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Related Experiment Video

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Machine learning identifies novel markers predicting functional decline in older adults.

Kate E Valerio1, Sarah Prieto1, Alexander N Hasselbach1

  • 1Department of Psychology, The Ohio State University, Columbus, OH 43210, USA.

Brain Communications
|July 21, 2021
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Summary

Machine learning identified key predictors of functional decline in older adults. Neurocognitive tests and specific biomarkers, like partner-reported language difficulties, best forecast future declines in daily living activities.

Keywords:
ADNIIADLangular gyruseveryday cognitionmachine learning

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Area of Science:

  • Gerontology
  • Neuroscience
  • Artificial Intelligence

Background:

  • Instrumental activities of daily living (IADLs) decline with age, but individual differences are significant.
  • Understanding predictors of IADL decline is crucial for interventions to maintain independence in older adults.
  • Previous research identified cognitive, neuroimaging, and fluid biomarkers, but a comprehensive approach is needed.

Purpose of the Study:

  • To utilize machine learning to identify a broad range of baseline variables that predict functional decline over two years.
  • To compare the predictive power of different data modalities (demographics, MRI, PET, neurocognitive, biomarkers) for functional decline.
  • To pinpoint specific variables across modalities that are most effective in predicting future functional decline.

Main Methods:

  • Applied machine learning, specifically Support Vector Machine classification, to data from 398 cognitively normal or mild cognitive impairment individuals.
  • Analyzed five distinct data modalities: demographics, structural MRI, fluorodeoxyglucose-PET, neurocognitive assessments, and genetic/fluid biomarkers.
  • Used variable selection techniques to identify the most predictive individual variables for functional decline within a two-year timeframe.

Main Results:

  • Neurocognitive measures showed the highest accuracy (74.2%) in predicting functional decline, followed by fluorodeoxyglucose-PET (70.8%).
  • Key individual predictors identified were partner-reported language from the Everyday Cognition questionnaire, ADAS13 scores, and left angular gyrus activity on PET.
  • These top three variables collectively accounted for 32% of the variance in functional decline.

Conclusions:

  • Machine learning effectively identified novel biomarkers associated with semantic processing that predict future functional decline.
  • Findings suggest the potential clinical utility of the Everyday Cognition questionnaire as a cost-effective tool for predicting IADL decline.
  • The study highlights the power of integrating diverse data modalities for a comprehensive understanding of age-related functional decline.