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

Researchers implementing non-invasive neuroimaging have reported distinct load-dependent brain activity patterns in older adults compared with younger adults. Although findings are mixed, these age-related patterns are often associated with compensatory mechanisms of cognitive decline even in the absence of direct comparisons between brain activity and cognitive performance. This study investigated the effects of cognitive load on brain-behavior relationships in younger and older adults using a data-driven, multivariate partial least squares (PLS) analysis of functional near-infrared spectroscopy (fNIRS) data. We measured bilateral prefrontal brain activity in 31 older and 27 younger adults while they performed single and dual 2-back tasks. Behavioral PLS analysis was used to determine relationships between performance metrics (reaction time and error rate) and brain oxygenation (HbO) and deoxygenation (HbR) patterns across groups and task loads. Results revealed significant age-group differences in brain-behavior relationships. In younger adults, increased brain activity (i.e., increased HbO and decreased HbR) was associated with faster reaction times and better accuracy in the single task, indicating sufficient neural capacity. Conversely, older adults showed a negative correlation between HbR and error rates in the single task; however, in the dual task, they demonstrated a positive relationship between HbO and performance, indicative of compensatory mechanisms under the higher cognitive load. Overall, older adults' showed relationships with either HbR or HbO, but not both, indicating that the robustness of the relationship between brain activity and behavior varies across task load conditions. Our PLS approach revealed distinct load-dependent brain activity between age groups, providing further insight into neurocognitive aging patterns, such as compensatory mechanisms, by emphasizing the variability and complexity of brain-behavior relationships. Our findings also highlight the importance of considering task complexity and cognitive demands in interpreting age-related brain activity patterns.