Machine learning algorithms on eye tracking trajectories to classify patients with spatial neglect
Machine learning algorithms on eye tracking trajectories to classify patients with spatial neglect
复制标题
基于眼睛跟踪轨迹的机器学习算法对空间忽视患者进行分类
DOI：
10.1016/j.cmpb.2022.106929
复制
发表时间：
2022
影响因子：
6.1
通讯作者：
F. Anselmi
中科院分区：
文献类型：
--
作者：
B. Franceschiello;Tommaso Di Noto;Alexia Bourgeois;M. Murray;Astrid Minier;P. Pouget;J. Richiardi;P. Bartolomeo;F. Anselmi
背景与目的：眼动轨迹是丰富的行为数据，为研究大脑如何处理信息提供了一个窗口。我们解决的挑战，表征视觉空间忽视的迹象，扫视眼轨迹记录在脑损伤患者的空间忽视，以及在健康对照组在视觉搜索任务。方法：我们建立了一个标准化的预处理流程，适用于其他基于任务的眼动仪测量。我们使用传统的机器学习算法和深度卷积网络（1D和2D）来自动分析眼睛轨迹。结果：我们的性能最佳的机器学习模型将忽视患者与健康个体进行分类，ROC曲线下面积（AUC）范围为0.83至0.86。此外，1D卷积神经网络评分与忽视行为的严重程度相关，如通过标准化纸笔测试估计的，并且与通过扩散张量成像（DTI）测量的白色物质束的完整性相关。有趣的是，后者表现出明显的相关性与第三分支的上级纵束（SLF），特别是受损的疏忽。结论：该研究介绍了新的方法，为两个预处理和分类的眼动轨迹在忽视综合征患者。所提出的方法可能适用于其他类型的神经系统疾病，开辟了新的计算机辅助，精确，灵敏和非侵入性诊断工具的可能性。
Background and Objective:Eye-movement trajectories are rich behavioral data, providing a window on how the brain processes information. We address the challenge of characterizing signs of visuo-spatial neglect from saccadic eye trajectories recorded in brain-damaged patients with spatial neglect as well as in healthy controls during a visual search task. Methods: We establish a standardized pre-processing pipeline adaptable to other task-based eye-tracker measurements. We use traditional machine learning algorithms together with deep convolutional networks (both 1D and 2D) to automatically analyze eye trajectories.Results:Our top-performing machine learning models classified neglect patients vs. healthy individuals with an Area Under the ROC curve (AUC) ranging from 0.83 to 0.86. Moreover, the 1D convolutional neural network scores correlated with the degree of severity of neglect behavior as estimated with standardized paper-and-pencil tests and with the integrity of white matter tracts measured from Diffusion Tensor Imaging (DTI). Interestingly, the latter showed a clear correlation with the third branch of the superior longitudinal fasciculus (SLF), especially damaged in neglect.Conclusions:The study introduces new methods for both the pre-processing and the classification of eye-movement trajectories in patients with neglect syndrome. The proposed methods can likely be applied to other types of neurological diseases opening the possibility of new computer-aided, precise, sensitive and non-invasive diagnostic tools.
影响因子：
13.9
作者：
Corbetta M;Shulman GL
通讯作者：
Shulman GL
DOI：
--
发表时间：
2011
期刊：
--
影响因子：
--
作者：
Dmitry Lagun;Cecelia M. Manzanares;S. Zola;E. Buffalo;Eugene Agichtein
通讯作者：
Dmitry Lagun;Cecelia M. Manzanares;S. Zola;E. Buffalo;Eugene Agichtein