1. Abdel Hameed M, Hassaballah M, Hosney ME, Alqahtani A. An AI-Enabled Internet of Things Based Autism Care System for Improving Cognitive Ability of Children with Autism Spectrum Disorders. Computational intelligence and neuroscience. 2022; 2022: 2247675.

Smart monitoring and assisted living systems for cognitive health assessment play a central role in assessment of individuals’ health conditions. Autistic children suffer from some difficulties including social skills, repetitive behaviors, speech and nonverbal communication, and accommodating to the environment around them. Thus, dealing with autistic children is a serious public health problem as it is hard to determine what they feel with a lack of emotional cognitive ability. Currently, no medical treatments have been shown to cure autistic children, with most of the social assistive research to date focusing on Autism Spectrum Disorder (ASD) without suggesting a real treatment. In this paper, we focus on improving cognitive ability and daily living skills and maximizing the ability of the autistic child to function and participate positively in the community. Through utilizing intelligent systems based Artificial Intelligence (AI) and Internet of Things (IoT) technologies, we facilitate the process of adaptation to the world around the autistic children. To this end, we propose an AI-enabled IoT system embodied in a sensor for measuring the heart rate to predict the state of the child and then sending the state to the guardian with feeling and expected behavior of the child via a mobile application. Further, the system can provide a new virtual environment to help the child to be capable of improving eye contact with other people. This way is represented in pictures of these persons in 3D models that break this child’s fear barrier. The system follows strategies that have focused on social communication skill development particularly at young ages to be more interactive with others.

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2. Abdulrazzaq AA, Hamid SS, Al-Douri AT, Mohamad AAH, Ibrahim AM. Early Detection of Autism Spectrum Disorders (ASD) with the Help of Data Mining Tools. BioMed research international. 2022; 2022: 1201129.

Autism is a disorder of neurobiological origin that originates a different course in the development of verbal and nonverbal communication, social interactions, the flexibility of behavior, and interests. The results obtained offer relevant information to reflect on the practices currently used in assessing the development of children and the detection of ASD and suggest the need to strengthen the training of health professionals in aspects such as psychology and developmental disorders. This study, based on genuine and current facts, used data from 292 children with an autism spectrum disorder. The input dataset has 20 characteristics, and the output dataset has one attribute. The output property indicates whether or not a certain person has autism. The research study first and foremost performed data pretreatment activities such as filling in missing data gaps in the data collection, digitizing categorical data, and normalizing. The features were then clustered using k-means and x-means clustering methods, then artificial neural networks and a linguistic strong neurofuzzy classifier were used to classify them. The outcomes of each strategy were examined, and their respective performances were compared.

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3. Chezan LC, Liu J, Drasgow E, Ding R, Magana A. The Quality of Life for Children with Autism Spectrum Disorder Scale: Factor Analysis, MIMIC Modeling, and Cut-Off Score Analysis. Journal of autism and developmental disorders. 2022.

Our purpose in this study was to further examine the psychometric properties of the Quality of Life for Children with Autism Spectrum Disorder (QOLASD-C) scale. We first investigated the factor structure and the internal consistency of the scale. The bifactor model showed good fit and strong reliability. Second, we used multiple-indicators multiple-causes (MIMIC) modeling to examine the associations between demographic variables and the QOLASD-C dimensions. Results showed differences on overall QOL based on age, race/ethnicity, and autism spectrum disorder severity, but no relationships with gender. All demographic variables were associated with one or all three subscales (i.e., interpersonal relationships, self-determination, emotional well-being) of the QOLASD-C. Third, an optimal cut-off score of 37 was identified. Implications for research and practice are discussed.

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4. Cogram P, Deacon RMJ, Klamer D, Rebowe N, Sprouse J, Reyes ST, Missling CU, Kaufmann WE. Brain cell signaling abnormalities are detected in blood in a murine model of Fragile X syndrome and corrected by Sigma-1 receptor agonist Blarcamesine. American journal of medical genetics Part A. 2022.

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5. Fell L, Goshe B, Traeger L, Perez G, Iannuzzi D, Park E, Kuhlthau K, Luberto C. Acceptability of A Virtual Mind-Body Group Intervention for Teen Siblings of Children with Autism Spectrum Disorder. Journal of autism and developmental disorders. 2022.

Teenage siblings of children with autism spectrum disorder (ASD) are at risk of worse mental health outcomes than their peers, yet there have been few interventions focused on improving their psychosocial wellbeing. This study explored the acceptability of an 8-session virtual group mind-body resiliency intervention for teen siblings of children with ASD. We used mixed methods to assess quantitative and qualitative survey results. Participants reported that the intervention had the right amount of sessions (88%), structure (74%), and duration (89%). Most participants felt comfortable during sessions (74%), found it helpful to learn mind-body exercises (74%), and that the intervention helped in coping with stress (71%). Though participants were satisfied with the opportunity to meet peers, they desired more social connection.

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6. Frost KM, Pomales-Ramos A, Ingersoll B. Brief Report: Response to Joint Attention and Object Imitation as Predictors of Expressive and Receptive Language Growth Rate in Young Children on the Autism Spectrum. Journal of autism and developmental disorders. 2022.

Joint attention and imitation are thought to facilitate a developmental cascade of language and social communication skills. Delays in developing these skills may affect the quality of children’s social interactions and subsequent language development. We examined how responding to joint attention and object imitation skills predicted rate of expressive and receptive communication growth rate in a heterogeneous sample of autistic children. Children’s baseline skills in responding to joint attention uniquely predicted expressive, but not receptive, language growth rate over time, while object imitation did not significantly predict language growth rate over and above joint attention skills. Future research should examine the potential moderating roles of child age and developmental level in explaining associations between joint attention and object imitation and later language development.

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7. Gray HL, Pang T, Agazzi H, Shaffer-Hudkins E, Kim E, Miltenberger RG, Waters KA, Jimenez C, Harris M, Stern M. A nutrition education intervention to improve eating behaviors of children with autism spectrum disorder: Study protocol for a pilot randomized controlled trial. Contemporary clinical trials. 2022: 106814.

Autism spectrum disorder (ASD) is a developmental disorder that affects communication and social behaviors. Children with ASD often experience mealtime behavior challenges and selective eating behaviors. They also tend to consume fewer fruits and vegetables and more high-energy dense foods, compared to neurotypical peers. A nutrition intervention was designed to prevent the development of feeding disorders and the long-term negative health impacts associated with poor dietary intake. This randomized controlled trial will evaluate the feasibility and preliminary efficacy of the nutrition education intervention for children with ASD and their parents through the Early Intervention (EI) services. We will recruit EI providers and parent-child dyads (n = 48) from EI programs, and randomly assign them into Autism Eats intervention (n = 24) or enhance usual care (EUC) comparison group (n = 24). The Autism Eats is 10 weekly sessions delivered individually as part of EI, while the EUC group will receive only 1 nutrition education session and then weekly parent handouts. The Autism Eats integrates ASD-specific feeding strategies and behaviorally-focused intervention strategies such as goal setting. Feasibility indicators include reach/participation, attrition, completion, fidelity, compatibility, and qualitative participant feedback. Outcome measures include dietary intakes and mealtime behaviors of children with ASD using 3-day food records and a validated questionnaire, the Brief Autism Mealtime Behavior Inventory (BAMBI). We will examine whether there are differences in children’s food intakes, variety, diet quality, and mealtime behaviors between Autism Eats and EUC groups at post-intervention and 5-month follow-up assessment. This study will provide critical data to inform a full-scale randomized controlled trial.

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8. Haine-Schlagel R, Corsello C, Caplan B, Gould H, Brookman-Frazee L. Setting Families Up for Success: A Pilot Study of a Toolkit to Enhance the Autism Spectrum Disorder Diagnostic Evaluation Process. Journal of autism and developmental disorders. 2022.

Families of children with autism spectrum disorder (ASD) face challenges engaging in services following diagnosis. This study: (1) developed and implemented a toolkit to tailor ASD evaluation feedback to families’ needs, and (2) evaluated caregiver and provider perceptions of the toolkit. Focus groups with providers (N = 11) informed toolkit development. Seven providers participated in pilot training and implementation. Provider and caregiver toolkit perceptions were assessed using interviews, surveys, and a fidelity checklist. Toolkit strategies reflect focus group themes. Provider and caregiver ratings suggest the initial feasibility, acceptability, and utility of the toolkit. This toolkit may be feasible to implement in community settings and may increase caregiver satisfaction, though further refinements are needed to support service connection.

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9. Harvey PC, Willis EPE, Brown DJ, Byrne AL, Baldwin APA, Heard D, Augutis W. Navigating the care of families with a child or children with autistic spectrum disorder. Journal of intellectual disabilities : JOID. 2022: 17446295221106001.

The aim of this project was to better understand nurse navigators work with children and families who are living with severe autism spectrum disorder to achieve improved health and wellbeing outcomes. Nurse navigators were introduced into the public health sector in Queensland in 2016, with 400 navigators currently working across 16 health services in diverse geographic and demographic settings. Narrative inquiry was used to explore one nurse navigator’s journey working with children and families living with severe Autism. The challenges of rigid health systems to adapt to the requirements of children with special needs, particularly in relation to care in the emergency department and where interventional procedures are necessary were apparent. Nurse navigators can effectively co-ordinate the care of an extremely vulnerable patient cohort and provide essential advocacy in a health system that is rigid and lacking the flexibility to deal with individual needs.

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10. Lane SJ, Leão MA, Spielmann V. Sleep, Sensory Integration/Processing, and Autism: A Scoping Review. Frontiers in psychology. 2022; 13: 877527.

The prevalence of sleep dysfunction is considerably higher in the autistic population than in the non-autistic. Similarly, the incidence of sensory reactivity differences in autism exceeds that in the neurotypical population. The basis of sleep disorders in autism is multifactorial, but sensory integration/processing concerns may play a role. Research that investigates this interplay for autistic individuals is limited but vital. In this scoping review, we examined literature addressing the following research question: What is the relationship between sleep and sensory integration/processing in autism? We included articles if they were peer-reviewed, English or Spanish, purposefully addressed sensory integration/processing differences, were sleep focused and included autism as the primary diagnosis or population. Articles were excluded if the language was not English or Spanish, research was conducted with animals, they were non-peer-reviewed, the primary population was not autistic, the sensory focus reflected a specific sensorineural loss (e.g., blindness, or deafness), there was not a clear inclusion of sensory integration/processing or sleep. We searched six databases and included all citations from the inception of each database through June 2021. The search strategy identified 397 documents that were reduced to 24 included articles after exclusion criteria were applied. The majority of studies we identified characterized the relation between sleep and sensory integration/processing differences in autism. Investigators found multiple sleep concerns such as bedtime resistance, sleep anxiety, delayed sleep onset, night awaking, and short sleep duration in autistic individuals. Identified sensory concerns focused on reactivity, finding hyper- and hypo-reactivity as well as sensory seeking across sensory domains. Co-existence of sleep concerns and sensory integration/processing differences was frequently reported. Few intervention studies showed a clear sensory focus; those that did emphasized pressure, movement, touch, and individual sensory preferences/needs. Swimming programs and massage showed promising results. No studies were of high quality. At a minimum, there is a co-existence of sensory reactivity differences and sleep concerns in autistic children, and possibly autistic adults. The relationship between poor sleep and sensory integration/processing differences is complex and multi-faceted, requiring additional research. Interventions that purposefully include a central sensory component have not been well studied in autistic children or adults. Overall studies with greater rigor and purposeful use of sensation and sensorimotor supports as a component of intervention are needed. This study was not funded.

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11. Li H, Wang X, Hu C, Li H, Xu Z, Lei P, Luo X, Hao Y. JUN and PDGFRA as Crucial Candidate Genes for Childhood Autism Spectrum Disorder. Frontiers in neuroinformatics. 2022; 16: 800079.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, characterized by marked genetic heterogeneity. In this study, two independent microarray datasets of cerebellum of ASD were integrative analyzed by NetworkAnalyst to screen candidate crucial genes. NetworkAnalyst identified two up-regulated genes, Jun proto-oncogene (JUN) and platelet derived growth factor receptor alpha (PDGFRA), as the most crucial genes in cerebellum of ASD patients. Based on KEGG pathway database, genes associated with JUN in the cerebellum highlight the pathways of Th17 cell differentiation and Th1 and Th2 cell differentiation. Genes associated with PDGFRA in the cerebellum were found enriched in pathways in EGFR tyrosine kinase inhibitor resistance and Rap1 signaling pathway. Analyzing all differentially expressed genes (DEGs) from the two datasets, Gene Set Enrichment Analysis (GSEA) brought out IL17 signaling pathway, which is related to the expression of JUN and PDGFRA. The ImmuCellAI found the elevated expression of JUN and PDGFRA correlating with increased Th17 and monocytes suggests JUN and PDGFRA may regulate Th17 cell activation and monocytes infiltrating. Mice model of maternal immune activation demonstrated that JUN and PDGFRA are up-regulated and related to the ASD-like behaviors that provide insights into the molecular mechanisms underlying the altered IL17 signaling pathway in ASD and may enable novel therapeutic strategies.

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12. Persson M, Reichenberg A, Andersson Franko M, Sandin S. Maternal type 1 diabetes, pre-term birth and risk of autism spectrum disorder-a prospective cohort study. International journal of epidemiology. 2022.

BACKGROUND: It has been suggested that maternal type 1 diabetes (T1D) increases the risk of autism spectrum disorder (ASD) in the offspring. However, it is unclear whether this risk is mediated by pre-term birth, affecting around one-third of pregnancies with T1D, and whether maternal levels of glycated haemoglobin (HbA1c) impact the risk. METHODS: A cohort of 1.4 million Swedish children born between 1998 and 2015, and their parents. Maternal T1D and HbA1c before or in early pregnancy, gestational and ASD diagnoses were obtained from Swedish national registers. Relative risk (RR) and 95% CIs of ASD were estimated by hazard ratios (HRs) from Cox regression or RR from log-binomial regression. RESULTS: Of 1 406 650 children, 8003 (0.6%) were born to mothers with T1D, 24 941 (1.8%) were diagnosed with ASD and 81 915 (5.8%) were born pre-term. The risk of ASD was increased in offspring of mothers with T1D was HR = 1.40 (1.21-1.61). The RR for each +5-mmol/mol excess HbA1c was estimated at HR = 1.03 (0.97-1.10). The T1D effect on ASD mediated through pre-term birth was estimated at RR = 1.06 (1.05 to 1.08), corresponding to 22% (16% to 41%) of the total effect. T1D in pregnancy was associated with increased ASD risk in the offspring. Twenty percent of the total effect was accounted for by pre-term birth. HbA1c was not associated with ASD risk, beyond the risk associated by the T1D diagnosis itself. CONCLUSION: Awareness of ASD in the offspring of mothers with T1D may be warranted, especially considering the additional effect of pre-term birth.

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13. Tsang T, Naples AJ, Barney EC, Xie M, Bernier R, Dawson G, Dziura J, Faja S, Jeste SS, McPartland JC, Nelson CA, Murias M, Seow H, Sugar C, Webb SJ, Shic F, Johnson SP. Attention Allocation During Exploration of Visual Arrays in ASD: Results from the ABC-CT Feasibility Study. Journal of autism and developmental disorders. 2022.

Visual exploration paradigms involving object arrays have been used to examine salience of social stimuli such as faces in ASD. Recent work suggests performance on these paradigms may associate with clinical features of ASD. We evaluate metrics from a visual exploration paradigm in 4-to-11-year-old children with ASD (n = 23; 18 males) and typical development (TD; n = 23; 13 males). Presented with arrays containing faces and nonsocial stimuli, children with ASD looked less at (p = 0.002) and showed fewer fixations to (p = 0.022) faces than TD children, and spent less time looking at each object on average (p = 0.004). Attention to the screen and faces correlated positively with social and cognitive skills in the ASD group (ps < .05). This work furthers our understanding of objective measures of visual exploration in ASD and its potential for quantifying features of ASD.

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14. Wang Y, Fu Y, Luo X. Identification of Pathogenetic Brain Regions via Neuroimaging Data for Diagnosis of Autism Spectrum Disorders. Frontiers in neuroscience. 2022; 16: 900330.

Autism spectrum disorder (ASD) is a kind of neurodevelopmental disorder that often occurs in children and has a hidden onset. Patients usually have lagged development of communication ability and social behavior and thus suffer an unhealthy physical and mental state. Evidence has indicated that diseases related to ASD have commonalities in brain imaging characteristics. This study aims to study the pathogenesis of ASD based on brain imaging data to locate the ASD-related brain regions. Specifically, we collected the functional magnetic resonance image data of 479 patients with ASD and 478 normal subjects matched in age and gender and used a machine-learning framework named random support vector machine cluster to extract distinctive brain regions from the preprocessed data. According to the experimental results, compared with other existing approaches, the method used in this study can more accurately distinguish patients from normal individuals based on brain imaging data. At the same time, this study found that the development of ASD was highly correlated with certain brain regions, e.g., lingual gyrus, superior frontal gyrus, medial gyrus, insular lobe, and olfactory cortex. This study explores the effectiveness of a novel machine-learning approach in the study of ASD brain imaging and provides a reference brain area for the medical research and clinical treatment of ASD.

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15. Zhu Y, Mu W, Chirica MG, Berenbaum H. Testing a theory-driven factor structure of the autism-spectrum quotient. Autism research : official journal of the International Society for Autism Research. 2022.

The Autism-Spectrum Quotient (AQ) is a popular instrument used to assess the degree to which individuals exhibit features of autism spectrum conditions (ASC). The current study aimed to develop a theory-driven factor structure of the AQ that would fit as well across samples as the 12 previously proposed factor structures, all of which, except for the original Baron-Cohen model, had been developed on the basis of exploratory factor analysis (EFA) or principal component analysis. We first proposed a six-factor solution: (1) social anhedonia; (2) interest in details/patterns; (3) imagination ability; (4) desire for predictability/routine; (5) social cognition; and (6) social discourse convention. We tested the six-factor structure and made final item selections (27 items) with EFA using data from college students (n = 503). Then, we empirically tested alternative factor structure models in three other independent samples (ns = 503; 1263; 1641) using confirmatory factor analysis. Results indicated that our model fit as well, if not better, than all of the other models across samples, regardless of parameter estimation methods and software packages. Overall, the theory-driven replicable six-factor structure that we report holds the potential to be used to measure the six domains of features that we identified in the AQ. LAY SUMMARY: Questionnaire measures of autism spectrum conditions have typically been used to measure approximately four broad dimensions. Our study suggests that the Autism-Spectrum Quotient can be used to measure six more narrowly defined dimensions: social anhedonia, interest in details/patterns, imagination ability, desire for predictability/routine, social cognition, and social discourse convention. Additional work is needed to develop measures of a much wider variety of autism spectrum features.

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