Zhang Dan, Xu Lihua, Liu Xu, Cui Huiru, Wei Yanyan, Zheng Wensi, Hong Yawen, Qian Zhenying, Hu Yegang, Tang Yingying, Li Chunbo, Liu Zhi, Chen Tao, Liu Haichun, Zhang Tianhong, Wang Jijun
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China.
Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, PR China.
Schizophr Bull. 2025 Mar 14;51(2):422-431. doi: 10.1093/schbul/sbae001.
Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of machine learning predictive models relying on EM indices and examine the longitudinal alterations of these indices across the temporal continuum.
EM assessments (fixation stability, free-viewing, and smooth pursuit tasks) were performed on 140 CHR and 98 healthy control participants at baseline, followed by a 1-year longitudinal observational study. We adopted Cox regression analysis and constructed random forest prediction models. We also employed linear mixed-effects models (LMMs) to analyze longitudinal changes of indices while stratifying by group and time.
Of the 123 CHR participants who underwent a 1-year clinical follow-up, 25 progressed to full-blown psychosis, while 98 remained non-converters. Compared with the non-converters, the converters exhibited prolonged fixation durations, decreased saccade amplitudes during the free-viewing task; larger saccades, and reduced velocity gain during the smooth pursuit task. Furthermore, based on 4 baseline EM measures, a random forest model classified converters and non-converters with an accuracy of 0.776 (95% CI: 0.633, 0.882). Finally, LMMs demonstrated no significant longitudinal alterations in the aforementioned indices among converters after 1 year.
Aberrant EMs may precede psychosis onset and remain stable after 1 year, and applying eye-tracking technology combined with a modeling approach could potentially aid in predicting CHRs evolution into overt psychosis.
目前，对于眼动（EM）特征对临床高危（CHR）转化的预测能力及其纵向轨迹的实质性研究较为匮乏。本研究旨在探讨依赖眼动指标的机器学习预测模型的有效性，并研究这些指标在整个时间连续体中的纵向变化。
对140名CHR参与者和98名健康对照参与者在基线时进行了眼动评估（注视稳定性、自由观看和平稳跟踪任务），随后进行了为期1年的纵向观察研究。我们采用Cox回归分析并构建了随机森林预测模型。我们还使用线性混合效应模型（LMMs）来分析指标的纵向变化，同时按组和时间进行分层。
在123名接受了1年临床随访的CHR参与者中，25人进展为全面性精神病，而98人仍未转化。与未转化者相比，转化者表现出更长的注视持续时间、在自由观看任务中扫视幅度减小、更大的扫视以及在平稳跟踪任务中速度增益降低。此外，基于4项基线眼动测量指标，随机森林模型对转化者和未转化者进行分类的准确率为0.776（95%CI：0.633，0.882）。最后，LMMs显示，1年后转化者中上述指标没有显著的纵向变化。
异常眼动可能在精神病发作之前出现，并在1年后保持稳定，应用眼动追踪技术结合建模方法可能有助于预测CHR向显性精神病的演变。
