1. Brady MJ, Jenkins CA, Gamble-Turner JM, Moseley RL, Janse van Rensburg M, Matthews RJ. « A perfect storm »: Autistic experiences of menopause and midlife. Autism;2024 (Apr 15):13623613241244548.

Previous studies report that menopause can be a very difficult transition for some autistic people. This study focuses on how autistic people experience menopause and what support and information might help them. Autistic Community Research Associates played an important role in the research and co-authored this article. We held four focus groups and eight interviews online with 24 autistic participants who lived in either Canada (n = 13) or the United Kingdom (n = 11). We analysed participant conversations using a method called reflexive thematic analysis. Participants described many intense challenges during menopause. Four themes and eight subthemes were identified across participant groups: (1) Complexity, multiplicity and intensity of symptoms (0 subthemes); (2) Life experience and adversity converging at midlife (three subthemes); (3) The importance of knowledge and connection (two subthemes); and (4) Barriers to support and care (three subthemes). The experiences of our participants may not be the same as other autistic people, and the study could have been more inclusive of diverse autistic groups. However, hearing about the experiences of others may provide reassurance to autistic people who struggle with menopause and let them know they are not alone.

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2. Forby L, Pazhoohi F, Kingstone A. Autistic traits and anthropomorphism: the case of vehicle fascia perception. Cogn Process;2024 (Apr 16)

Individuals high in autistic traits can have difficulties with social interactions which may stem from difficulties with mentalizing abilities, yet findings from research investigating anthropomorphism of non-human objects in high trait individuals are inconsistent. Measuring emotions and attributes of front-facing vehicles, individuals scoring high versus low on the AQ-10 were compared for ratings of angry-happy, hostile-friendly, masculine-feminine, and submissive-dominant, as a function of vehicle size (large versus small). Our results showed that participants perceived large vehicles as more angry, hostile, masculine, and dominant than small vehicles, with no significant difference in ratings between high and low AQ-10 scorers. The current findings support previous research reporting high autistic trait individuals’ intact object processing. Our novel findings also suggest high autistic trait individuals’ anthropomorphizing abilities are comparable to those found in low autistic trait individuals.

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3. Leroy G, Andrews JG, KeAlohi-Preece M, Jaswani A, Song H, Galindo MK, Rice SA. Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes. J Am Med Inform Assoc;2024 (Apr 16)

OBJECTIVE: Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence. METHODS: We use unstructured data from the Centers for Disease Control and Prevention (CDC) surveillance records labeled by a CDC-trained clinician with ASD A1-3 and B1-4 criterion labels per sentence and with ASD cases labels per record using Diagnostic and Statistical Manual of Mental Disorders (DSM5) rules. One rule-based and three deep ML algorithms and six ensembles were compared and evaluated using a test set with 6773 sentences (N = 35 cases) set aside in advance. Criterion and case labeling were evaluated for each ML algorithm and ensemble. Case labeling outcomes were compared also with seven traditional tests. RESULTS: Performance for criterion labeling was highest for the hybrid BiLSTM ML model. The best case labeling was achieved by an ensemble of two BiLSTM ML models using a majority vote. It achieved 100% precision (or PPV), 83% recall (or sensitivity), 100% specificity, 91% accuracy, and 0.91 F-measure. A comparison with existing diagnostic tests shows that our best ensemble was more accurate overall. CONCLUSIONS: Transparent ML is achievable even with small datasets. By focusing on intermediate steps, deep ML can provide transparent decisions. By leveraging data redundancies, ML errors at the intermediate level have a low impact on final outcomes.

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4. MacKenzie KT, Crown MJ, Northrup JB, Rutenberg E, Hartman AG, Mazefsky CA. Correlates of Impairment and Growth in Families of Young Autistic Children. J Autism Dev Disord;2024 (Apr 16)

The purpose of this project was to investigate potential correlates of family life impairment in families of young autistic children. This project incorporated measures of specific child and parent challenges in addition to a commonly used unidimensional measure of autism characteristics. In this way, we could assess whether such challenges explain variance in family life impairment, and whether their inclusion diminish associations between autism characteristics and family life impairment. Cross-sectional data were collected from 564 parents of autistic children aged 2 to 5 years who participated in a larger online study. Participants completed measures on child characteristics (autism characteristics, emotion dysregulation, speaking ability, flexibility, and sleep problems), parent depression, and family life impairment, using the Family Life Impairment Scale (FLIS). Multiple linear regression models were generated to examine whether any of the independent variables were associated with the four domains of the FLIS. Models controlled for child age and sex, parent education, and single-parent homes. All independent variables were associated with impairment in one or more FLIS domains. None of the primary independent variables were significantly associated with positive growth. More overt characteristics and behaviors (e.g., autism characteristics, reactivity, speaking ability, and flexibility) were associated with impairment in domains that reflected a family’s ability to navigate the community. However, sleep challenges and parent and child emotional difficulties were most strongly associated with parent impairment. Findings suggests that families may have different needs across contexts and provide new avenues through which they might be better supported.

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5. Morales-Hidalgo P, Voltas N, Canals J. Self-perceived bullying victimization in pre-adolescents on the autism spectrum: EPINED study. Autism;2024 (Apr 16):13623613241244875.

Autistic individuals face a higher risk of various forms of victimization throughout their lives, with bullying being especially prevalent during their school years. Previous studies indicate that autistic children are 2.4 times more likely to be bullied than their typically developing peers and twice as vulnerable as those with other disabilities. However, the extent of this issue among Spanish schoolchildren with autism remains unexplored. In addition, there is no information regarding the presence of bullying victimization in children with marked but undiagnosed autistic traits (i.e. subthreshold autistic traits). This study examines the self-reported prevalence of bullying victimization in autistic pre-adolescents and those with subthreshold autistic traits, comparing them with peers without neurodevelopmental conditions. The study involved 323 participants (11 and 12 years old; 45 with autism or subthreshold autistic traits) from Spanish general education schools. The results revealed a higher rate of bullying victimization among autistic participants (58%; 3.1 times higher risk) and those with subthreshold autistic traits (27%; 1.5 times higher risk) compared with their peers without neurodevelopmental conditions (18.3%). Victimization was linked to more intense restrictive behaviours and increased behavioural and emotional problems. No significant associations were found with other individual or family factors. Our findings underscore the heightened risk of bullying faced by autistic pre-adolescents and those with subthreshold autistic traits at school, emphasizing the need to identify and implement preventive measures to mitigate bullying and its detrimental impact on their mental well-being and overall quality of life.

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6. Nie W, Zhou B, Wang Z, Chen B, Wang X, Hu C, Li H, Xu Q, Xu X, Liu H. Computational Interpersonal Communication Model for Screening Autistic Toddlers: A Case Study of Response-to-Name. IEEE J Biomed Health Inform;2024 (Apr 16);Pp

Interpersonal communication facilitates symptom measures of autistic sociability to enhance clinical decision-making in identifying children with autism spectrum disorder (ASD). Traditional methods are carried out by clinical practitioners with assessment scales, which are subjective to quantify. Recent studies employ engineering technologies to analyze children’s behaviors with quantitative indicators, but these methods only generate specific rule-driven indicators that are not adaptable to diverse interaction scenarios. To tackle this issue, we propose a Computational Interpersonal Communication Model (CICM) based on psychological theory to represent dyadic interpersonal communication as a stochastic process, providing a scenario-independent theoretical framework for evaluating autistic sociability. We apply CICM to the response-to-name (RTN) with 48 subjects, including 30 toddlers with ASD and 18 typically developing (TD), and design a joint state transition matrix as quantitative indicators. Paired with machine learning, our proposed CICM-driven indicators achieve consistencies of 98.44% and 83.33% with RTN expert ratings and ASD diagnosis, respectively. Beyond outstanding screening results, we also reveal the interpretability between CICM-driven indicators and expert ratings based on statistical analysis.

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7. Rakap S, Balikci S. Enhancing IEP Goal Development for Preschoolers with Autism: A Preliminary Study on ChatGPT Integration. J Autism Dev Disord;2024 (Apr 16)

PURPOSE: The impact of well-crafted IEP goals on student outcomes is well-documented, but creating high-quality goals can be a challenging task for many special education teachers. This study aims to investigate potential effectiveness of using ChatGPT, an AI technology, in supporting development of high-quality, individualized IEP goals for preschool children with autism. METHODS: Thirty special education teachers working with preschool children with autism were randomly assigned to either the ChatGPT or control groups. Both groups received written guidelines on how to write SMART IEP goals, but only the ChatGPT group was given handout on how to use ChatGPT during IEP goal writing process. Quality of IEP goals written by the two groups was compared using a two-sample t-test, and categorization of goals by developmental domains was reported using frequency counts. RESULTS: Results indicate that using ChatGPT significantly improved the quality of IEP goals developed by special education teachers compared to those who did not use the technology. Teachers in the ChatGPT group had a higher proportion of goals targeting communication, social skills, motor/sensory, and self-care skills, while teachers in the control group had a higher proportion of goals targeting preacademic skills and behaviors. CONCLUSION: The potential of ChatGPT as an effective tool for supporting special education teachers in developing high-quality IEP goals suggests promising implications for improving outcomes for preschool children with autism. Its integration may offer valuable assistance in tailoring individualized goals to meet the diverse needs of students in special education settings.

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8. Shuster CL, Brennan PA, Carter BS, Check J, D’Sa V, Graff JC, Helderman J, Hofheimer JA, Joseph RM, Murphy LE, O’Connor TG, O’Shea TM, Pievsky M, Sheinkopf SJ, Shuffrey LC, Smith LM, Wu PC, Lester BM. Developmental characteristics and accuracy of autism screening among two-year-old toddlers in the ECHO program. Pediatr Res;2024 (Apr 15)

BACKGROUND: The Modified Checklist for Autism in Toddlers (M-CHAT) is a common pediatric screening tool with mixed accuracy findings. Prior evidence supports M-CHAT screening for developmental concerns, especially in toddlers born preterm. This study examined M-CHAT accuracy in a large, nationwide sample. METHODS: 3393 participants from the Environmental influences on Child Health Outcomes (ECHO) program were included. Harmonized M-CHAT (M-CHAT-H) results were compared with parent-reported autism diagnosis and autism-related characteristics to assess accuracy for term and preterm children, together and separately. Generalized estimating equations, clustering for ECHO cohort and controlling for demographic covariates, were used to examine associations between developmental and behavioral characteristics with M-CHAT-H accuracy. RESULTS: Sensitivity of the M-CHAT-H ranged from 36 to 60%; specificity ranged from 88 to 99%. Positive M-CHAT-H was associated with more developmental delays and behavior problems. Children with severe motor delays and more autism-related problems were more likely to have a false-negative M-CHAT-H. Children with fewer behavior problems and fewer autism-related concerns were more likely to have a false-positive screen. CONCLUSION: The M-CHAT-H accurately detects children at low risk for autism and children at increased risk with moderate accuracy. These findings support use of the M-CHAT-H in assessing autism risk and developmental and behavioral concerns in children. IMPACT: Previous literature regarding accuracy of the Modified Checklist for Autism in Toddlers (M-CHAT) is mixed but this study provides evidence that the M-CHAT performs well in detecting children at low risk for autism and consistently detects children with developmental delays and behavioral problems. The M-CHAT moderately detects children at increased risk for autism and remains a useful screening tool. This study examines M-CHAT accuracy in a large-scale, nationwide sample, examining associations between screening accuracy and developmental outcomes. These findings impact pediatric screening for autism, supporting continued use of the M-CHAT while further elucidating the factors associated with inaccurate screens.

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9. Wadle SL, Ritter TC, Wadle TTX, Hirtz JJ. Topography and ensemble activity in auditory cortex of a mouse model of Fragile-X-Syndrome. eNeuro;2024 (Apr 16)

Autism spectrum disorder (ASD) is often associated with social communication impairments and specific sound processing deficits, for example problems in following speech in noisy environments. To investigate underlying neuronal processing defects located in the auditory neocortex (AC), we performed two-photon Ca(2+) imaging in FMR1 (Fragile X Messenger Ribonucleoprotein 1) knockout (KO) mice, a model for Fragile-X-Syndrome (FXS), the most common cause of hereditary ASD in humans. For primary AC (A1) and the anterior auditory field (AAF), topographic frequency representation was less ordered compared to control animals. We additionally analyzed ensemble AC activity in response to various sounds and found subfield-specific differences. In A1, ensemble correlations were lower in general, while in secondary AC (A2), correlations were higher in response to complex sounds, yet not to pure tones (PT). Furthermore, sound specificity of ensemble activity was decreased in AAF. Repeating these experiments one week later revealed no major differences regarding representational drift. Nevertheless, we found subfield- and genotype-specific changes in ensemble correlation values between the two times points, hinting at alterations in network stability in FMR1 KO mice. These detailed insights into AC networks activity and topography in FMR1 KO mice add to the understanding of auditory processing defects in FXS.Significance statement Communicative challenges often observed in people with autism spectrum disorder might be due to defects in cortical brain circuits responsible for sound analysis. To investigate these in detail, we used a mouse model of Fragile-X-Syndrome, which often is associated with autism spectrum disorder in humans. We found several alterations compared to control animals, including a less well-ordered topography of frequency analysis in auditory cortex. Furthermore, neuronal population activity patterns in response to various sounds were altered. This was also highly dependent on whether pure tones or complex sounds were presented. These data help to understand the causes of sound processing defects in Fragile-X-Syndrome.

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