Search

Background: Eye tracking (ET) is emerging as a promising early and objective screening method for autism spectrum disorders (ASD), but it requires more reliable metrics with enhanced sensitivity and specificity for clinical use. Methods: This study introduces a suite of novel ET metrics: Area of Interest (AOI) Switch Counts (ASC), Favorable AOI Shifts (FAS) along self-determined pathways, and AOI Vacancy Counts (AVC), applied to toddlers and preschoolers diagnosed with ASD. The correlation between these new ET metrics and Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) scores via linear regression and sensitivity and specificity of the cut-off scores were assessed to predict diagnosis. Results: Our findings indicate significantly lower FAS and ASC and higher AVC (P<0.05) in children with ASD compared to their non-ASD counterparts within this high-risk cohort; the significance was not seen in total fixation time neither pupil size (p > 0.05). Furthermore, FAS was negatively correlated with ADOS-2 total scores and social affect (SA) subscale (p < 0.05). Among these new ET metrics, AVC yielded the best sensitivity 88-100% and specificity 80-88% with cut off score 0.305-0.306, followed by FAS and ASC to separate ASD from non-ASD for diagnosis. Conclusions: This study confirms the utility of innovative ET metrics—FAS, AVC, and ASC—which exhibit markedly improved sensitivity and specificity, enhancing ASD screening and diagnostic processes. [ABSTRACT FROM AUTHOR]

Introduction: 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. Methods: 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. Results: 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. Discussion: 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. [ABSTRACT FROM AUTHOR]