Abstract：
Pupillometry has been used in the studies of postural control to assess cognitive load during dual tasks, but its response to increased balance task intensity has not been investigated. Furthermore, it is unknown whether side-specific changes in pupil diameter occur with more demanding balance tasks providing additional insights into postural control. The two aims of this study were to analyze differences in steady-state pupil diameter between balance tasks with increased intensity and to determine whether there are side-specific changes. Forty-eight healthy subjects performed parallel and left and right one-legged stances on a force plate with and without foam with right and left pupil diameters measured with a mobile infrared eye-tracker. Differences between balance tasks in parameters (average pupil diameter of each eye, average of both pupil diameters and the difference between the left and right pupil diameter) were analyzed using a two-way repeated measures analysis of variance, and deep learning neural network models were used to investigate how pupillometry predicted each balance task. The pupil diameter of the left eye, the average pupil diameter of both eyes and the difference in pupil diameters increased statistically significantly from simpler to more demanding balance tasks, with this being more pronounced for the left eye. The deep learning neural network models revealed side-specific changes in pupil diameter with more demanding balance tasks. This study confirms pupillary responses to increased intensity of balance task and indicates side-specific pupil responses that could be related to task-specific involvement of higher levels of postural control.
摘要：
瞳孔测量已用于姿势控制的研究中，以评估双重任务期间的认知负荷，但是它对平衡任务强度增加的反应尚未得到研究。此外，目前尚不清楚瞳孔直径的侧特异性变化是否与更苛刻的平衡任务一起发生,从而为姿势控制提供更多见解.这项研究的两个目的是分析强度增加的平衡任务之间稳态瞳孔直径的差异，并确定是否存在特定的变化。48名健康受试者在有和没有泡沫的力板上进行平行和左右单腿姿势，并使用移动式红外眼睛跟踪器测量左右瞳孔直径。参数平衡任务之间的差异(每只眼睛的平均瞳孔直径，两个瞳孔直径的平均值和左右瞳孔直径之间的差异）使用双向重复测量方差分析进行分析，和深度学习神经网络模型被用来研究瞳孔测量如何预测每个平衡任务。左眼的瞳孔直径，两只眼睛的平均瞳孔直径和瞳孔直径的差异从更简单到更苛刻的平衡任务在统计学上显着增加，这对左眼来说更明显。深度学习神经网络模型揭示了瞳孔直径的侧面特定变化，以及更苛刻的平衡任务。这项研究证实了瞳孔对平衡任务强度增加的反应，并指出了特定于侧面的瞳孔反应，这可能与特定于任务的更高水平的姿势控制有关。
