Kirby Krystal M, Pillai Sreekrishna, Brouillette Robert M, Keller Jeffrey N, De Vito Alyssa N, Bernstein John P, Van Gemmert Arend W A, Carmichael Owen T
Fine Motor Control and Learning Laboratory (FMCL), School of Kinesiology, Louisiana State University, Baton Rouge, LA, United States.
Biomedical Imaging Center, Pennington Biomedical Research Center, Baton Rouge, LA, United States.
Front Aging Neurosci. 2021 Feb 18;13:630049. doi: 10.3389/fnagi.2021.630049. eCollection 2021.
Prior research has suggested that measurements of brain functioning and performance on (tasks which require simultaneous performance) are promising candidate predictors of fall risk among older adults. However, no prior study has investigated whether brain function measurements during dual task performance could improve prediction of fall risks and whether the type of subtasks used in the dual task paradigm affects the strength of the association between fall characteristics and dual task performance. In this study, 31 cognitively normal, community-dwelling older adults provided a self-reported fall profile (number of falls and fear of falling), completed a gait dual task (spell a word backward while walking on a GaitRite mat), and completed a supine dual task (rhythmic finger tapping with one hand while completing the AX continuous performance task (AX-CPT) with the other hand) during functional magnetic resonance imaging (fMRI). Gait performance, AX-CPT reaction time and accuracy, finger tapping cadence, and brain functioning in finger-tapping-related and AX-CPT-related brain regions all showed declines in the dual task condition compared to the single task condition. Dual-task gait, AX-CPT and finger tapping performance, and brain functioning were all independent predictors of fall profile. No particular measurement domain stood out as being the most strongly associated measure with fall variables. Fall characteristics are determined by multiple factors; brain functioning, motor task, and cognitive task performance in challenging dual-task conditions all contribute to the risk of falling.
先前的研究表明,大脑功能测量以及在(需要同时执行的任务)上的表现是老年人跌倒风险很有前景的候选预测指标。然而,之前没有研究调查过在双重任务执行过程中的大脑功能测量是否能改善对跌倒风险的预测,以及双重任务范式中使用的子任务类型是否会影响跌倒特征与双重任务表现之间关联的强度。在本研究中,31名认知正常、居住在社区的老年人提供了自我报告的跌倒情况(跌倒次数和跌倒恐惧),完成了一项步态双重任务(在GaitRite垫子上行走时倒着拼写一个单词),并在功能磁共振成像(fMRI)期间完成了一项仰卧双重任务(一只手进行有节奏的手指敲击,同时另一只手完成AX连续性能任务(AX-CPT))。与单任务条件相比,双重任务条件下的步态表现、AX-CPT反应时间和准确性、手指敲击节奏以及与手指敲击相关和AX-CPT相关脑区的大脑功能均出现下降。双重任务步态、AX-CPT和手指敲击表现以及大脑功能都是跌倒情况的独立预测指标。没有哪个特定的测量领域作为与跌倒变量关联最强的测量指标脱颖而出。跌倒特征由多种因素决定;在具有挑战性的双重任务条件下的大脑功能、运动任务和认知任务表现都会增加跌倒风险。