The Role of EEG Functional Connectivity Coupled with Eye Tracking in Early Diagnosis of Autism Spectrum Disorder

AbstractBackground:Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated them simultaneously to measure restricted and repetitive behavior (RRBs) to infer early ASD diagnosis.Methods:Typically developing (TD) children (n=27) and ASD (n=32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) was measured to evaluate the predictive performance.Results:Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (P0.7, P0.7 P0.6, P<0.02 )for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91% and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores respectively.Conclusions:Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET substantially improves early ASD diagnosis.