Autistic children and their parents in the context of war: Preliminary findings.

While existing literature on the intersection of trauma and autism is limited, emerging evidence suggests heightened vulnerability of autistic children to the psychological consequences of traumatic events, including an elevated risk of developing posttraumatic stress disorder (PTSD). Additionally, parents of autistic children often experience elevated levels of negative emotional states, compared to parents of typically developing children. This study investigates the impact of terrorism and war on autistic and non-autistic children and their parents, presenting preliminary results from the initial data collection phase of a year-long longitudinal investigation of the experience of autistic children and their parents following Hamas' 7 October 2023 attack on Israel. Data gathered within 30 days of the initial attack reveal that both autistic and non-autistic children exhibited clinically significant post-traumatic stress symptoms, with autistic children demonstrating a more pronounced manifestation. Moreover, parents of autistic children reported significantly higher levels of depression, anxiety, and stress in the aftermath of the events, compared to an independent cohort of parents of autistic children assessed prior to the crisis. These results underscore the heightened susceptibility of autistic children to post-traumatic stress and the unique challenges confronted by their parents during times of conflict. The study highlights the imperative for tailored support services for autistic children and their families amidst traumatic incidents and stresses the need for further research in comparable contexts globally.

Automated Analysis of Stereotypical Movements in Videos of Children With Autism Spectrum Disorder.

Importance: Stereotypical motor movements (SMMs) are a form of restricted and repetitive behavior, which is a core symptom of autism spectrum disorder (ASD). Current quantification of SMM severity is extremely limited, with studies relying on coarse and subjective caregiver reports or laborious manual annotation of short video recordings. Objective: To assess the utility of a new open-source AI algorithm that can analyze extensive video recordings of children and automatically identify segments with heterogeneous SMMs, thereby enabling their direct and objective quantification. Design, Setting, and Participants: This retrospective cohort study included 241 children (aged 1.4 to 8.0 years) with ASD. Video recordings of 319 behavioral assessments carried out at the Azrieli National Centre for Autism and Neurodevelopment Research in Israel between 2017 and 2021 were extracted. Behavioral assessments included cognitive, language, and autism diagnostic observation schedule, 2nd edition (ADOS-2) assessments. Data were analyzed from October 2020 to May 2024. Exposures: Each assessment was recorded with 2 to 4 cameras, yielding 580 hours of video footage. Within these extensive video recordings, manual annotators identified 7352 video segments containing heterogeneous SMMs performed by different children (21.14 hours of video). Main outcomes and measures: A pose estimation algorithm was used to extract skeletal representations of all individuals in each video frame and was trained an object detection algorithm to identify the child in each video. The skeletal representation of the child was then used to train an SMM recognition algorithm using a 3 dimensional convolutional neural network. Data from 220 children were used for training and data from the remaining 21 children were used for testing. Results: Among 319 behavioral assessment recordings from 241 children (172 [78%] male; mean [SD] age, 3.97 [1.30] years), the algorithm accurately detected 92.53% (95% CI, 81.09%-95.10%) of manually annotated SMMs in our test data with 66.82% (95% CI, 55.28%-72.05%) precision. Overall number and duration of algorithm-identified SMMs per child were highly correlated with manually annotated number and duration of SMMs (r = 0.8; 95% CI, 0.67-0.93; P < .001; and r = 0.88; 95% CI, 0.74-0.96; P < .001, respectively). Conclusions and relevance: This study suggests the ability of an algorithm to identify a highly diverse range of SMMs and quantify them with high accuracy, enabling objective and direct estimation of SMM severity in individual children with ASD.

The Importance of Language Delays as an Early Indicator of Subsequent ASD Diagnosis in Public Healthcare Settings.

Previous studies have reported that ASD children with more severe symptoms are diagnosed earlier. However, previous studies in community settings have mostly relied on retrospective parental reports without the use of quantitative standardized test scores. Here, we evaluated the association of language, cognitive, and ASD severity standardized scores with the age of diagnosis in 1-6-year-old children diagnosed in a public healthcare setting. The results revealed that language scores were the strongest variable associated with the age of diagnosis, explaining ~ 30% of the variability across children. Indeed, all children diagnosed before 30-months of age exhibited moderate-to-severe language delays. These results further substantiate the prominence of language delay as a highly visible symptom associated with earlier ASD diagnosis in community clinical settings.