Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Xiangya Hospital, Central South University, Changsha, China.
Department of Biology, Emory University, Atlanta, Georgia, USA.
Alzheimers Dement. 2024 Feb;20(2):1089-1101. doi: 10.1002/alz.13517. Epub 2023 Oct 24.
Whether the integration of eye-tracking, gait, and corresponding dual-task analysis can distinguish cognitive impairment (CI) patients from controls remains unclear.
One thousand four hundred eighty-one participants, including 724 CI and 757 controls, were enrolled in this study. Eye movement and gait, combined with dual-task patterns, were measured. The LightGBM machine learning models were constructed.
A total of 105 gait and eye-tracking features were extracted. Forty-six parameters, including 32 gait and 14 eye-tracking features, showed significant differences between two groups (P < 0.05). Of these, the Gait_3Back-TurnTime and Dual-task cost-TurnTime patterns were significantly correlated with plasma phosphorylated tau 181 (p-tau181) level. A model based on dual-task gait, dual-task smooth pursuit, prosaccade, and anti-saccade achieved the best area under the receiver operating characteristics curve (AUC) of 0.987 for CI detection, while combined with p-tau181, the model discriminated mild cognitive impairment from controls with an AUC of 0.824.
Combining dual-task gait and dual-task eye-tracking analysis is feasible for the detection of CI.
This is the first study to report the efficiency of integrated parameters of dual-task gait and eye-tracking for cognitive impairment (CI) detection in a large cohort. We identified 46 gait and eye-tracking features associated with CI, and two were correlated to plasma phosphorylated tau 181. We constructed the model based on dual-task gait, smooth pursuit, prosaccade, and anti-saccade, achieving the best area under the curve of 0.987 for CI detection.
眼动、步态及其对应的双重任务分析整合是否能区分认知障碍（CI）患者和正常人仍不清楚。
本研究纳入了 1481 名参与者，包括 724 名 CI 患者和 757 名正常人。测量了眼动和步态，并结合双重任务模式进行分析。构建了 LightGBM 机器学习模型。
共提取了 105 项步态和眼动特征。两组间有 46 个参数（32 项步态和 14 项眼动特征）存在显著差异（P<0.05）。其中，步态_3Back-TurnTime 和双重任务成本-TurnTime 模式与血浆磷酸化 tau181（p-tau181）水平显著相关。基于双重任务步态、双重任务平滑追踪、正眼跳和反眼跳的模型对 CI 的检测具有最佳的受试者工作特征曲线下面积（AUC）为 0.987，而与 p-tau181 结合时，该模型对轻度认知障碍和正常人的区分 AUC 为 0.824。
结合双重任务步态和双重任务眼动分析对 CI 的检测是可行的。
这是第一项在大样本队列中报告双重任务步态和眼动综合参数对 CI 检测效率的研究。我们确定了 46 项与 CI 相关的步态和眼动特征，其中 2 项与血浆磷酸化 tau181 相关。我们构建了基于双重任务步态、平滑追踪、正眼跳和反眼跳的模型，其对 CI 的检测具有最佳的曲线下面积为 0.987。
