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Updated: Jun 24, 2025

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A machine learning approach for potential Super-Agers identification using neuronal functional connectivity networks.

Mohammad Fili1, Parvin Mohammadiarvejeh1,2, Brandon S Klinedinst3

  • 1School of Industrial Engineering and Management Oklahoma State University Stillwater Oklahoma USA.

Alzheimer'S & Dementia (Amsterdam, Netherlands)
|June 11, 2024
PubMed
Summary
This summary is machine-generated.

A new algorithm, Optimal Labeling with Bayesian Optimization (OLBO), accurately identifies "Positive-Agers" who maintain superior cognitive function despite aging. This machine learning approach uses resting-state functional magnetic resonance imaging (rsfMRI) and demographic data to distinguish resilient aging brains.

Keywords:
Bayesian optimizationSuper‐Agersclassificationcognitive declinersresting state functional MRI (rsfMRI)

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Aging is frequently linked to cognitive decline, impacting brain function.
  • Identifying neural factors associated with cognitive resilience in aging is crucial for understanding brain health.
  • Distinguishing between individuals with superior cognitive abilities (Positive-Agers) and those experiencing decline offers insights into aging brain mechanisms.

Purpose of the Study:

  • To develop an optimal labeling mechanism for differentiating between Positive-Agers and Cognitive Decliners.
  • To identify Positive-Agers using neuronal functional connectivity networks and demographic data.
  • To establish a mathematical definition for cognitive classes based on cognitive tests.

Main Methods:

  • Utilized principal component analysis to define latent cognitive trajectory groups.
  • Developed a hybrid machine learning and optimization algorithm, Optimal Labeling with Bayesian Optimization (OLBO).
  • Employed unsupervised learning with logistic regression and Bayesian updating on resting-state functional magnetic resonance imaging (rsfMRI) data from 6369 UK Biobank participants.

Main Results:

  • The OLBO algorithm achieved an 88% area under the curve (AUC) in distinguishing Positive-Agers from Cognitive Decliners.
  • OLBO demonstrated superior performance compared to baseline models in classifying cognitive trajectories.
  • The posterior default mode network showed the most significant impact on the odds of Positive-Aging.

Conclusions:

  • The Optimal Labeling with Bayesian Optimization (OLBO) algorithm is a novel and accurate method for distinguishing cognitive trajectories.
  • This approach can identify cognitively resilient individuals with high precision using neuroimaging and demographic data.
  • Findings highlight the potential of machine learning in understanding and predicting cognitive aging patterns.