Pubmed du 30/12/23
1. Hegemann L, Bugge Askeland R, Barbo Valand S, Øyen AS, Schjølberg S, Bal VH, Bishop SL, Stoltenberg C, von Soest T, Hannigan LJ, Havdahl A. Measuring autism-associated traits in the general population: Factor structure and measurement invariance across sex and diagnosis status of the Social Communication Questionnaire. Autism;2023 (Dec 30):13623613231219306.
Using questionnaires in research relies on the expectation that they measure the same things across different groups of individuals. If this is not true, then interpretations of results can be misleading when researchers compare responses across different groups of individuals or use in it a group that differs from that in which the questionnaire was developed. For the questionnaire we investigated, the Social Communication Questionnaire (SCQ), we found that parents of boys and girls responded to questionnaire items in largely the same way but that the SCQ measured traits and behaviors slightly differently depending on whether the children had autism. Based on these results, we concluded that researchers using this questionnaire should carefully consider these differences when deciding how to interpret findings. SCQ scores as a reflection of « autism-associated traits » in samples that are mostly or entirely made up of individuals without an autism diagnosis may be misleading and we encourage a more precise interpretation of scores as a broader indication of social-communicative and behavioral traits.
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2. Li C, Zhang R, Zhou Y, Li T, Qin R, Li L, Yuan X, Wang L, Wang X. Gray matter asymmetry alterations in children and adolescents with comorbid autism spectrum disorder and attention-deficit/hyperactivity disorder. Eur Child Adolesc Psychiatry;2023 (Dec 30)
Despite the high coexistence of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) (ASD + ADHD), the underlying neurobiological basis of this disorder remains unclear. Altered brain structural asymmetries have been verified in ASD and ADHD, respectively, making brain asymmetry a candidate for characterizing this coexisting disorder. Here, we measured the gray matter (GM) volume asymmetry in ASD + ADHD versus ASD without ADHD (ASD-only), ADHD without ASD (ADHD-only), and typically developing controls (TDc). High-resolution T1-weighted data from 48 ASD + ADHD, 63 ASD-only, 32 ADHD-only, and 211 matched TDc were included in our study. We also assessed brain-behavior relationships and the effects of age on GM asymmetry. We found that there were both shared and disorder-specific GM volume asymmetry alterations in ASD + ADHD, ASD-only, and ADHD-only compared with TDc. This finding demonstrates that ASD + ADHD is neither an endophenocopy nor an additive pathology of ASD and ADHD, but an entirely different neuroanatomical pathology. In addition, ASD + ADHD displayed altered GM volume asymmetries in the prefrontal regions responsible for executive function and theory of mind compared with ASD-only. We also found significant effects of age on GM asymmetry. The present study may provide structural insights into the neural basis of ASD + ADHD.
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3. Nordin V, Palmgren M, Lindbladh A, Bölte S, Jonsson U. School absenteeism in autistic children and adolescents: A scoping review. Autism;2023 (Dec 30):13623613231217409.
Autistic children and teenagers are, on average, absent from school more than their peers. The aim of this review was to provide an overview of the research on absence from school in autistic learners in primary and secondary school, to help guide future research. We sifted through 4632 reports and found 42 studies with a focus on school absence and autism. We looked at how, when, and where the studies were conducted. We also summarized the results and outlined how absence was measured in the studies. Absence from school may lead to problems later in life, like incomplete education and unemployment. It is therefore important to know how common this problem is among autistic learners, what the reasons may be, and what type of support they need. The studies were from high-income countries and were mainly published in the last 10 years. Studies based on school registers from the United States and the United Kingdom clearly showed that children and teenagers with autism had higher risk of school absence than those without autism. Absence was often linked to problems with mental health or additional neurodevelopmental conditions. Several studies also showed that absence in autistic children and adolescents was related to problems in school, like bullying or lack of knowledge about autism. Support programs were only evaluated in a few studies with a small number of study participants. We conclude that more research is needed to better understand why autistic learners are absent and what they need to thrive in school.
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4. Xu K, Sun Z, Qiao Z, Chen A. Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method. Complement Ther Clin Pract;2023 (Dec 30);54:101825.
PURPOSE: This study aimed to investigate the relationship between physical fitness, gray matter volume (GMV), and autism severity in children with autism spectrum disorder (ASD). Besides, we sought to diagnose autism severity associated with physical fitness and GMV using machine learning methods. METHODS: Ninety children diagnosed with ASD underwent physical fitness tests, magnetic resonance imaging scans, and autism severity assessments. Diagnosis models were established using extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms. Hyperparameters were optimized through the grid search cross-validation method. The shapley additive explanation (SHAP) method was employed to explain the diagnosis results. RESULTS: Our study revealed associations between muscular strength in physical fitness and GMV in specific brain regions (left paracentral lobule, bilateral thalamus, left inferior temporal gyrus, and cerebellar vermis I-II) with autism severity in children with ASD. The accuracy (95 % confidence interval) of the XGB, RF, SVM, and DT models were 77.9 % (77.3, 78.6 %), 72.4 % (71.7, 73.2 %), 71.9 % (71.1, 72.6 %), and 66.9 % (66.2, 67.7 %), respectively. SHAP analysis revealed that muscular strength and thalamic GMV significantly influenced the decision-making process of the XGB model. CONCLUSION: Machine learning methods can effectively diagnose autism severity associated with physical fitness and GMV in children with ASD. In this respect, the XGB model demonstrated excellent performance across various indicators, suggesting its potential for diagnosing autism severity.
