Gait automaticity refers to the ability to walk with minimal recruitment of attentional networks typically mediated through the prefrontal cortex (PFC). Reduced gait automaticity is common with aging, contributing to an increased risk of falls and reduced quality of life. A common assessment of gait automaticity involves examining PFC activation using near-infrared spectroscopy (fNIRS) during dual-task (DT) paradigms, such as walking while performing a cognitive task. However, neither PFC activity nor task performance in isolation measures automaticity accurately. For example, greater PFC activation could be interpreted as worse gait automaticity when accompanied by poorer DT performance, but when accompanied by better DT performance, it could be seen as successful compensation. Thus, there is a need to incorporate behavioral performance and PFC measurements for a more comprehensive evaluation of gait automaticity. To address this need, we propose a novel automaticity index as an analytical approach that combines changes in PFC activity with changes in DT performance to quantify gait automaticity. We validated the index in 173 participants (≥65 y/o) who completed DTs with two levels of difficulty while PFC activation was recorded with fNIRS. The two DTs consisted of reciting every other letter of the alphabet while walking over either an even or uneven surface. We found that as DT difficulty increases, more participants showed the anticipated decrease in automaticity as measured by the novel index compared to PFC activation. Furthermore, when comparing across individuals, lower cognitive function related to worse automaticity index, but not PFC activation or DT performance. In sum, the proposed index better quantified the differences in automaticity between tasks and individuals by providing a unified measure of gait automaticity that includes both brain activation and performance. This new approach opens exciting possibilities to assess participant-specific deficits and compare rehabilitation outcomes from gait automaticity interventions.
Keywords: aging; brain imaging; cognition; community mobility; interference; locomotion; motor control; near-infrared spectroscopy.