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Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural

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Artificial intelligence reveals age-related brain changes during motor tasks. Elderly adults show altered neural network activity, suggesting increased cognitive effort due to working memory decline.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Large-scale functional connectivity is crucial for brain function and diagnosing neurological disorders.
  • Age-related changes in neural activity impact cognitive processes and motor control.
  • Artificial intelligence offers novel methods for analyzing complex brain connectivity patterns.

Purpose of the Study:

  • To investigate age-related changes in functional brain connectivity during a simple motor task using artificial intelligence.
  • To identify specific alterations in neural response patterns between younger and older adults.
  • To correlate observed connectivity changes with cognitive functions like working memory.

Main Methods:

  • Utilized whole-scalp electroencephalography (EEG) to record brain activity in twenty subjects across two age groups performing repetitive motor tasks.
  • Applied a feed-forward multilayer perceptron model to analyze functional connectivity between sensor groups over key brain regions (frontal, parietal, motor cortex).
  • Evaluated functional dependence using coefficient of determination and statistical analysis to identify significant network features.

Main Results:

  • Functional connectivity patterns in elderly adults showed pronounced theta-band network activation and decreased mu-band activation in frontal-parietal and motor areas.
  • Between-subject analysis indicated strengthened inter-areal task-relevant links in older adults.
  • Findings align with theories of healthy aging impacting neural activation and cognitive load.

Conclusions:

  • Age-related decline in working memory may increase cognitive demand for simple motor tasks in elderly individuals.
  • AI-driven analysis of EEG provides valuable insights into the neural mechanisms underlying cognitive aging.
  • Altered functional connectivity patterns serve as potential biomarkers for age-related cognitive changes.