1. Andersen PN, Skogli EW, Hovik KT, Geurts H, Egeland J, Oie M. {{Working memory arrest in children with high-functioning autism compared to children with attention-deficit/hyperactivity disorder: Results from a 2-year longitudinal study}}. {Autism};2014 (Mar 6)
The aim of this study was to analyse the development of verbal working memory in children with high-functioning autism compared to children with attention-deficit/hyperactivity disorder and typically developing children. A total of 34 children with high-functioning autism, 72 children with attention-deficit/hyperactivity disorder and 45 typically developing children (age 9-16 years) were included at baseline and followed up approximately 25 months later. The children were given a letter/number sequencing task to assess verbal working memory. The performance of children with high-functioning autism on verbal working memory did not improve after 2 years, while improvement was observed in children with attention-deficit/hyperactivity disorder and typically developing children. The results indicate a different developmental trajectory for verbal working memory in children with high-functioning autism compared to children with attention-deficit/hyperactivity disorder and typically developing children. More research is needed to construct a developmental framework more suitable for children with autism spectrum disorder.
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2. Benson PR. {{Coping and Psychological Adjustment Among Mothers of Children with ASD: An Accelerated Longitudinal Study}}. {J Autism Dev Disord};2014 (Mar 6)
Utilizing a cohort sequential design and multilevel modeling on a sample of 113 mothers, the effects of four coping strategies (engagement, disengagement, distraction, and cognitive reframing) on multiple measures of maternal adjustment were assessed over a 7 years period when children with autism spectrum disorders in the study were approximately 7-14 years old. Findings indicated increased use of disengagement and distraction to be related to increased maternal maladjustment over time, while increased use of cognitive reframing was linked to improved maternal outcomes (findings regarding engagement’s effects on adjustment measures were mixed). In addition, results indicated that use of different coping strategies at times moderated the effects of child behavior on maternal adjustment. Study findings are discussed in light of prior research and study limitations and clinical implications are highlighted.
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3. Hao G, Layton TL, Zou XB, Li DY. {{Evaluating autism in a Chinese population: the Clinical Autism Diagnostic Scale}}. {World J Pediatr};2014 (Mar 6)
BACKGROUND: The purpose of this study was to report on the psychometric measures and discriminatory function of a new diagnostic test for autism spectrum disorders, the Clinical Autism Diagnostic Scale (CADS). METHODS: The CADS was used to test 216 children in the study, including 86 with low-functioning autism specturm disorders (ASD), 16 children with highfunctioning ASD, 16 with pervasive developmental disorder, not otherwise specified, 7 with Asperger syndrome, 65 with typical development, 11 children with language impairments and 15 with intellectual disabilities. Ages ranged from 38-73 months. Behaviors for the groups were compared across seven domains. RESULTS: The results indicated the instrument was reliable, valid, and successfully differentiated the different groups of children with and without autism. All ASD groups were found to display difficulties in the domains of sensory behaviors and stereotyped behaviors. The play and social domains were found to measure similar underlying concepts of behaviors, while the receptive language and expressive language domains were also found to measure similar underlying-language concepts. The group of children diagnosed as having low-functioning autism performed less well on all tested domains in the instrument than did the other three groups of children with ASD, and these other three groups each also presented unique patterns of behaviors and differed on individual domains. CONCLUSIONS: CADS is a reliable and valid test. It successfully differentiates the abilities of children with ASD at different levels of functioning.
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4. Sparaci L, Stefanini S, D’Elia L, Vicari S, Rizzolatti G. {{What and Why Understanding in Autism Spectrum Disorders and Williams Syndrome: Similarities and Differences}}. {Autism Res};2014 (Mar 6)
Children with autism spectrum disorders (ASD) and children with Williams syndrome (WS) show divergent social phenotypes, but also several similarities in their socio-cognitive deficits. Cross-syndrome direct comparisons could lead to a better understanding of mechanisms that determine deficits in social cognition in the two syndromes. A fundamental factor for social cognition is the ability to understand and predict others’ actions (e.g. what action is being done and why it is being done when observing a goal-related act). Here we compared the understanding of others’ actions in children with ASD, WS and in children with typical development. Comprehension of what motor act was being done and of why it was being done was assessed with or without contextual cueing using a computer-based task. The results showed that what understanding was impaired in the WS group, but not in the ASD group, which showed mental-age appropriate performance. Why understanding was impaired in both experimental groups. Autism Res 2014, : -. (c) 2014 International Society for Autism Research, Wiley Periodicals, Inc.
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5. Liu L, Lei J, Sanders SJ, Willsey AJ, Kou Y, Cicek AE, Klei L, Lu C, He X, Li M, Muhle RA, Ma’ayan A, Noonan JP, Sestan N, McFadden KA, State MW, Buxbaum JD, Devlin B, Roeder K. {{DAWN: A framework to identify autism genes and subnetworks using gene expression and genetics}}. {Mol Autism};2014 (Mar 6);5(1):22.
BACKGROUND: emph{De novo} loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes. METHODS: To accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motor-somatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk. RESULTS: Using currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model. CONCLUSIONS: Validation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.
