essential. The medial temporal lobe (MTL) is one of the earliest regions affected, and its network flexibility, a dynamic measure of brain connectivity, may serve as a sensitive biomarker of early decline. Cognitive (acquisition, generalization), genetic (APOE, ABCA7), and biochemical (P-tau217) markers may predict MTL dynamic flexibility. Given the high rate of missing data in AD research, this study uses machine learning with advanced imputation methods to predict MTL dynamic flexibility from multimodal predictors in an aging cohort.

Authors

  • Soodeh Moallemian
  • Abolfazl Saghafi
  • Rutvik Deshpande
  • Jose M Perez
  • Miray Budak
  • Bernadette A Fausto
  • Fanny Elahi
  • Mark A Gluck

Keywords

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