1. Abbas SQ, Chi L, Chen YP. DeepMNF: Deep Multimodal Neuroimaging Framework for Diagnosing Autism Spectrum Disorder. Artif Intell Med;2023 (Feb);136:102475.

The growing prevalence of neurological disorders, e.g., Autism Spectrum Disorder (ASD), demands robust computer-aided diagnosis (CAD) due to the diverse symptoms which require early intervention, particularly in young children. The absence of a benchmark neuroimaging diagnostics paves the way to study transitions in the brain’s anatomical structure and neurological patterns associated with ASD. The existing CADs take advantage of the large-scale baseline dataset from the Autism Brain Imaging Data Exchange (ABIDE) repository to improve diagnostic performance, but the involvement of multisite data also amplifies the variabilities and heterogeneities that hinder satisfactory results. To resolve this problem, we propose a Deep Multimodal Neuroimaging Framework (DeepMNF) that employs Functional Magnetic Resonance Imaging (fMRI) and Structural Magnetic Resonance Imaging (sMRI) to integrate cross-modality spatiotemporal information by exploiting 2-dimensional time-series data along with 3-dimensional images. The purpose is to fuse complementary information that increases group differences and homogeneities. To the best of our knowledge, our DeepMNF achieves superior validation performance than the best reported result on the ABIDE-1 repository involving datasets from all available screening sites. In this work, we also demonstrate the performance of the studied modalities in a single model as well as their possible combinations to develop the multimodal framework.

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2. Baghdadli A, Audras-Torrent L, Rattaz C, Gonnier V, Ferrando L, Michelon C, Odoyer R, Maffre T, Picot MC. Multistage screening process for neurodevelopmental disorders in siblings of children with autism: the FRATSA protocol study. BMJ Open;2023 (Jan 30);13(1):e066520.

INTRODUCTION: The elevated rates of neurodevelopmental disorders (NDDs) among siblings of children with autism spectrum disorder (ASD) raise concerns about their developmental monitoring and development. The main aim of this study is to assess the feasibility and acceptability of a standardised screening process on a large sample of siblings. METHODS AND ANALYSIS: This prospective study will assess the feasibility of a selective and multi-stage screening process for NDD performed on 384 siblings of children with confirmed ASD. Stage 1 will consist of the screening of NDD performed using online parental questionnaires (Social Responsiveness Scale, IdentiDys scale, DCDQ, parental concerns) through a web platform. In cases of a positive result, the second stage, consisting of a clinical semi-structured interview with a psychologist, will be proposed to the sibling before referral for diagnosis and treatment, if necessary. Approximately 12 months after stage 2, parents will be contacted by telephone to collect the diagnosis established following the referrals and their level of satisfaction concerning the screening process. Based on an expected participation rate of 50%, to estimate this rate with an accuracy of 5%, it is necessary to screen 384 subjects. ETHICS AND DISSEMINATION: The Ethics Committee on the Research of Human Subjects of Paris (Ile de France VII) approved this study in March 2022 (number: 2021-A02241-40). Express consent is required from all participants. Findings from the cohort study will be disseminated by publication of peer-reviewed manuscripts, presentations at scientific meetings and conferences with associated teams. TRIAL REGISTRATION NUMBER: NCT05512637.

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3. Banerjee A. Internet Use and Autism Spectrum Disorders. J Autism Dev Disord;2023 (Jan 30)

N/a.

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4. Chiu HT, Ip IN, Ching FNY, Wong BP, Lui WH, Tse CS, Wong SWH. Resting Heart Rate Variability and Emotion Dysregulation in Adolescents with Autism Spectrum Disorder. J Autism Dev Disord;2023 (Jan 30)

Emotion dysregulation is common among individuals with autism spectrum disorder (ASD). This study examined the relationship between emotion dysregulation and resting heart rate variability (HRV), a marker of the autonomic nervous system, in ASD adolescents. Resting HRV data were collected from ASD (n = 23) and typically developing (TD) adolescents (n = 32) via short-term electrocardiogram. Parents/caregivers reported participants’ level of emotion dysregulation with the Emotion Dysregulation Inventory (EDI). Controlling for the effects of age and gender, regression analyses revealed moderating effects of group, suggesting that lower resting HRV was more strongly associated with greater emotion dysregulation in ASD than TD adolescents. The results support the view that disruptions in autonomic functioning may contribute to emotion dysregulation in ASD.

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5. Dhanasekara CS, Ancona D, Cortes L, Hu A, Rimu AH, Robohm-Leavitt C, Payne D, Wakefield SM, Mastergeorge AM, Kahathuduwa CN. Association Between Autism Spectrum Disorders and Cardiometabolic Diseases: A Systematic Review and Meta-analysis. JAMA Pediatr;2023 (Jan 30)

IMPORTANCE: Although the increased risk of obesity among individuals with autism has been well established, evidence on the association between autism, cardiometabolic disorders, and obesity remains inconclusive. OBJECTIVE: To examine the association between autism spectrum disorders and cardiometabolic diseases in a systematic review and meta-analysis. DATA SOURCES: PubMed, Scopus, Web of Science, ProQuest, Embase, and Ovid databases were searched from inception through July 31, 2022, without restrictions on date of publication or language. STUDY SELECTION: Observational or baseline data of interventional studies reporting the prevalence of cardiometabolic risk factors (ie, diabetes, hypertension, dyslipidemia, atherosclerotic macrovascular disease) among children and/or adults with autism and matched with participants without autism were included. DATA EXTRACTION AND SYNTHESIS: Screening, data extraction, and quality assessment were performed independently by at least 2 researchers. DerSimonian-Laird random-effects meta-analyses were performed using the meta package in R. MAIN OUTCOMES AND MEASURES: Relative risks (RRs) of diabetes, hypertension, dyslipidemia, and atherosclerotic macrovascular disease among individuals with autism were the primary outcomes. Secondary outcomes included the RR of type 1 and type 2 diabetes, heart disease, stroke, and peripheral vascular disease. RESULTS: A total of 34 studies were evaluated and included 276 173 participants with autism and 7 733 306 participants without autism (mean [range] age, 31.2 [3.8-72.8] years; pooled proportion [range] of female individuals, 47% [0-66%]). Autism was associated with greater risks of developing diabetes overall (RR, 1.57; 95% CI, 1.23-2.01; 20 studies), type 1 diabetes (RR, 1.64; 95% CI, 1.06-2.54; 6 studies), and type 2 diabetes (RR, 2.47; 95% CI, 1.30-4.70; 3 studies). Autism was also associated with increased risks of dyslipidemia (RR, 1.69; 95% CI, 1.20-2.40; 7 studies) and heart disease (RR, 1.46; 95% CI, 1.42-1.50; 3 studies). Yet, there was no significantly associated increased risk of hypertension and stroke with autism (RR, 1.22; 95% CI, 0.98-1.52; 12 studies; and RR, 1.19; 95% CI, 0.63-2.24; 4 studies, respectively). Meta-regression analyses revealed that children with autism were at a greater associated risk of developing diabetes and hypertension compared with adults. High between-study heterogeneity was a concern for several meta-analyses. CONCLUSIONS AND RELEVANCE: Results suggest that the associated increased risk of cardiometabolic diseases should prompt clinicians to vigilantly monitor individuals with autism for potential contributors, signs of cardiometabolic disease, and their complications.

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6. George-Levi S, Laslo-Roth R, Ben-Yaakov L. Differences in Interpersonal Resources and Risk Factors Among Mothers and Fathers of Children on the Autism Spectrum: A Serial Mediation Model. J Autism Dev Disord;2023 (Jan 30)

Mothers and fathers of children on the autism spectrum may differ in their perception of their interpersonal resources and risk factors. Fathers (114) and mothers (507) of children on the autism spectrum participated in the study. Fathers (vs. mothers) reported lower interpersonal resources (interpersonal emotion regulation and perceived support from friends and formal sources, but not family) and higher levels of interpersonal risk factors (social, not emotional, loneliness). A serial mediation model indicated that parents’ gender predicted interpersonal emotion regulation which in turn related to parents’ social loneliness directly and indirectly through perceived social support. Fathers of children on the autism spectrum may differ from mothers in perceptions of interpersonal resources and risk factors related to parents’ social belonging needs.

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7. Ryan NM, Heron EA. Evidence for parent-of-origin effects in autism spectrum disorder: a narrative review. J Appl Genet;2023 (Jan 30)

Autism spectrum disorder (ASD) is a heterogeneous group of early-onset neurodevelopmental disorders known to be highly heritable with a complex genetic architecture. Abnormal brain developmental trajectories that impact synaptic functioning, excitation-inhibition balance and brain connectivity are now understood to play a central role in ASD. Ongoing efforts to identify the genetic underpinnings still prove challenging, in part due to phenotypic and genetic heterogeneity.This review focuses on parent-of-origin effects (POEs), where the phenotypic effect of an allele depends on its parental origin. POEs include genomic imprinting, transgenerational effects, mitochondrial DNA, sex chromosomes and mutational transmission bias. The motivation for investigating these mechanisms in ASD has been driven by their known impacts on early brain development and brain functioning, in particular for the most well-documented POE, genomic imprinting. Moreover, imprinting is implicated in syndromes such as Angelman and Prader-Willi, which frequently share comorbid symptoms with ASD. In addition to other regions in the genome, this comprehensive review highlights the 15q11-q13 and 7q chromosomal regions as well as the mitochondrial DNA as harbouring the majority of currently identified POEs in ASD.

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8. Torenvliet C, Groenman AP, Radhoe TA, Agelink van Rentergem JA, Van der Putten WJ, Geurts HM. A longitudinal study on cognitive aging in autism. Psychiatry Res;2023 (Jan 18);321:115063.

Longitudinal studies on cognitive aging in autism are scarce, and largely underpowered, yet essential to obtain more conclusive results on cognitive changes in autism during adulthood. In the largest longitudinal study on cognition thus far, we aimed to get more insight into cognitive aging in autism. As pre-registered, we computed reliable change indices (RCIs) and multilevel models to estimate cognitive changes in 128 autistic, and 112 non-autistic adults (range: 24-85 yrs.) over two to three timepoints (average interval: 3.5 yrs.). Participants were tested on 15 outcome measures, covering verbal memory, visual (working) memory, prospective memory, theory of mind, fluency, response speed, inhibition, planning, and switching. RCIs showed no significant differences between groups (autism/no-autism) in changes over time. Using multilevel models, most tasks showed sensitivity to cross-sectional age-related effects, and/or longitudinal changes, with worse performance at older age, and later timepoints. However, effects were not significantly different between the autism and no-autism group. This lack of group differences was substantiated by additional Bayesian analyses. In sum, the current study provides evidence for parallel (similar) cognitive aging in autism. Specifically, autistic individuals diagnosed in adulthood, without intellectual disability, do not seem at risk for accelerated cognitive decline.

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9. Wang R, Chaudhari P, Davatzikos C. Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies. Proc Natl Acad Sci U S A;2023 (Feb 7);120(6):e2211613120.

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multistudy magnetic resonance imaging consortia for diagnosing Alzheimer’s disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attributes such as gender, age, racial groups and different clinical studies and are unbiased under multiple fairness metrics such as demographic parity difference, equalized odds difference, equal opportunity difference, etc. We find that models that incorporate multisource data from demographic, clinical, genetic factors, and cognitive scores are also unbiased. These models have a better predictive AUC across subgroups than those trained only with imaging features, but there are also situations when these additional features do not help.

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10. Weir EM. Autism, Physical Health Conditions, and a Need for Reform. JAMA Pediatr;2023 (Jan 30)

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11. Zhang G, Li S, Yang L, Wang M, Chen G, Zhu D. [Analysis of NOVA2 gene variant in a child with Neurodevelopmental disorder with or without autistic features and/or structural brain abnormalities]. Zhonghua Yi Xue Yi Chuan Xue Za Zhi;2023 (Feb 10);40(2):213-216.

OBJECTIVE: To explore the genetic basis for a child with Neurodevelopmental disorder with or without autistic features and/or structural brain abnormalities (NEDASB). METHODS: A child with NEDASB who presented at the Third Affiliated Hospital of Zhengzhou University in July 2021 was selected as the subject. Peripheral blood samples of the child and her parents were collected and subjected to high-throughput sequencing. Candidate variant was verified by Sanger sequencing and bioinformatic analysis. RESULTS: The child was found to harbor a heterozygous c.820_828delinsCTTCA (p.Thr274Leufs*121) variant of the NOVA2 gene, for which both of her parents were of wild type. The variant was predicted as pathogenic based on the guidelines from the American College of Medical Genetics and Genomics. CONCLUSION: The heterozygous c.820_828delinsCTTCA (p.Thr274Leufs*121) variant of the NOVA2 gene probably underlay the disease in this child. Above finding has enriched the spectrum of NOVA2 gene variants and provided a basis for genetic counseling and prenatal diagnosis for this family.

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