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      Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism

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          Abstract

          Children with autism spectrum disorders (ASDs) often display atypical learning styles; however, little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. While learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.

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          Most cited references118

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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            Statistical Power Analysis for the Behavioral Sciences

            <i>Statistical Power Analysis</i> is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: <br> * a chapter covering power analysis in set correlation and multivariate methods;<br> * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and;<br> * expanded power and sample size tables for multiple regression/correlation.<br>
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              Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders

              Describes the Autism Diagnostic Interview-Revised (ADI-R), a revision of the Autism Diagnostic Interview, a semistructured, investigator-based interview for caregivers of children and adults for whom autism or pervasive developmental disorders is a possible diagnosis. The revised interview has been reorganized, shortened, modified to be appropriate for children with mental ages from about 18 months into adulthood and linked to ICD-10 and DSM-IV criteria. Psychometric data are presented for a sample of preschool children.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                03 August 2023
                2023
                : 12
                : e86035
                Affiliations
                [1 ] Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine ( https://ror.org/00f54p054) Stanford United States
                [2 ] Department of Psychology, Santa Clara University ( https://ror.org/03ypqe447) Santa Clara United States
                [3 ] Department of Psychology, Rutgers University ( https://ror.org/05vt9qd57) Newark United States
                [4 ] Department of Neurology & Neurological Sciences, Stanford Neurosciences Institute ( https://ror.org/00f54p054) Stanford United States
                [5 ] Stanford Neurosciences Institute, Stanford University School of Medicine ( https://ror.org/00f54p054) Stanford United States
                University of Oxford ( https://ror.org/052gg0110) United Kingdom
                Donders Institute for Brain, Cognition and Behaviour Netherlands
                University of Oxford ( https://ror.org/052gg0110) United Kingdom
                University of Oxford ( https://ror.org/052gg0110) United Kingdom
                University of Oxford ( https://ror.org/052gg0110) United Kingdom
                University of New Mexico ( https://ror.org/05fs6jp91) United States
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-4343-2623
                https://orcid.org/0000-0002-2231-1112
                https://orcid.org/0000-0002-1255-1200
                https://orcid.org/0000-0002-2118-5601
                https://orcid.org/0000-0003-0768-0347
                https://orcid.org/0000-0003-1622-9857
                Article
                86035
                10.7554/eLife.86035
                10550286
                37534879
                387cd04c-0e41-4115-b03b-2d6dc27b4f32
                © 2023, Liu, Chang et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 08 January 2023
                : 02 August 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: HD059205
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: MH084164
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: HD094623
                Award Recipient :
                Funded by: Stanford Maternal and Child Health Research Institute;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: MH101394
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                Learning in children with autism spectrum disorder (ASD) was achieved by fundamentally different cognitive and neural mechanisms from typically developing children, and insistence on sameness, a core symptom of ASD, contributed to such atypical mechanisms of learning in affected children.

                Life sciences
                autism spectrum disorder,learning,intervention,math problem solving,neural representations,restricted,repetitive interests,behaviors,human

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