Background and hypothesis 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. Study design 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. Study results 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. Conclusions 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.

中文翻译：

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预测精神病转变的眼动特征：纵向变化和影响

背景和假设目前，对眼动（EM）特征对临床高风险（CHR）转换及其纵向轨迹的预测能力的实质性调查很少。本研究旨在调查依赖 EM 指数的机器学习预测模型的效率，并检查这些指数在时间连续体中的纵向变化。研究设计 对 140 名 CHR 和 98 名健康对照参与者进行基线 EM 评估（注视稳定性、自由观看和平滑追踪任务），随后进行为期 1 年的纵向观察研究。我们采用Cox回归分析并构建随机森林预测模型。我们还采用线性混合效应模型（LMM）来分析指数的纵向变化，同时按组和时间分层。研究结果 在接受了 1 年临床随访的 123 名 CHR 参与者中，25 人发展为全面的精神病，而 98 人仍未转变。与非转换者相比，转换者在自由观看任务中表现出注视持续时间延长、扫视幅度降低；在顺利的追踪任务中，眼跳幅度更大，速度增益降低。此外，基于 4 项基线 EM 测量，随机森林模型对转化者和非转化者进行分类，准确度为 0.776（95% CI：0.633，0.882）。最后，LMM 证明一年后加工商的上述指数没有显着的纵向变化。结论 异常 EM 可能先于精神病发作并在 1 年后保持稳定，应用眼动追踪技术与建模方法相结合可能有助于预测 CHR 演变为明显的精神病。