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

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.