Measuring and modelling fixational eye movements
Measuring and modelling fixational eye movements
复制标题
测量和建模注视眼球运动
DOI：
10.1167/jov.22.14.4304
复制
发表时间：
2022
影响因子：
1.8
通讯作者：
Hexley A
中科院分区：
文献类型：
--
作者：
Hexley A
前言注视性眼球运动（FEM）包括漂移（缓慢、曲折的运动），叠加震颤（快速振荡），被微扫视（快速、跳跃式运动）打断。在这里，我们记录FEM在高的空间和时间分辨率使用自适应光学扫描激光检眼镜（AOSLO）。我们使用的数据来开发模型的有限元模型，重点在于表征的漂移component.MethodsFrom每个10名参与者，我们记录了50个两秒钟的AOSLO电影在中央凹固定。在后处理中提取FEM迹线，相对于单独收集的视网膜图像蒙太奇。在每一个跟踪，我们分类期间的微扫视，漂移和叠加震颤，跟踪故障，使用现有的和新的自动化技术。我们根据AOSLO模拟器（埃里卡，Young和Smithson，2021）生成的地面实况数据和手动标记验证了每种技术。从每条迹线中分离出500 ms的漂移周期，并针对从统计（尤其是聚合物）物理学文献中常见的随机游走模型中选择的候选模型进行评价。不同的模型捕捉不同的行为，如持久性或反持久性，自我回避和边界或本地化。这种行为差异可能导致在不同条件下的漂移或相同漂移响应的不同时间尺度。使用诊断图对它们进行评估，例如自相关函数和均方位移相对于时间滞后的双对数图。结果一旦去除微跳视，诊断图在参与者之间显示出高度一致性。平均漂移速度存在个体差异，但参与者的随机游走特征在很大程度上得到了保留。这些特点是不适合现有的模型和他们的提取是依赖于microsaccade detection.ConclusionsWe分析FEM对不同的随机游走模型，以消除漂移，我们报告的模型，最适合的新数据。从AOSLO记录中提取高分辨率漂移轨迹的改进方法对于提供可以区分眼漂移候选模型的数据非常重要。
IntroductionFixational eye movements (FEMs) comprise periods of drift (slow, meandering motion), with superimposed tremor (fast oscillations), interrupted by microsaccades (fast, jump-like movements). Here, we record FEMs at high spatial and temporal resolution using an adaptive optics scanning laser ophthalmoscope (AOSLO). We use the data to develop models of FEMs, with emphasis on characterising the drift component.MethodsFrom each of 10 participants, we recorded 50 two-second AOSLO movies during foveal fixation. FEM traces were extracted in post-processing, relative to separately collected retinal image montages. Within each trace we classified periods of microsaccades, drift and superimposed tremor, and tracking failures, using both existing and new automated techniques. We validate each technique against ground-truth data, generated with an AOSLO simulator (ERICA, Young and Smithson, 2021), and manual labelling. 500 ms drift periods were isolated from each trace and evaluated against candidate models selected from random walk models that are common in the statistical (especially polymer) physics literature. Different models capture different behaviours, such as persistence or anti-persistence, self-avoidance and bounding or localisation. Such differences in behaviour can characterise either drift under varying conditions or different timescales of the same drift response. They were evaluated using diagnostic plots, like the autocorrelation function and log-log plots of mean-squared-displacement against time-lag.ResultsDiagnostic plots showed high levels of consistency across participants, once microsaccades were removed. There were individual differences in mean drift velocity but random walk characteristics were largely preserved across participants. These characteristics are poorly fitted by existing models and their extraction is dependent on microsaccade detection.ConclusionsWe analysed FEMs against different random walk models to characterise drift and we report the model that best fits the new data. Improved methods for extracting high-resolution drift traces from AOSLO recordings are important in delivering data that can discriminate candidate models of ocular drift.