1. Carter Leno V, Pickles A, van Noordt S, Huberty S, Desjardins J, Webb SJ, Elsabbagh M. 12-Month peak alpha frequency is a correlate but not a longitudinal predictor of non-verbal cognitive abilities in infants at low and high risk for autism spectrum disorder. Developmental cognitive neuroscience. 2021 ; 48 : 100938.

Although studies of PAF in individuals with autism spectrum disorder (ASD) report group differences and associations with non-verbal cognitive ability, it is not known how PAF relates to familial risk for ASD, and whether similar associations with cognition in are present in infancy. Using a large multi-site prospective longitudinal dataset of infants with low and high familial risk for ASD, metrics of PAF at 12 months were extracted and growth curves estimated for cognitive development between 12-36 months. Analyses tested whether PAF 1) differs between low and high risk infants, 2) is associated with concurrent non-verbal/verbal cognitive ability and 3) predicts developmental change in non-verbal/verbal ability. Moderation of associations between PAF and cognitive ability by familial risk status was also tested. No differences in 12-month PAF were found between low and high risk infants. PAF was associated with concurrent non-verbal cognitive ability, but did not predict change in non-verbal cognitive over development. No associations were found between PAF and verbal ability, along with no evidence of moderation. PAF is not related to familial risk for ASD, and is a neural marker of concurrent non-verbal cognitive ability, but not verbal ability, in young infants at low and high risk for ASD.

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2. Dutheil F, Comptour A, Morlon R, Mermillod M, Pereira B, Baker JS, Charkhabi M, Clinchamps M, Bourdel N. Autism spectrum disorder and air pollution : A systematic review and meta-analysis. Environmental pollution (Barking, Essex : 1987). 2021 ; 278 : 116856.

Despite the widely-known effects of air pollution, pollutants exposure surrounding pregnancy and the risk for autism spectrum disorder (ASD) in newborns remains controversial. The purpose of our study was to carry out a systematic review and meta-analyses of the risk of ASD in newborns following air pollution exposure during the perinatal period (preconception to second year of life). The PubMed, Cochrane Library, Embase and ScienceDirect databases were searched for articles, published up to July 2020, with the keywords « air pollution » and « autism ». Three models were used for each meta-analysis : a global model based on all risks listed in included articles, a pessimistic model based on less favorable data only, and an optimistic model based on the most favorable data only. 28 studies corresponding to a total of 758 997 newborns were included (47190 ASD and 703980 controls). Maternal exposure to all pollutants was associated with an increased risk of ASD in newborns by 3.9% using the global model and by 12.3% using the optimistic model, while the pessimistic model found no change. Each increase of 5 μg/m(3) in particulate matter <2.5 μm (PM2.5) was associated with an increased risk of ASD in newborns, regardless of the model used (global +7%, pessimistic +5%, optimistic +15%). This risk increased during preconception (global +17%), during pregnancy (global +5%, and optimistic +16%), and during the postnatal period (global +11% and optimistic +16%). Evidence levels were poor for other pollutants (PM10, NOx, O3, metals, solvents, styrene, PAHs, pesticides). PM2.5 was associated with a greater risk than PM10 (coefficient 0.20, 95CI -0.02 to 0.42), NOx (0.29, 0.08 to 0.50) or solvents (0.24, 0.04 to 0.44). All models revealed that exposure to pollutants, notably PM2.5 during pregnancy, was associated with an increased risk of ASD in newborns. Pregnancy and postnatal periods seem to be the most at-risk periods.

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3. Hammill C, Lerch JP, Taylor MJ, Ameis SH, Chakravarty MM, Szatmari P, Anagnostou E, Lai MC. Quantitative and Qualitative Sex-Modulations in the Brain Anatomy of Autism. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021.

BACKGROUND : Sex-based neurobiological heterogeneity in autism is poorly understood. Research is disproportionately biased to males, leading to an unwarranted presumption that autism neurobiology is the same across sexes. Previous neuroimaging studies using amalgamated multi-center datasets to increase autistic female samples are characterized by large statistical noise. METHODS : We used a better-powered dataset of 1,183 scans of 839 individuals-299 (467 scans) autistic males, 74 (102 scans) autistic females, 240 (334 scans) control males and 226 (280 scans) control females-to test two whole-brain models of overall/global sex-modulations on autism neuroanatomy, by summary measures computed across the brain : local magnitude model, where the same brain regions/circuitries are involved across sexes but effect sizes are larger in females, indicating quantitative sex-modulation ; and spatial dissimilarity model, where the neuroanatomy differs spatially between sexes, indicating qualitative sex-modulation. The male and female autism groups were matched on age, IQ and autism symptoms. Autism brain features were defined by comparisons to same-sex controls. RESULTS : Across five metrics (cortical thickness, surface area, volume, mean absolute curvature, and subcortical volume), we found no evidence supporting the local magnitude model. We found indicators supporting the spatial dissimilarity model on cortical mean absolute curvature and subcortical volume, but not other metrics. CONCLUSIONS : The overall/global autism neuroanatomy in females and males does not simply differ quantitatively in the same brain regions/circuitries. They may differ qualitatively in spatial involvement in cortical curvature and subcortical volume. The neuroanatomy of autism may be partly sex-specific. Sex-stratification to inform autism preclinical/clinical research is needed to identify sex-informed neurodevelopmental targets.

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4. Howells K, Sivaratnam C, Lindor E, He J, Hyde C, McGillivray J, Wilson RB, Rinehart N. Can a Community-Based Football Program Benefit Motor Ability in Children with Autism Spectrum Disorder ? A Pilot Evaluation Considering the Role of Social Impairments. J Autism Dev Disord. 2021.

This non-randomised pilot study evaluated the impact of a community football program on motor ability in children aged 5-12 years with autism spectrum disorder. Sixteen children were evaluated at baseline-and-post attendance in a football program for a varied number of weeks and compared to 19 children engaging in treatment-as-usual. Primary analyses indicated a statistically significant increase in total MABC-2, aiming and catching, and balance scores for the intervention group, with no changes in scores in the comparison group. There were no changes in manual dexterity across either group. At a between group level, the changes in aiming and catching scores were significantly greater for the intervention group. Further analyses highlighted the potential importance of social impairments regarding aiming and catching.

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5. Lovell B, Elder GJ, Wetherell MA. Sleep disturbances and physical health problems in caregivers of children with ASD. Res Dev Disabil. 2021 ; 113 : 103932.

OBJECTIVES : Caregivers of children with autism spectrum disorder self-report more physical health problems than controls. Sleep disturbances are also more prevalent in caregivers, and are positively associated with physical health problems. The negative impact of caring for a child with ASD on physical health therefore, might occur indirectly via poorer sleep. METHODS : Participants, of which n = 43 were caregivers and n = 17 were controls, completed self-report measures of physical health problems and, to capture objective measures of sleep, wore an actigraphy device. RESULTS : Physical health problems were greater in caregivers, as were subjective reports of disturbed sleep. Objectively, waking after sleep onset (WASO) and average number of awakenings were higher, as was sleep latency, and sleep efficiency was poorer, in caregivers. Total sleep time however, was greater in caregivers, as was time in bed. Physical health problems, while unrelated to actigraphy measures, were positively associated with self-reported sleep disturbances. Caregivers’ increased risk for physical health problems occurred indirectly via greater self-reports of disturbed sleep. CONCLUSIONS : Interventions that help alleviate caregivers’ sleep disturbances might be effective, by reducing physical health problems, for improving quality of provided care, and this might be explored in future research.

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6. Lu M, Chen J, He W, Pang F, Zou Y. Association between perceived social support of parents and emotional/behavioral problems in children with ASD : A chain mediation model. Res Dev Disabil. 2021 ; 113 : 103933.

BACKGROUND : Parental psychosocial factors are associated with emotional/behavioral problems in children with Autism Spectrum Disorder (ASD), but studies investigating their relationships are limited. AIMS : To explore the relationships between parents’ perceived social support, parental resilience, parenting self-efficacy, and emotional/behavioral problems in children with ASD, and the mechanism underlying these relationships. METHOD : The participants were 289 parents of children with ASD (including fathers and mothers) in China. A survey comprising the Multidimensional Scale of Perceived Social Support, Resilience Scale, Parenting Sense of Competence Scale, and Strengths and Difficulties Questionnaire was administered. RESULTS : (1) Parents’ perceived social support, parental resilience, and parenting self-efficacy were significantly associated with emotional/behavioral problems in children with ASD ; (2) parental resilience and parenting self-efficacy were found to play a chain-mediating role in the association between perceived social support of parents and emotional/behavioral problems in children with ASD. CONCLUSION : It is crucial to improve parents’ perceived social support, parental resilience, and parenting self-efficacy to reduce emotional/behavioral problems in children with ASD.

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7. Wang H, Avillach P. Autism Spectrum Disorders Classification using Genotype Data : A Deep Learning-based Predictive Classifier. JMIR medical informatics. 2021.

BACKGROUND : In the United States, there are 3 million people who have Autism Spectrum Disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of the disorder remain unknown, however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions on the children with ASD. Identification of ASD based on objective pathogenic mutation screening is a major first step towards early intervention and effective treatment of affected children. OBJECTIVE : Recent investigation interrogated genomics data for detecting and treating autism disorder, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks performs better than shallow machine learning models on complex and high-dimensional data, in this paper, we sought to apply deep learning to genetic data obtained across thousands of simplex families at-risk for autism spectrum disorder to identify contributory mutations and create an advanced diagnostic classifier for autism screening. METHODS : After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on Chi-Square test. A convolutional neural network-based diagnostic classifier is then designed using the identified significant common variants to predict autism. The performance is then compared with shallow machine learning-based classifiers and randomly selected common variants. RESULTS : The selected contributory common variants are significantly enriched in chromosome X while chromosome Y is also discriminatory in determining identification as autistic or non-autistic of an individual. ARSD, MAGEB16 and MXRA5 genes had the largest effect in the contributory variants. As a result, screening algorithms were adapted to include these common variants. The deep learning model yielded an AUC of 0.955 and an accuracy of 88% for identifying autism from non-autism individuals. We demonstrated a significant improvement over standard autism screening tools. CONCLUSIONS : The common variants are informative for autism identification. These findings also suggest that deep learning process is a reliable method in distinguishing the diseased group from the control group based on the common variants of autism.

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8. White SW, Stoppelbein L, Scott H, Spain D. It took a pandemic : Perspectives on impact, stress, and telehealth from caregivers of people with autism. Res Dev Disabil. 2021 ; 113 : 103938.

Shelter in place mandates due to the COVID-19 pandemic left caregivers to determine how to best meet the therapeutic requirements of their children with autism spectrum disorder (ASD). Understanding the challenges faced by families, and their experiences using telehealth for the first time, may help make services sustainable in future public health emergencies. A sample of 70 caregivers of people with ASD from across the US completed an anonymous online survey. Results indicate that impaired emotion regulation was a primary contributor of parent-reported stress for persons with ASD during the pandemic, while loss of established structure and routine contributed to parental stress. Nearly half the sample reported using telehealth for the first time. Many caregivers were appreciative that telehealth permitted continuation of services, but expressed concerns about limited effectiveness due in part to their children’s social communication problems.

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