Background: As the prevalence of autism spectrum disorders in people with epilepsy ranges from 15 to 47 % (Clarke et al. in Epilepsia 46:1970-1977, it is speculated that there is a special relationship between the two disorders, yet there has been a lack of systematic studies comparing the behavioral phenotype between autistic individuals and autistic individuals with epilepsy. This study aims to investigate how the co-occurrence of epilepsy and Autism Spectrum Disorder (ASD) affects autistic characteristics assessed by the Social Responsiveness Scale (SRS), which has been used as a measure of autism symptoms in previous studies. In this research we referred to all individuals with Autism or Autistic Disorder as individuals with ASD. Methods:We reviewed the complete medical records of 182 participants who presented to a single tertiary care referral center from January 1, 2013 to July 28, 2015, and subsequently received complete child and adolescent psychiatric assessments. Of the 182 participants, 22 were diagnosed with Autism Spectrum Disorder and epilepsy. Types of epilepsy observed in these individuals included complex partial seizure, generalized tonic-clonic seizure, or infantile spasm. Using 'Propensity Score Matching' we selected 44 children, diagnosed with only Autism Spectrum Disorder, whose age, gender, and intelligence quotient (IQ) were closely matched with the 22 children diagnosed with Autism Spectrum Disorder and epilepsy. Social functioning of participants was assessed by the social responsiveness scale, which consists of five categories: social awareness, social cognition, social communication, social motivation, and autistic mannerisms. Bivariate analyses were conducted to compare the ASD participants with epilepsy group with the ASD-only group on demographic and clinical characteristics. Chi square and t test p values were calculated when appropriate. Results:There was no significant difference in age (p = 0.172), gender (p > 0.999), IQ (FSIQ, p = 0.139; VIQ, p = 0.114; PIQ, p = 0.295) between the two groups. ASD participants with epilepsy were significantly more impaired than ASD participants on some measures of social functioning such as social awareness (p = 0.03) and social communication (p = 0.027). ASD participants with epilepsy also scored significantly higher on total SRS t-score than ASD participants (p = 0.023). Conclusions:Understanding the relationship between ASD and epilepsy is critical for appropriate management (e.g. social skills training, seizure control) of ASD participants with co-occurring epilepsy. Results of this study suggest that mechanisms involved in producing epilepsy may play a role in producing or augmenting autistic features such as poor social functioning. Prospective study with larger sample sizes is warranted to further explore this association.

ImportanceJoint attention, composed of complex behaviors, is an early-emerging social function that is deficient in children with autism spectrum disorder (ASD). Currently, no methods are available for objectively quantifying joint attention.ObjectiveTo train deep learning (DL) models to distinguish ASD from typical development (TD) and to differentiate ASD symptom severities using video data of joint attention behaviors.Design, Setting, and ParticipantsIn this diagnostic study, joint attention tasks were administered to children with and without ASD, and video data were collected from multiple institutions from August 5, 2021, to July 18, 2022. Of 110 children, 95 (86.4%) completed study measures. Enrollment criteria were 24 to 72 months of age and ability to sit with no history of visual or auditory deficits.ExposuresChildren were screened using the Childhood Autism Rating Scale. Forty-five children were diagnosed with ASD. Three types of joint attention were assessed using a specific protocol.Main Outcomes and MeasuresCorrectly distinguishing ASD from TD and different levels of ASD symptom severity using the DL model area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall.ResultsThe analytical population consisted of 45 children with ASD (mean [SD] age, 48.0 [13.4] months; 24 [53.3%] boys) vs 50 with TD (mean [SD] age, 47.9 [12.5] months; 27 [54.0%] boys). The DL ASD vs TD models showed good predictive performance for initiation of joint attention (IJA) (AUROC, 99.6% [95% CI, 99.4%-99.7%]; accuracy, 97.6% [95% CI, 97.1%-98.1%]; precision, 95.5% [95% CI, 94.4%-96.5%]; and recall, 99.2% [95% CI, 98.7%-99.6%]), low-level response to joint attention (RJA) (AUROC, 99.8% [95% CI, 99.6%-99.9%]; accuracy, 98.8% [95% CI, 98.4%-99.2%]; precision, 98.9% [95% CI, 98.3%-99.4%]; and recall, 99.1% [95% CI, 98.6%-99.5%]), and high-level RJA (AUROC, 99.5% [95% CI, 99.2%-99.8%]; accuracy, 98.4% [95% CI, 97.9%-98.9%]; precision, 98.8% [95% CI, 98.2%-99.4%]; and recall, 98.6% [95% CI, 97.9%-99.2%]). The DL-based ASD symptom severity models showed reasonable predictive performance for IJA (AUROC, 90.3% [95% CI, 88.8%-91.8%]; accuracy, 84.8% [95% CI, 82.3%-87.2%]; precision, 76.2% [95% CI, 72.9%-79.6%]; and recall, 84.8% [95% CI, 82.3%-87.2%]), low-level RJA (AUROC, 84.4% [95% CI, 82.0%-86.7%]; accuracy, 78.4% [95% CI, 75.0%-81.7%]; precision, 74.7% [95% CI, 70.4%-78.8%]; and recall, 78.4% [95% CI, 75.0%-81.7%]), and high-level RJA (AUROC, 84.2% [95% CI, 81.8%-86.6%]; accuracy, 81.0% [95% CI, 77.3%-84.4%]; precision, 68.6% [95% CI, 63.8%-73.6%]; and recall, 81.0% [95% CI, 77.3%-84.4%]).Conclusions and RelevanceIn this diagnostic study, DL models for identifying ASD and differentiating levels of ASD symptom severity were developed and the premises for DL-based predictions were visualized. The findings suggest that this method may allow digital measurement of joint attention; however, follow-up studies are necessary for further validation.

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Background Heterogeneity in clinical manifestation and underlying neuro-biological mechanisms are major obstacles to providing personalized interventions for individuals with autism spectrum disorder (ASD). Despite various efforts to unify disparate data modalities and machine learning techniques for subclassification, replicable ASD clusters remain elusive. Our study aims to introduce a novel method, utilizing the objective behavioral biomarker of gaze patterns during joint attention, to subclassify ASD. We will assess whether behavior-based subgrouping yields clinically, genetically, and neurologically distinct ASD groups. Methods We propose a study involving 60 individuals with ASD recruited from a specialized psychiatric clinic to perform joint attention tasks. Through the examination of gaze patterns in social contexts, we will conduct a semi-supervised clustering analysis, yielding two primary clusters: good gaze response group and poor gaze response group. Subsequent comparison will occur across these clusters, scrutinizing neuroanatomical structure and connectivity using structural as well as functional brain imaging studies, genetic predisposition through single nucleotide polymorphism data, and assorted socio-demographic and clinical information. Conclusions The aim of the study is to investigate the discriminative properties and the validity of the joint attention-based subclassification of ASD using multi-modality data. Trial registration Clinical trial, KCT0008530, Registered 16 June 2023, https://cris.nih.go.kr/cris/index/index.do.