1. Bhat AN. {{Motor Impairment Increases in Children With Autism Spectrum Disorder as a Function of Social Communication, Cognitive and Functional Impairment, Repetitive Behavior Severity, and Comorbid Diagnoses: A SPARK Study Report}}. {Autism Res}. 2020.
Eighty-seven percent of a large sample of children with autism spectrum disorder (ASD) are at risk for motor impairment (Bhat, Physical Therapy, 2020, 100, 633-644). In spite of the high prevalence for motor impairment in children with ASD, it is not considered among the diagnostic criteria or specifiers within DSM-V. In this article, we analyzed the SPARK study dataset (n = 13,887) to examine associations between risk for motor impairment using the Developmental Coordination Disorder-Questionnaire (DCD-Q), social communication impairment using the Social Communication Questionnaire (SCQ), repetitive behavior severity using the Repetitive Behaviors Scale – Revised (RBS-R), and parent-reported categories of cognitive, functional, and language impairments. Upon including children with ASD with cognitive impairments, 88.2% of the SPARK sample was at risk for motor impairment. The relative risk ratio for motor impairment in children with ASD was 22.2 times greater compared to the general population and that risk further increased up to 6.2 with increasing social communication (5.7), functional (6.2), cognitive (3.8), and language (1.6) impairments as well as repetitive behavior severity (5.0). Additionally, the magnitude of risk for motor impairment (fine- and gross-motor) increased with increasing severity of all impairment types with medium to large effects. These findings highlight the multisystem nature of ASD, the need to recognize motor impairments as one of the diagnostic criteria or specifiers for ASD, and the need for appropriate motor screening and assessment of children with ASD. Interventions must address not only the social communication and cognitive/behavioral challenges of children with ASD but also their motor function and participation. LAY ABSTRACT: Eighty-eight percent of the SPARK sample of children with ASD were at risk for motor impairment. The relative risk for motor impairment was 22.2 times greater in children with ASD compared to the general population and the risk increased with more social communication, repetitive behavior, cognitive, and functional impairment. It is important to recognize motor impairments as one of the diagnostic criteria or specifiers for ASD and there is a need to administer appropriate motor screening, assessment, and interventions in children with ASD.
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2. Ding Q, Zhang F, Feng Y, Wang H. {{Carbamazepine Restores Neuronal Signaling, Protein Synthesis, and Cognitive Function in a Mouse Model of Fragile X Syndrome}}. {International journal of molecular sciences}. 2020; 21(23).
Fragile X syndrome (FXS) is a leading genetic disorder of intellectual disability caused by the loss of the functional fragile X mental retardation protein (FMRP). To date, there is no efficacious mechanism-based medication for FXS. With regard to potential disease mechanisms in FXS, it is widely accepted that the lack of FMRP causes elevated protein synthesis and deregulation of neuronal signaling. Abnormal enhancement of the ERK½ (extracellular signal-regulated kinase ½) and PI3K-Akt (Phosphoinositide 3 kinase-protein kinase B) signaling pathways has been identified in both FXS patients and FXS mouse models. In this study, we show that carbamazepine, which is an FDA-approved drug and has been mainly used to treat seizure and neuropathic pain, corrects cognitive deficits including passive avoidance and object location memory in FXS mice. Carbamazepine also rescues hyper locomotion and social deficits. At the cellular level, carbamazepine dampens the elevated level of ERK½ and Akt signaling as well as protein synthesis in FXS mouse neurons. Together, these results advocate repurposing carbamazepine for FXS treatment.
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3. Dodds RL. {{Helping Optimize Language Acquisition (HOLA) Online Parent Training Modules for Latinx Parents of Toddlers at Risk for ASD: Protocol for a Pilot Funded by the Organization for Autism Research}}. {JMIR research protocols}. 2020; 9(12): e18004.
BACKGROUND: Culturally competent parent training in evidence-based intervention for autism spectrum disorder (ASD) can provide young Latinx children from underserved communities with early interventional support while they wait for professional services, thus reducing the impact of intervention delays. Providing parents with brief bilingual training in Pivotal Response Treatment (PRT) is a strategy that can overcome these barriers and is inexpensive to disseminate. Brief PRT training has been shown to significantly improve joint attention, expressive language, responsivity, and adaptive skills in young children with ASD. However, it is unknown whether an interactive, culturally competent online parent training in PRT is effective in a Latinx population. OBJECTIVE: To this end, we will recruit 24 children (16-36 months old) at risk for ASD and their parent(s) from East and South Los Angeles and provide them with a series of 6 online learning modules in their choice of Spanish or English. METHODS: This pilot study will utilize a single-group, pilot, pre-post design with follow-up assessments 6 weeks later. Linear mixed-effects model analysis will be used to explore most parent-reported and coded outcomes. RESULTS: Brief online parent training in evidence-based treatments has the capacity to increase access to culturally competent early communication interventions for young children at risk for ASD. CONCLUSIONS: The results of this trial may have particular salience in additional underresourced communities where children have limited access to interventions prior to entering school. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/18004.
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4. Harris JF, Coffield CN, Janvier YM, Mandell D, Cidav Z. {{Validation of The Developmental Check-In Tool for Low-Literacy Autism Screening}}. {Pediatrics}. 2020.
BACKGROUND: Persistent disparities exist in early identification of autism spectrum disorder (ASD) among children from low-income families who are racial and/or ethnic minorities and where English is not the primary language. Parental literacy and level of maternal education may contribute to disparities. The Developmental Check-In (DCI) is a visually based ASD screening tool created to reduce literacy demands and to be easily administered and scored across settings. In a previous study, the DCI showed acceptable discriminative ability between ASD versus non-ASD in a young, underserved sample at high-risk for ASD. In this study, we tested the DCI among an unselected, general sample of young underserved children. METHODS: Six hundred twenty-four children ages 24 to 60 months were recruited through Head Start and Early Head Start. Parents completed the DCI, Modified Checklist for Autism in Toddlers, Revised with Follow-Up, and Social Communication Questionnaire. Children scoring positive on any measure received evaluation for ASD. Those screening negative on both Modified Checklist for Autism in Toddlers, Revised with Follow-Up and Social Communication Questionnaire were considered non-ASD. RESULTS: Parents were primarily Hispanic, reported high school education or less, and had public or no insurance. The DCI demonstrated good discriminative power (area under the curve = 0.80), performing well across all age groups, genders, levels of maternal education, primary language, and included ethnic and racial groups. Item-level analyses indicated that 24 of 26 DCI items discriminated ASD from non-ASD. CONCLUSIONS: The DCI is a promising ASD screening tool for young, underserved children and may be of particular value in screening for ASD for those with low literacy levels or with limited English proficiency.
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5. Khozaei A, Moradi H, Hosseini R, Pouretemad H, Eskandari B. {{Early screening of autism spectrum disorder using cry features}}. {PLoS One}. 2020; 15(12): e0241690.
The increase in the number of children with autism and the importance of early autism intervention has prompted researchers to perform automatic and early autism screening. Consequently, in the present paper, a cry-based screening approach for children with Autism Spectrum Disorder (ASD) is introduced which would provide both early and automatic screening. During the study, we realized that ASD specific features are not necessarily observable in all children with ASD and in all instances collected from each child. Therefore, we proposed a new classification approach to be able to determine such features and their corresponding instances. To test the proposed approach a set of data relating to children between 18 to 53 months which had been recorded using high-quality voice recording devices and typical smartphones at various locations such as homes and daycares was studied. Then, after preprocessing, the approach was used to train a classifier, using data for 10 boys with ASD and 10 Typically Developed (TD) boys. The trained classifier was tested on the data of 14 boys and 7 girls with ASD and 14 TD boys and 7 TD girls. The sensitivity, specificity, and precision of the proposed approach for boys were 85.71%, 100%, and 92.85%, respectively. These measures were 71.42%, 100%, and 85.71% for girls, respectively. It was shown that the proposed approach outperforms the common classification methods. Furthermore, it demonstrated better results than the studies which used voice features for screening ASD. To pilot the practicality of the proposed approach for early autism screening, the trained classifier was tested on 57 participants between 10 to 18 months. These 57 participants consisted of 28 boys and 29 girls and the results were very encouraging for the use of the approach in early ASD screening.
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6. Kotila A, Hyvärinen A, Mäkinen L, Leinonen E, Hurtig T, Ebeling H, Korhonen V, Kiviniemi VJ, Loukusa S. {{Processing of pragmatic communication in ASD: a video-based brain imaging study}}. {Sci Rep}. 2020; 10(1): 21739.
Social and pragmatic difficulties in autism spectrum disorder (ASD) are widely recognized, although their underlying neural level processing is not well understood. The aim of this study was to examine the activity of the brain network components linked to social and pragmatic understanding in order to reveal whether complex socio-pragmatic events evoke differences in brain activity between the ASD and control groups. Nineteen young adults (mean age 23.6 years) with ASD and 19 controls (mean age 22.7 years) were recruited for the study. The stimulus data consisted of video clips showing complex social events that demanded processing of pragmatic communication. In the analysis, the functional magnetic resonance imaging signal responses of the selected brain network components linked to social and pragmatic information processing were compared. Although the processing of the young adults with ASD was similar to that of the control group during the majority of the social scenes, differences between the groups were found in the activity of the social brain network components when the participants were observing situations with concurrent verbal and non-verbal communication events. The results suggest that the ASD group had challenges in processing concurrent multimodal cues in complex pragmatic communication situations.
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7. Nakayama Y, Adachi K, Shioda N, Maeta S, Nanba E, Kugoh H. {{Establishment of FXS-A9 panel with a single human X chromosome from Fragile X syndrome-associated individual}}. {Experimental cell research}. 2020: 112419.
Fragile X syndrome (FXS) is the most common inheritable form of intellectual disability. FMR1, the gene responsible for FXS, is located on human chromosome Xq27.3 and contains a stretch of CGG trinucleotide repeats in its 5′ untranslated region. FXS is caused by CGG repeats that expand beyond 200, resulting in FMR1 silencing via promoter hypermethylation. The molecular mechanism underlying CGG repeat expansion, a fundamental cause of FXS, remains poorly understood, partly due to a lack of experimental systems. Accumulated evidence indicates that the large chromosomal region flanking a CGG repeat is critical for repeat dynamics. In the present study, we isolated and introduced whole human X chromosomes from healthy, FXS premutation carriers, or FXS patients who carried disease condition-associated CGG repeat lengths, into mouse A9 cells via microcell-mediated chromosome transfer. The CGG repeat length-associated methylation status and human FMR1 expression in these monochromosomal hybrid cells mimicked those in humans. Thus, this set of A9 cells containing CGG repeats from three different origins (FXS-A9 panel) may provide a valuable resource for investigating a series of genetic and epigenetic CGG repeat dynamics during FXS pathogenesis.
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8. Narzisi A. {{The Challenging Heterogeneity of Autism: Editorial for Brain Sciences Special Issue « Advances in Autism Research »}}. {Brain Sci}. 2020; 10(12).
My personal experience as Guest Editor of the Special Issue (SI) entitled « Advances in Autism Research » began with a nice correspondence with Andrew Meltzoff, from the University of Washington, Seattle (WA, USA), which, in hindsight, I consider as a good omen for the success of this Special Issue: « Dear Antonio… […].
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9. Nawa Y, Kimura H, Mori D, Kato H, Toyama M, Furuta S, Yu Y, Ishizuka K, Kushima I, Aleksic B, Arioka Y, Morikawa M, Okada T, Inada T, Kaibuchi K, Ikeda M, Iwata N, Suzuki M, Okahisa Y, Egawa J, Someya T, Nishimura F, Sasaki T, Ozaki N. {{Rare single-nucleotide DAB1 variants and their contribution to Schizophrenia and autism spectrum disorder susceptibility}}. {Human genome variation}. 2020; 7(1): 37.
Disabled 1 (DAB1) is an intracellular adaptor protein in the Reelin signaling pathway and plays an essential role in correct neuronal migration and layer formation in the developing brain. DAB1 has been repeatedly reported to be associated with neurodevelopmental disorders including schizophrenia (SCZ) and autism spectrum disorders (ASD) in genetic, animal, and postmortem studies. Recently, increasing attention has been given to rare single-nucleotide variants (SNVs) found by deep sequencing of candidate genes. In this study, we performed exon-targeted resequencing of DAB1 in 370 SCZ and 192 ASD patients using next-generation sequencing technology to identify rare SNVs with a minor allele frequency <1%. We detected two rare missense mutations (G382C, V129I) and then performed a genetic association study in a sample comprising 1763 SCZ, 380 ASD, and 2190 healthy control subjects. Although no statistically significant association with the detected mutations was observed for either SCZ or ASD, G382C was found only in the case group, and in silico analyses and in vitro functional assays suggested that G382C alters the function of the DAB1 protein. The rare variants of DAB1 found in the present study should be studied further to elucidate their potential functional relevance to the pathophysiology of SCZ and ASD. Lien vers le texte intégral (Open Access ou abonnement)
10. Rahman MM, Usman OL, Muniyandi RC, Sahran S, Mohamed S, Razak RA. {{A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder}}. {Brain Sci}. 2020; 10(12).
Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning’s speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.
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11. Wagley N, Lajiness-O’Neill R, Hay JSF, Ugolini M, Bowyer SM, Kovelman I, Brennan JR. {{Predictive Processing during a Naturalistic Statistical Learning Task in ASD}}. {eNeuro}. 2020; 7(6).
Children’s sensitivity to regularities within the linguistic stream, such as the likelihood that syllables co-occur, is foundational to speech segmentation and language acquisition. Yet, little is known about the neurocognitive mechanisms underlying speech segmentation in typical development and in neurodevelopmental disorders that impact language acquisition such as autism spectrum disorder (ASD). Here, we investigate the neural signals of statistical learning in 15 human participants (children ages 8-12) with a clinical diagnosis of ASD and 14 age-matched and gender-matched typically developing peers. We tracked the evoked neural responses to syllable sequences in a naturalistic statistical learning corpus using magnetoencephalography (MEG) in the left primary auditory cortex, posterior superior temporal gyrus (pSTG), and inferior frontal gyrus (IFG), across three repetitions of the passage. In typically developing children, we observed a neural index of learning in all three regions of interest (ROIs), measured by the change in evoked response amplitude as a function of syllable surprisal across passage repetitions. As surprisal increased, the amplitude of the neural response increased; this sensitivity emerged after repeated exposure to the corpus. Children with ASD did not show this pattern of learning in all three regions. We discuss two possible hypotheses related to children’s sensitivity to bottom-up sensory deficits and difficulty with top-down incremental processing.
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12. Wang CR, Sun YH, Xu T. {{[Cohort studies on the association between maternal smoking during pregnancy and autism spectrum disorders of children: a Meta-analysis]}}. {Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi}. 2020; 41(11): 1921-6.
Objective: To examine the association between maternal smoking during pregnancy and autism spectrum disorders (ASD) of children through Meta-analysis. Methods: We searched data on relative risk (RR) and 95% confidence interval (CI) on cohort studies published between January 2000 and July 2019 from PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang database. We used Stata software 15.1 to perform the Meta analysis with random effect model applied to pool RRs according to the results of heterogeneity test through subgroup analysis and Meta regression analysis to explore the potential heterogeneity, publication bias and sensitivity. Results: A total of eleven cohort studies involving 1 631 618 samples and 9 276 ASD cases were included in this Meta-analysis. Results showed that maternal smoking was associated with the increased risk of autism spectrum disorder (RR=1.16, 95%CI: 1.02-1.32). For subgroup analysis, the pooled RR for prospective studies (RR=1.16, 95%CI: 1.10-1.23) appeared higher than that in the retrospective studies (RR=0.92, 95%CI: 0.83-1.06). The pooled RR for studies with adjusted confounding factors (RR=1.13, 95%CI: 1.04-1.23) was higher than that without (RR=1.12, 95%CI: 1.04-1.20). In studies that exposure to smoking assessed before delivery, inter-study heterogeneity appeared higher than those after delivery. Sample size and time of assessment on smoking seemed the sources of heterogeneity. No significant publication bias was observed in this study, and the results were quite stable. Conclusions: Maternal smoking was associated with the increased risk of autism spectrum disorder. However, value of the combined effect seemed low. High-quality, large-sample, and prospective cohort studies should be conducted to further verify the causal relationship, based on the correction of potential confounding factors.
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13. Zwaigenbaum L. {{Autism Screening: An Important Step Forward but « Miles to Go Before We Sleep »}}. {Pediatrics}. 2020.
