Alarifi Hana, Aldhalaan Hesham, Hadjikhani Nouchine, Johnels Jakob Åsberg, Alarifi Jhan, Ascenso Guido, Alabdulaziz Reem
Autism Center, King Faisal Specialists Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia.
Neurolimbic Research, Harvard/MGH Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
Child Adolesc Psychiatry Ment Health. 2023 Sep 30;17(1):112. doi: 10.1186/s13034-023-00662-3.
Despite the prevalence of Autism Spectrum Disorder (ASD) globally, there's a knowledge gap pertaining to autism in Arabic nations. Recognizing the need for validated biomarkers for ASD, our study leverages eye-tracking technology to understand gaze patterns associated with ASD, focusing on joint attention (JA) and atypical gaze patterns during face perception. While previous studies typically evaluate a single eye-tracking metric, our research combines multiple metrics to capture the multidimensional nature of autism, focusing on dwell times on eyes, left facial side, and joint attention.
We recorded data from 104 participants (41 neurotypical, mean age: 8.21 ± 4.12 years; 63 with ASD, mean age 8 ± 3.89 years). The data collection consisted of a series of visual stimuli of cartoon faces of humans and animals, presented to the participants in a controlled environment. During each stimulus, the eye movements of the participants were recorded and analyzed, extracting metrics such as time to first fixation and dwell time. We then used these data to train a number of machine learning classification algorithms, to determine if these biomarkers can be used to diagnose ASD.
We found no significant difference in eye-dwell time between autistic and control groups on human or animal eyes. However, autistic individuals focused less on the left side of both human and animal faces, indicating reduced left visual field (LVF) bias. They also showed slower response times and shorter dwell times on congruent objects during joint attention (JA) tasks, indicating diminished reflexive joint attention. No significant difference was found in time spent on incongruent objects during JA tasks. These results suggest potential eye-tracking biomarkers for autism. The best-performing algorithm was the random forest one, which achieved accuracy = 0.76 ± 0.08, precision = 0.78 ± 0.13, recall = 0.84 ± 0.07, and F1 = 0.80 ± 0.09.
Although the autism group displayed notable differences in reflexive joint attention and left visual field bias, the dwell time on eyes was not significantly different. Nevertheless, the machine algorithm model trained on these data proved effective at diagnosing ASD, showing the potential of these biomarkers. Our study shows promising results and opens up potential for further exploration in this under-researched geographical context.
尽管自闭症谱系障碍（ASD）在全球普遍存在，但阿拉伯国家在自闭症知识方面存在差距。认识到需要针对ASD的经过验证的生物标志物，我们的研究利用眼动追踪技术来了解与ASD相关的注视模式，重点关注共同注意（JA）和面部感知过程中的非典型注视模式。虽然先前的研究通常只评估单一的眼动追踪指标，但我们的研究结合了多个指标来捕捉自闭症的多维度特征，重点关注眼睛、面部左侧的停留时间以及共同注意。
我们记录了104名参与者的数据（41名神经典型者，平均年龄：8.21±4.12岁；63名患有ASD者，平均年龄8±3.89岁）。数据收集包括在受控环境中向参与者呈现的一系列人类和动物卡通面部的视觉刺激。在每次刺激过程中，记录并分析参与者的眼动，提取首次注视时间和停留时间等指标。然后我们使用这些数据训练了一些机器学习分类算法，以确定这些生物标志物是否可用于诊断ASD。
我们发现自闭症组和对照组在注视人类或动物眼睛的停留时间上没有显著差异。然而，自闭症个体对人类和动物面部左侧的关注较少，表明左视野（LVF）偏向降低。他们在共同注意（JA）任务中对一致物体的反应时间也较慢，停留时间较短，表明反射性共同注意减弱。在JA任务中，对不一致物体的注视时间没有显著差异。这些结果表明了自闭症潜在的眼动追踪生物标志物。表现最佳的算法是随机森林算法，其准确率=0.76±0.08，精确率=0.78±0.13，召回率=0.84±0.07，F1值=0.80±0.09。
虽然自闭症组在反射性共同注意和左视野偏向上表现出显著差异，但注视眼睛的停留时间没有显著差异。尽管如此，基于这些数据训练的机器学习算法模型在诊断ASD方面被证明是有效的，显示了这些生物标志物的潜力。我们的研究显示出有希望的结果，并为在这个研究较少的地理背景下进行进一步探索开辟了潜力。
