Liu Shuqi, Rosso Andrea L, Baillargeon Emma M, Weinstein Andrea M, Rosano Caterina, Torres-Oviedo Gelsy
Sensorimotor Learning Laboratory, University of Pittsburgh, Department of Bioengineering, Pittsburgh, PA, USA.
Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
bioRxiv. 2023 Aug 26:2023.07.31.551290. doi: 10.1101/2023.07.31.551290.
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.
步态自动性是指在最小程度调动通常由前额叶皮层(PFC)介导的注意力网络的情况下行走的能力。步态自动性降低在衰老过程中很常见,会导致跌倒风险增加和生活质量下降。对步态自动性的一种常见评估方法是在双任务(DT)范式中,例如在执行认知任务时行走,使用近红外光谱(fNIRS)检查PFC的激活情况。然而,单独测量PFC活动或任务表现都无法准确衡量自动性。例如,当双任务表现较差时,PFC激活增加可能被解释为步态自动性较差,但当双任务表现较好时,这可能被视为成功的补偿。因此,需要结合行为表现和PFC测量来更全面地评估步态自动性。为满足这一需求,我们提出了一种新颖的自动性指数,作为一种分析方法,将PFC活动的变化与双任务表现的变化相结合,以量化步态自动性。我们在173名年龄≥65岁的参与者中验证了该指数,这些参与者在完成两种难度水平的双任务时,用fNIRS记录PFC激活情况。这两个双任务包括在平坦或不平坦的表面行走时背诵字母表中的每隔一个字母。我们发现,随着双任务难度的增加,与PFC激活相比,更多参与者表现出该新颖指数所测量的自动性预期下降。此外,在个体间比较时,较低的认知功能与较差的自动性指数相关,但与PFC激活或双任务表现无关。总之,所提出的指数通过提供一种包括大脑激活和表现的步态自动性统一测量方法,更好地量化了任务和个体之间自动性的差异。这种新方法为评估参与者特定的缺陷以及比较步态自动性干预的康复结果开辟了令人兴奋的可能性。