Sun Binbin, Calvert Elombe Issa, Ye Alyssa, Mao Heng, Liu Kevin, Wang Raymond Kong, Wang Xin-Yuan, Wu Zhi-Liu, Wei Zhen, Kong Xue-Jun
Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China.
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
Front Neurosci. 2024 Nov 27;18:1502045. doi: 10.3389/fnins.2024.1502045. eCollection 2024.
Early identification of Autism Spectrum Disorder (ASD) is critical for effective intervention. Restricted interests (RIs), a subset of repetitive behaviors, are a prominent but underutilized domain for early ASD diagnosis. This study aimed to identify objective biomarkers for ASD by integrating electroencephalography (EEG) and eye-tracking (ET) to analyze toddlers' visual attention and cortical responses to RI versus neutral interest (NI) objects.
The study involved 59 toddlers aged 2-4 years, including 32 with ASD and 27 non-ASD controls. Participants underwent a 24-object passive viewing paradigm, featuring RI (e.g., transportation items) and NI objects (e.g., balloons). ET metrics (fixation time and pupil size) and EEG time-frequency (TF) power in theta (4-8 Hz) and alpha (8-13 Hz) bands were analyzed. Statistical methods included logistic regression models to assess the predictive potential of combined EEG and ET biomarkers.
Toddlers with ASD exhibited significantly increased fixation times and pupil sizes for RI objects compared to NI objects, alongside distinct EEG patterns with elevated theta and reduced alpha power in occipital regions during RI stimuli. The multimodal logistic regression model, incorporating EEG and ET metrics, achieved an area under the curve (AUC) of 0.75, demonstrating robust predictive capability for ASD.
This novel integration of ET and EEG metrics highlights the potential of RIs as diagnostic markers for ASD. The observed neural and attentional distinctions underscore the utility of multimodal biomarkers for early diagnosis and personalized intervention strategies. Future work should validate findings across broader age ranges and diverse populations.
早期识别自闭症谱系障碍(ASD)对于有效干预至关重要。受限兴趣(RIs)作为重复行为的一个子集,是ASD早期诊断中一个突出但未得到充分利用的领域。本研究旨在通过整合脑电图(EEG)和眼动追踪(ET)来识别ASD的客观生物标志物,以分析幼儿对受限兴趣对象与中性兴趣(NI)对象的视觉注意力和皮层反应。
该研究纳入了59名2至4岁的幼儿,其中包括32名患有ASD的幼儿和27名非ASD对照组幼儿。参与者接受了一个包含24个对象的被动观看范式,其中有受限兴趣对象(如交通工具)和中性兴趣对象(如气球)。分析了眼动追踪指标(注视时间和瞳孔大小)以及脑电图在θ波(4 - 8赫兹)和α波(8 - 13赫兹)频段的时频(TF)功率。统计方法包括逻辑回归模型,以评估脑电图和眼动追踪生物标志物组合的预测潜力。
与中性兴趣对象相比,患有ASD的幼儿对受限兴趣对象的注视时间和瞳孔大小显著增加,同时在受限兴趣刺激期间,枕叶区域出现了明显的脑电图模式,θ波功率升高,α波功率降低。结合脑电图和眼动追踪指标的多模态逻辑回归模型的曲线下面积(AUC)为0.75,显示出对ASD具有强大的预测能力。
眼动追踪和脑电图指标的这种新颖整合突出了受限兴趣作为ASD诊断标志物的潜力。观察到的神经和注意力差异强调了多模态生物标志物在早期诊断和个性化干预策略中的实用性。未来的工作应在更广泛的年龄范围和不同人群中验证研究结果。