1. Casartelli L, Federici A, Biffi E, Molteni M, Ronconi L. {{Are We « Motorically » Wired to Others? High-Level Motor Computations and Their Role in Autism}}. {Neuroscientist};2017 (Dec 1):1073858417750466.
High-level motor computations reflect abstract components far apart from the mere motor performance. Neural correlates of these computations have been explored both in nonhuman and human primates, supporting the idea that our brain recruits complex nodes for motor representations. Of note, these computations have exciting implications for social cognition, and they also entail important challenges in the context of autism. Here, we focus on these challenges benefiting from recent studies addressing motor interference, motor resonance, and high-level motor planning. In addition, we suggest new ideas about how one maps and shares the (motor) space with others. Taken together, these issues inspire intriguing and fascinating questions about the social tendency of our high-level motor computations, and it may indicate that we are « motorically » wired to others. Thus, after furnishing preliminary insights on putative neural nodes involved in these computations, we focus on how the hypothesized social nature of high-level motor computations may be anomalous or limited in autism, and why this represents a critical challenge for the future.
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2. Dona O, Hall GB, Noseworthy MD. {{Temporal fractal analysis of the rs-BOLD signal identifies brain abnormalities in autism spectrum disorder}}. {PLoS One};2017;12(12):e0190081.
BACKGROUND: Brain connectivity in autism spectrum disorders (ASD) has proven difficult to characterize due to the heterogeneous nature of the spectrum. Connectivity in the brain occurs in a complex, multilevel and multi-temporal manner, driving the fluctuations observed in local oxygen demand. These fluctuations can be characterized as fractals, as they auto-correlate at different time scales. In this study, we propose a model-free complexity analysis based on the fractal dimension of the rs-BOLD signal, acquired with magnetic resonance imaging. The fractal dimension can be interpreted as measure of signal complexity and connectivity. Previous studies have suggested that reduction in signal complexity can be associated with disease. Therefore, we hypothesized that a detectable difference in rs-BOLD signal complexity could be observed between ASD patients and Controls. METHODS AND FINDINGS: Anatomical and functional data from fifty-five subjects with ASD (12.7 +/- 2.4 y/o) and 55 age-matched (14.1 +/- 3.1 y/o) healthy controls were accessed through the NITRC database and the ABIDE project. Subjects were scanned using a 3T GE Signa MRI and a 32-channel RF-coil. Axial FSPGR-3D images were used to prescribe rs-BOLD (TE/TR = 30/2000ms) where 300 time points were acquired. Motion correction was performed on the functional data and anatomical and functional images were aligned and spatially warped to the N27 standard brain atlas. Fractal analysis, performed on a grey matter mask, was done by estimating the Hurst exponent in the frequency domain using a power spectral density approach and refining the estimation in the time domain with de-trended fluctuation analysis and signal summation conversion methods. Voxel-wise fractal dimension (FD) was calculated for every subject in the control group and in the ASD group to create ROI-based Z-scores for the ASD patients. Voxel-wise validation of FD normality across controls was confirmed, and non-Gaussian voxels were eliminated from subsequent analysis. To maintain a 95% confidence level, only regions where Z-score values were at least 2 standard deviations away from the mean (i.e. where |Z| > 2.0) were included in the analysis. We found that the main regions, where signal complexity significantly decreased among ASD patients, were the amygdala (p = 0.001), the vermis (p = 0.02), the basal ganglia (p = 0.01) and the hippocampus (p = 0.02). No regions reported significant increase in signal complexity in this study. Our findings were correlated with ADIR and ADOS assessment tools, reporting the highest correlation with the ADOS metrics. CONCLUSIONS: Brain connectivity is best modeled as a complex system. Therefore, a measure of complexity as the fractal dimension of fluctuations in brain oxygen demand and utilization could provide important information about connectivity issues in ASD. Moreover, this technique can be used in the characterization of a single subject, with respect to controls, without the need for group analysis. Our novel approach provides an ideal avenue for personalized diagnostics, thus providing unique patient specific assessment that could help in individualizing treatments.
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3. Jawaid S, Kidd GJ, Wang J, Swetlik C, Dutta R, Trapp BD. {{Alterations in CA1 hippocampal synapses in a mouse model of fragile X syndrome}}. {Glia};2017 (Dec 23)
Fragile X Syndrome (FXS) is the major cause of inherited mental retardation and the leading genetic cause of Autism spectrum disorders. FXS is caused by mutations in the Fragile X Mental Retardation 1 (Fmr1) gene, which results in transcriptional silencing of Fragile X Mental Retardation Protein (FMRP). To elucidate cellular mechanisms involved in the pathogenesis of FXS, we compared dendritic spines in the hippocampal CA1 region of adult wild-type (WT) and Fmr1 knockout (Fmr1-KO) mice. Using diolistic labeling, confocal microscopy, and three-dimensional electron microscopy, we show a significant increase in the diameter of secondary dendrites, an increase in dendritic spine density, and a decrease in mature dendritic spines in adult Fmr1-KO mice. While WT and Fmr1-KO mice had the same mean density of spines, the variance in spine density was three times greater in Fmr1-KO mice. Reduced astrocyte participation in the tripartite synapse and less mature post-synaptic densities were also found in Fmr1-KO mice. We investigated whether the increase in synaptic spine density was associated with altered synaptic pruning during development. Our data are consistent with reduced microglia-mediated synaptic pruning in the CA1 region of Fmr1-KO hippocampi when compared with WT littermates at postnatal day 21, which is the peak period of synaptic pruning in the mouse hippocampus. Collectively, these results support abnormal synaptogenesis and synaptic remodeling in mice deficient in FMRP. Deficits in the maturation and distribution of synaptic spines on dendrites of CA1 hippocampal neurons may play a role in the intellectual disabilities associated with FXS.
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4. Kalsner L, Twachtman-Bassett J, Tokarski K, Stanley C, Dumont-Mathieu T, Cotney J, Chamberlain S. {{Genetic testing including targeted gene panel in a diverse clinical population of children with autism spectrum disorder: Findings and implications}}. {Mol Genet Genomic Med};2017 (Dec 21)
BACKGROUND: Genetic testing of children with autism spectrum disorder (ASD) is now standard in the clinical setting, with American College of Medical Genetics and Genomics (ACMGG) guidelines recommending microarray for all children, fragile X testing for boys and additional gene sequencing, including PTEN and MECP2, in appropriate patients. Increasingly, testing utilizing high throughput sequencing, including gene panels and whole exome sequencing, are offered as well. METHODS: We performed genetic testing including microarray, fragile X testing and targeted gene panel, consistently sequencing 161 genes associated with ASD risk, in a clinical population of 100 well characterized children with ASD. Frequency of rare variants identified in individual genes was compared with that reported in the Exome Aggregation Consortium (ExAC) database. RESULTS: We did not diagnose any conditions with complete penetrance for ASD; however, copy number variants believed to contribute to ASD risk were identified in 12%. Eleven children were found to have likely pathogenic variants on gene panel, yet, after careful analysis, none was considered likely causative of disease. KIRREL3 variants were identified in 6.7% of children compared to 2% in ExAC, suggesting a potential role for KIRREL3 variants in ASD risk. Children with KIRREL3 variants more often had minor facial dysmorphism and intellectual disability. We also observed an increase in rare variants in TSC2. However, analysis of variant data from the Simons Simplex Collection indicated that rare variants in TSC2 occur more commonly in specific racial/ethnic groups, which are more prevalent in our population than in the ExAC database. CONCLUSION: The yield of genetic testing including microarray, fragile X (boys) and targeted gene panel was 12%. Gene panel did not increase diagnostic yield; however, we found an increase in rare variants in KIRREL3. Our findings reinforce the need for racial/ethnic diversity in large-scale genomic databases used to identify variants that contribute to disease risk.
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5. Lauber E, Filice F, Schwaller B. {{Parvalbumin neurons as a hub in autism spectrum disorders}}. {J Neurosci Res};2017 (Dec 22)
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6. Levy S, Duda M, Haber N, Wall DP. {{Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism}}. {Mol Autism};2017;8:65.
Background: Autism spectrum disorder (ASD) diagnosis can be delayed due in part to the time required for administration of standard exams, such as the Autism Diagnostic Observation Schedule (ADOS). Shorter and potentially mobilized approaches would help to alleviate bottlenecks in the healthcare system. Previous work using machine learning suggested that a subset of the behaviors measured by ADOS can achieve clinically acceptable levels of accuracy. Here we expand on this initial work to build sparse models that have higher potential to generalize to the clinical population. Methods: We assembled a collection of score sheets for two ADOS modules, one for children with phrased speech (Module 2; 1319 ASD cases, 70 controls) and the other for children with verbal fluency (Module 3; 2870 ASD cases, 273 controls). We used sparsity/parsimony enforcing regularization techniques in a nested cross validation grid search to select features for 17 unique supervised learning models, encoding missing values as additional indicator features. We augmented our feature sets with gender and age to train minimal and interpretable classifiers capable of robust detection of ASD from non-ASD. Results: By applying 17 unique supervised learning methods across 5 classification families tuned for sparse use of features and to be within 1 standard error of the optimal model, we find reduced sets of 10 and 5 features used in a majority of models. We tested the performance of the most interpretable of these sparse models, including Logistic Regression with L2 regularization or Linear SVM with L1 regularization. We obtained an area under the ROC curve of 0.95 for ADOS Module 3 and 0.93 for ADOS Module 2 with less than or equal to 10 features. Conclusions: The resulting models provide improved stability over previous machine learning efforts to minimize the time complexity of autism detection due to regularization and a small parameter space. These robustness techniques yield classifiers that are sparse, interpretable and that have potential to generalize to alternative modes of autism screening, diagnosis and monitoring, possibly including analysis of short home videos.
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7. Li D, Tomljenovic L, Li Y, Shaw CA. {{Removal notice to « Subcutaneous injections of aluminum at vaccine adjuvant levels activate innate immune genes in mouse brain that are homologous with biomarkers of autism » [Journal of Inorganic Biochemistry 177 (2017) 39-54]}}. {J Inorg Biochem};2017 (Dec);177:247.
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8. Rouches A, Lefer G, Dajean-Trutaud S, Lopez-Cazaux S. {{Amelioration de la sante orale des enfants avec autisme : les outils a notre disposition}}. {Arch Pediatr};2017 (Dec 18)
Autism spectrum disorder (ASD) is a life-long heterogeneous psychiatric disorder, characterized by impaired social interaction and communication, and the presence of repetitive and stereotyped behaviors as well as restricted interests. These features have an impact on the oral health of these individuals: high risk of dental caries, poorer periodontal status, and bruxism are often described. Children with ASD often provide limited collaboration with medical procedures, particularly those considered invasive such as dental care. Children with ASD are prone to agitation, self-injury, and emotional dysregulation; they can also present hypersensitivity to sensory input. These features make it difficult for professionals to examine and treat children with ASD; they interfere with dental care and constitute a barrier to it. Most of them are treated under general anesthesia or sedation. Therefore, children with ASD present a challenge for the dental community. Adapted and specific strategies are required to allow individuals with ASD to go beyond the barriers of dental care. Different tools and techniques of evidence-based practice can be considered: visual pedagogy, behavioral approaches, and numeric devices can be used. Pediatricians have a key role in the oral care of children with autism. The aim of this article is to present the oral health associated with ASD, to set out the possible ways to improve oral health, to enable the practitioner to detect problems, to raise awareness, and to help patients and their families in their care pathway.
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9. Schiltz HK, McVey AJ, Magnus B, Dolan BK, Willar KS, Pleiss S, Karst J, Carson AM, Caiozzo C, Vogt E, Van Hecke AV. {{Examining the Links Between Challenging Behaviors in Youth with ASD and Parental Stress, Mental Health, and Involvement: Applying an Adaptation of the Family Stress Model to Families of Youth with ASD}}. {J Autism Dev Disord};2017 (Dec 23)
Raising a child with autism spectrum disorder (ASD) poses unique challenges that may impact parents’ mental health and parenting experiences. The current study analyzed self-report data from 77 parents of youth with ASD. A serial multiple mediation model revealed that parenting stress (SIPA) and parental mental health (BAI and BDI-II) appears to be impacted by challenging adolescent behaviors (SSIS-PBs) and, in turn, affect parental involvement (PRQ), controlling for social skills (SSIS-SSs). Further, the study explored the malleability of parents’ mental health over the course of a social skills intervention, and provides modest evidence that parent depressive symptoms decline across intervention. This study illustrates the importance of considering the entire family system in research on youth with ASD.
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10. Shen L, Zhang K, Feng C, Chen Y, Li S, Iqbal J, Liao L, Zhao Y, Zhai J. {{iTRAQ based proteomic analysis reveals protein profile in plasma from children with autism}}. {Proteomics Clin Appl};2017 (Dec 23)
PURPOSE: Autism is a childhood neurological disorder with poorly understood etiology and pathology. This study is designed to identify differentially expressed proteins which might serve as potential biomarkers for autism. EXPERIMENTAL DESIGN: We performed iTRAQ (isobaric tags for relative and absolute quantitation) analysis for normal and autistic children’s plasma of the same age group. RESULTS: The results showed that 24 differentially expressed proteins were identified between autistic subjects and controls. For the first time, differential expression of complement C5 (C5) and fermitin family homolog 3 (FERMT3) were related to autism. Five proteins i.e., complement C3 (C3), C5, integrin alpha-IIb (ITGA2B), talin-1 (TLN1), and vitamin D-binding protein (GC), were validated via enzyme-linked immunosorbent assay (ELISA). By ROC (receiver operating characteristic) analysis, combinations of these four proteins C3, C5, ITGA2B and TLN1 distinguished autistic children from healthy controls with a high AUC (area under the ROC curve) value (0.982, 95% CI, 0.957-1.000, P < 0.000). CONCLUSION: These above described proteins are found involved in different pathways that have previously been linked to the pathophysiology of autism spectrum disorders (ASDs). The results strongly support that focal adhesions, acting cytoskeleton, cell adhesion, motility and migration, synaptogenesis, and complement system are involved in the pathogenesis of autism, and highlight the important role of platelet function in the pathophysiology of autism. This article is protected by copyright. All rights reserved. Lien vers le texte intégral (Open Access ou abonnement)
11. Tewolde FG, Bishop DVM, Manning C. {{Visual Motion Prediction and Verbal False Memory Performance in Autistic Children}}. {Autism Res};2017 (Dec 21)
Recent theoretical accounts propose that atypical predictive processing can explain the diverse cognitive and behavioral features associated with autism, and that difficulties in making predictions may be related to reduced contextual processing. In this pre-registered study, 30 autistic children aged 6-14 years and 30 typically developing children matched in age and non-verbal IQ completed visual extrapolation and false memory tasks to assess predictive abilities and contextual processing, respectively. In the visual extrapolation tasks, children were asked to predict when an occluded car would reach the end of a road and when an occluded set of lights would fill up a grid. Autistic children made predictions that were just as precise as those made by typically developing children, across a range of occlusion durations. In the false memory task, autistic and typically developing children did not differ significantly in their discrimination between items presented in a list and semantically related, non-presented items, although the data were insensitive, suggesting the need for larger samples. Our findings help to refine theoretical accounts by challenging the notion that autism is caused by pervasively disordered prediction abilities. Further studies will be required to assess the relationship between predictive processing and context use in autism, and to establish the conditions under which predictive processing may be impaired. Autism Res 2017. (c) 2017 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc. LAY SUMMARY: It has been suggested that autistic individuals have difficulties making predictions and perceiving the overall gist of things. Yet, here we found that autistic children made similar predictions about hidden objects as non-autistic children. In a memory task, autistic children were slightly less confused about whether they had heard a word before, when words were closely related in meaning. We conclude that autistic children do not show difficulties with this type of prediction.
