Pubmed du 25/08/19

dimanche 25 août 2019

1. Erratum : Soda et al., "Hyperexcitability and Hyperplasticity Disrupt Cerebellar Signal Transfer in the IB2 KO Mouse Model of Autism". J Neurosci. 2019.

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2. Kahathuduwa CN, West B, Mastergeorge A. Effects of Overweight or Obesity on Brain Resting State Functional Connectivity of Children with Autism Spectrum Disorder. J Autism Dev Disord. 2019.

Evidence on neurophysiological correlates of coexisting autism spectrum disorders (ASD) and overweight/obesity may elucidate mechanisms leading to the observed greater risk of obesity in children with ASD. An exploratory secondary data analysis was performed on resting state functional magnetic resonance imaging (rs-fMRI) data of children downloaded from the ABIDE Preprocessed database (n = 81). Children with isolated ASD showed hypo-connectivity between anterior and posterior default mode network (DMN) (p = 0.003 ; FWER). Children with coexisting ASD and overweight/obesity showed hyper-connectivity between anterior and posterior DMN (p = 0.015 ; FWER). More evidence is needed to confirm these contrasting rs-fMRI connectivity profiles and to explicate causal inferences regarding neurophysiological mechanisms associated with coexisting ASD and overweight/obesity.

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3. Stevens E, Dixon DR, Novack MN, Granpeesheh D, Smith T, Linstead E. Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning. International journal of medical informatics. 2019 ; 129 : 29-36.

BACKGROUND AND OBJECTIVE : Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups ; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes. MATERIALS AND METHODS : The present study included a sample of children with ASD (N = 2400), the largest of its kind to date. Unsupervised machine learning was applied to model ASD subgroups as well as their taxonomic relationships. Retrospective treatment data were available for a portion of the sample (n = 1034). Treatment response was examined within each subgroup via regression. RESULTS : The application of a Gaussian Mixture Model revealed 16 subgroups. Further examination of the subgroups through Hierarchical Agglomerative Clustering suggested 2 overlying behavioral phenotypes with unique deficit profiles each composed of subgroups that differed in severity of those deficits. Furthermore, differentiated response to treatment was found across subtypes, with a substantially higher amount of variance accounted for due to the homogenization effect of the clustering. DISCUSSION : The high amount of variance explained by the regression models indicates that clustering provides a basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized intervention based on unsupervised machine learning.

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4. Yamamoto K, Masumoto K. Memory for Rules and Output Monitoring in Adults with Autism Spectrum Disorder. J Autism Dev Disord. 2019.

This study examined factors related to repetitive errors in people with autism spectrum disorder (ASD) from the perspective of output monitoring and memory for rules. Previous studies have suggested that output monitoring errors are associated with repetition errors. Moreover, people with ASD have a reduced memory for rules, which could result in repetitive errors. Typically developing (TD) and ASD participants memorized rules and conducted an object arrangement task consisting of sorting objects according to their price under two conditions. Memory tests and output monitoring tests were conducted immediately, and 1 week later. Results indicated that output monitoring in ASD was significantly lower than in TD, although the memory for rules showed no differences between ASD and TD.

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