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Using multivariate partial least squares on fNIRS data to examine load-dependent brain-behaviour relationships in

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Older adults show distinct brain activity patterns during cognitive tasks, suggesting compensatory mechanisms for cognitive aging. This study highlights age-related differences in brain-behavior relationships under varying cognitive loads.

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Aging Research

Background:

  • Non-invasive neuroimaging reveals age-related differences in brain activity patterns.
  • These patterns are often linked to compensatory mechanisms for cognitive decline in aging.
  • Direct comparisons between brain activity and cognitive performance are often lacking.

Purpose of the Study:

  • To investigate the impact of cognitive load on brain-behavior relationships in younger and older adults.
  • To analyze functional near-infrared spectroscopy (fNIRS) data using a data-driven approach.
  • To explore age-group differences in prefrontal brain activity during cognitive tasks.

Main Methods:

  • Utilized functional near-infrared spectroscopy (fNIRS) to measure prefrontal brain activity.
  • Employed a multivariate partial least squares (PLS) analysis on data from 31 older and 27 younger adults.
  • Assessed performance metrics (reaction time, error rate) during single and dual 2-back tasks.

Main Results:

  • Significant age-group differences in brain-behavior relationships were observed.
  • Younger adults showed neural efficiency (increased HbO, decreased HbR) linked to better performance.
  • Older adults exhibited compensatory mechanisms, with distinct HbO/HbR relationships depending on task load.

Conclusions:

  • Cognitive load significantly modulates brain-behavior relationships differently across age groups.
  • Older adults demonstrate compensatory neural activity, particularly under higher cognitive demands.
  • Findings underscore the complexity of neurocognitive aging and the importance of task complexity in interpretation.