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Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques.

Sravani Varanasi1, Roopan Tuli2, Fei Han3

  • 1Department of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD 21250, USA.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

This study identifies brain connectivity signatures to distinguish young and old adults, revealing differences in attention and frontal networks. These findings aid in understanding aging brains and monitoring treatment effectiveness.

Keywords:
brain connectivityenergy landscapefMRImachine learningresting state network

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Medical Imaging

Background:

  • Brain connectivity is crucial for understanding brain function and identifying biomarkers for neurological disorders.
  • Age-related changes in brain connectivity can serve as indicators for cognitive aging and potential therapeutic interventions.

Purpose of the Study:

  • To investigate age-related changes in brain connectivity using advanced modeling techniques.
  • To identify distinct aging signatures in brain networks differentiating between young and old adults.
  • To explore the potential of these signatures in evaluating neural disorders and treatment efficacy.

Main Methods:

  • Utilized an energy-based machine learning technique to analyze functional MRI data from young and old adults.
  • Generated disconnectivity graphs and activation maps for seven prominent resting-state networks (RSNs).
  • Applied two-sample t-tests with Bonferroni correction to identify significant differences in connectivity states (local minimums) between age groups.

Main Results:

  • Identified specific connectivity states as aging signatures capable of differentiating between young and old subjects.
  • Found that the attention network exhibits stronger average connectivity among its regions in younger individuals.
  • Observed a common pattern in the frontal network where left and right regions sometimes function independently in both age groups.

Conclusions:

  • Combined machine learning and statistical methods effectively extracted brain connectivity signatures indicative of aging.
  • These signatures hold potential for distinguishing between aging brains and monitoring the efficacy of treatments for neurological conditions.