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Query: UMLS:C0004352 (autism)
 32,579 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Children with Smith-Magenis Syndrome (SMS) exhibit deficits in adaptive behavior but systematic studies using objective measures are lacking. This descriptive study assessed adaptive functioning in 19 children with SMS using the Vineland Adaptive Behavior Scales (VABS). Maladaptive behavior was examined through parent questionnaires and the Childhood Autism Rating Scale. Cognitive functioning was evaluated with an age-appropriate test. Children scored below average on VABS Communication, Daily Living Skills, and Socialization scales. Learning problems and hyperactivity scales on the Conner's Parent Rating Scale were elevated, and girls were more impulsive than boys. Stereotypic and self-injurious behaviors were present in all children. Cognitive functioning was delayed and consistent with communication and daily living skills, while socialization scores were higher than IQ.
J Autism Dev Disord 2006 May
PMID:Adaptive and maladaptive behavior in children with Smith-Magenis Syndrome. 1657 Feb 14

Individuals with autism often experience difficulty acquiring a functional intraverbal repertoire, despite demonstrating strong mand, tact, and listener skills. This learning problem may be related to the fact that the primary antecedent variable for most intraverbal behavior involves a type of multiple control identified as a verbal conditional discrimination (VC(D)). The current study is a descriptive analysis that sought to determine if there is a general sequence of intraverbal acquisition by typically developing children and for children with autism, and if this sequence could be used as a framework for intraverbal assessment and intervention. Thirty-nine typically developing children and 71 children with autism were administered an 80-item intraverbal subtest that contained increasingly difficult intraverbal questions and VC(D)s. For the typically developing children the results showed that there was a correlation between age and correct intraverbal responses. However, there was variability in the scores of children who were the same age. An error analysis revealed that compound VC(D)s were the primary cause of errors. Children with autism made the same types of errors as typically developing children who scored at their level on the subtest. These data suggest a potential framework and sequence for intraverbal assessment and intervention.
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PMID:Intraverbal behavior and verbal conditional discriminations in typically developing children and children with autism. 2253 53

An important prerequisite for computational neuroanatomy is the spatial normalization of the data. Despite its importance for the success of the subsequent statistical analysis, image alignment is dealt with from the perspective of image matching, while its influence on the group analysis is neglected. The choice of the template, the registration algorithm as well as the registration parameters, all confound group differences and impact the outcome of the analysis. In order to limit their influence, we perform multiple registrations by varying these parameters, resulting in multiple instances for each sample. In order to harness the high dimensionality of the data and emphasize the group differences, we propose a supervised dimensionality reduction technique that takes into account the organization of the data. This is achieved by solving a supervised dictionary learning problem for block-sparse signals. Structured sparsity allows the grouping of instances across different independent samples, while label supervision allows for discriminative dictionaries. The block structure of dictionaries allows constructing multiple classifiers that treat each dictionary block as a basis of a subspace that spans a separate band of information. We formulate this problem as a convex optimization problem with a geometric programming (GP) component. Promising results that demonstrate the potential of the proposed approach are shown for an MR image dataset of Autism subjects.
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PMID:Supervised block sparse dictionary learning for simultaneous clustering and classification in computational anatomy. 2548 10

Stimulus overselectivity refers to maladaptive narrow attending that is a common learning problem among children with intellectual disabilities and frequently associated with autism. The present study contrasted overselectivity among groups of children with autism, Down syndrome, and typical development. The groups with autism and Down syndrome were matched for intellectual level, and all three groups were matched for developmental levels on tests of nonverbal reasoning and receptive vocabulary. Delayed matching-to-sample tests presented color/form compounds, printed words, photographs of faces, Mayer-Johnson Picture Communication Symbols, and unfamiliar black forms. No significant differences among groups emerged for test accuracy scores. Overselectivity was not statistically overrepresented among individuals with autism in contrast to those with Down syndrome or typically developing children.
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PMID:Stimulus Overselectivity in Autism, Down Syndrome, and Typical Development. 2711 13

It is challenging to derive early diagnosis from neuroimaging data for autism spectrum disorder (ASD). In this work, we propose a novel sparse multi-view task-centralized (Sparse-MVTC) classification method for computer-assisted diagnosis of ASD. In particular, since ASD is known to be age- and sex-related, we partition all subjects into different groups of age/sex, each of which can be treated as a classification task to learn. Meanwhile, we extract multi-view features from functional magnetic resonance imaging to describe the brain connectivity of each subject. This formulates a multi-view multi-task sparse learning problem and it is solved by a novel Sparse-MVTC method. Specifically, we treat each task as a central task and other tasks as the auxiliary ones. We then consider the task-task and view-view relations between the central task and each auxiliary task. We can use this task-centralized strategy for a highly efficient solution. The comprehensive experiments on the ABIDE database demonstrate that our proposed Sparse-MVTC method can significantly outperform the existing classification methods in ASD diagnosis.
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PMID:Sparse Multi-view Task-Centralized Learning for ASD Diagnosis. 2945 53

Genetics has been one of the most powerful windows into the biology of autism spectrum disorder (ASD). It is estimated that a thousand or more genes may confer risk for ASD when functionally perturbed, however, only around 100 genes currently have sufficient evidence to be considered true "autism risk genes". Massive genetic studies are currently underway producing data to implicate additional genes. This approach - although necessary - is costly and slow-moving, making identification of putative ASD risk genes with existing data vital. Here, we approach autism risk gene discovery as a machine learning problem, rather than a genetic association problem, by using genome-scale data as predictors to identify new genes with similar properties to established autism risk genes. This ensemble method, forecASD, integrates brain gene expression, heterogeneous network data, and previous gene-level predictors of autism association into an ensemble classifier that yields a single score indexing evidence of each gene's involvement in the etiology of autism. We demonstrate that forecASD has substantially better performance than previous predictors of autism association in three independent trio-based sequencing studies. Studying forecASD prioritized genes, we show that forecASD is a robust indicator of a gene's involvement in ASD etiology, with diverse applications to gene discovery, differential expression analysis, eQTL prioritization, and pathway enrichment analysis.
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PMID:Forecasting risk gene discovery in autism with machine learning and genome-scale data. 3324 69