Pubmed du 16/06/20

mardi 16 juin 2020

1. Almehmadi W, Tenbrink T, Sanoudaki E. Pragmatic and Conversational Features of Arabic-Speaking Adolescents With Autism Spectrum Disorder : Examining Performance and Caregivers’ Perceptions. J Speech Lang Hear Res ;2020 (Jun 16):1-14.

Purpose This study investigates the features of pragmatic and conversational skills in the language of Arabic-speaking adolescents with autism spectrum disorder (ASD) by comparing them with typically developing (TD) Arabic-speaking adolescents in Saudi Arabia. It aims to identify the differences in the pragmatic skills of the two groups and the perception of those skills by caregivers, with respect to four main pragmatic areas : discourse management, communicative function, conversational repair, and presupposition abilities. Method Data for this study were collected from 15 Saudi adolescents with ASD and a control group of 15 TD adolescents, matched for gender and language abilities. All the participants were in the normal IQ range. The caregivers of the adolescents with ASD and TD adolescents also participated in this study. Data were collected on the adolescents’ performances using the Yale in vivo Pragmatic Protocol. In addition, the Pragmatics Profile of Everyday Communication Skills (PPECS) was used to collect data on the caregivers’ perceptions of the adolescents’ abilities. The combination of tools in this study allows for a unique comparison between actual performance and caregivers’ perceptions. Results As expected, both the adolescents’ performances and the caregivers’ perceptions reflected an overall deficit in the pragmatic and conversational skills of adolescents with ASD. However, we also identified an inconsistency between the caregivers’ estimation of the participant’s pragmatic abilities and the actual abilities demonstrated by the adolescents. In particular, TD adolescents performed significantly better than adolescents with ASD in the pragmatic areas of turn-taking, topic maintenance, and topic initiation, but the caregivers did not detect differences between the two groups in these discourse management abilities. Conclusions This study has important implications for both ASD interventions and assessment. It provides a comprehensive assessment approach for measuring pragmatic skills, including both direct (participants’ performances) and indirect (caregivers’ perceptions) measures. Future research may benefit from adopting the combined approach used in this study to explore pragmatics in ASD. Differences between caregivers’ perceptions and the performances of individuals with ASD should be considered, as well as the influence of various factors on their communication.

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2. Boedhoe PSW, van Rooij D, Hoogman M, Twisk JWR, Schmaal L, Abe Y, Alonso P, Ameis SH, Anikin A, Anticevic A, Arango C, Arnold PD, Asherson P, Assogna F, Auzias G, Banaschewski T, Baranov A, Batistuzzo MC, Baumeister S, Baur-Streubel R, Behrmann M, Bellgrove MA, Benedetti F, Beucke JC, Biederman J, Bollettini I, Bose A, Bralten J, Bramati IE, Brandeis D, Brem S, Brennan BP, Busatto GF, Calderoni S, Calvo A, Calvo R, Castellanos FX, Cercignani M, Chaim-Avancini TM, Chantiluke KC, Cheng Y, Cho KIK, Christakou A, Coghill D, Conzelmann A, Cubillo AI, Dale AM, Dallaspezia S, Daly E, Denys D, Deruelle C, Di Martino A, Dinstein I, Doyle AE, Durston S, Earl EA, Ecker C, Ehrlich S, Ely BA, Epstein JN, Ethofer T, Fair DA, Fallgatter AJ, Faraone SV, Fedor J, Feng X, Feusner JD, Fitzgerald J, Fitzgerald KD, Fouche JP, Freitag CM, Fridgeirsson EA, Frodl T, Gabel MC, Gallagher L, Gogberashvili T, Gori I, Gruner P, Gürsel DA, Haar S, Haavik J, Hall GB, Harrison NA, Hartman CA, Heslenfeld DJ, Hirano Y, Hoekstra PJ, Hoexter MQ, Hohmann S, Høvik MF, Hu H, Huyser C, Jahanshad N, Jalbrzikowski M, James A, Janssen J, Jaspers-Fayer F, Jernigan TL, Kapilushniy D, Kardatzki B, Karkashadze G, Kathmann N, Kaufmann C, Kelly C, Khadka S, King JA, Koch K, Kohls G, Kohls K, Kuno M, Kuntsi J, Kvale G, Kwon JS, Lázaro L, Lera-Miguel S, Lesch KP, Hoekstra L, Liu Y, Lochner C, Louza MR, Luna B, Lundervold AJ, Malpas CB, Marques P, Marsh R, Martínez-Zalacaín I, Mataix-Cols D, Mattos P, McCarthy H, McGrath J, Mehta MA, Menchón JM, Mennes M, Martinho MM, Moreira PS, Morer A, Morgado P, Muratori F, Murphy CM, Murphy DGM, Nakagawa A, Nakamae T, Nakao T, Namazova-Baranova L, Narayanaswamy JC, Nicolau R, Nigg JT, Novotny SE, Nurmi EL, Weiss EO, O’Gorman Tuura RL, O’Hearn K, O’Neill J, Oosterlaan J, Oranje B, Paloyelis Y, Parellada M, Pauli P, Perriello C, Piacentini J, Piras F, Plessen KJ, Puig O, Ramos-Quiroga JA, Reddy YCJ, Reif A, Reneman L, Retico A, Rosa PGP, Rubia K, Rus OG, Sakai Y, Schrantee A, Schwarz L, Schweren LJS, Seitz J, Shaw P, Shook D, Silk TJ, Simpson HB, Skokauskas N, Soliva Vila JC, Solovieva A, Soreni N, Soriano-Mas C, Spalletta G, Stern ER, Stevens MC, Stewart SE, Sudre G, Szeszko PR, Tamm L, Taylor MJ, Tolin DF, Tosetti M, Tovar-Moll F, Tsuchiyagaito A, van Erp TGM, van Wingen GA, Vance A, Venkatasubramanian G, Vilarroya O, Vives-Gilabert Y, von Polier GG, Walitza S, Wallace GL, Wang Z, Wolfers T, Yoncheva YN, Yun JY, Zanetti MV, Zhou F, Ziegler GC, Zierhut KC, Zwiers MP, Thompson PM, Stein DJ, Buitelaar J, Franke B, van den Heuvel OA. Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders : Findings From the ENIGMA ADHD, ASD, and OCD Working Groups. Am J Psychiatry ;2020 (Jun 16):appiajp202019030331.

OBJECTIVE : Attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. The authors sought to directly compare these disorders using structural brain imaging data from ENIGMA consortium data. METHODS : Structural T(1)-weighted whole-brain MRI data from healthy control subjects (N=5,827) and from patients with ADHD (N=2,271), ASD (N=1,777), and OCD (N=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. The authors examined subcortical volume, cortical thickness, and cortical surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults, using linear mixed-effects models adjusting for age, sex, and site (and intracranial volume for subcortical and surface area measures). RESULTS : No shared differences were found among all three disorders, and shared differences between any two disorders did not survive correction for multiple comparisons. Children with ADHD compared with those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller intracranial volume than control subjects and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared with adult control subjects and other clinical groups. No OCD-specific differences were observed across different age groups and surface area differences among all disorders in childhood and adulthood. CONCLUSIONS : The study findings suggest robust but subtle differences across different age groups among ADHD, ASD, and OCD. ADHD-specific intracranial volume and hippocampal differences in children and adolescents, and ASD-specific cortical thickness differences in the frontal cortex in adults, support previous work emphasizing structural brain differences in these disorders.

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3. Cola ML, Plate S, Yankowitz L, Petrulla V, Bateman L, Zampella CJ, de Marchena A, Pandey J, Schultz RT, Parish-Morris J. Sex differences in the first impressions made by girls and boys with autism. Mol Autism ;2020 (Jun 16) ;11(1):49.

BACKGROUND : Individuals with autism spectrum disorder (ASD) are characterized by social communication challenges and repetitive behaviors that may be quickly detected by experts (Autism Res 10:653-62, 2017 ; American Psychiatric Association, Diagnostic and statistical manual of mental disorders, 2013). Recent research suggests that even naïve non-experts judge a variety of human dimensions using narrow windows of experience called "first impressions." Growing recognition of sex differences in a variety of observable behaviors in ASD, combined with research showing that some autistic girls and women may "camouflage" outward symptoms, suggests it may be more difficult for naïve conversation partners to detect ASD symptoms in girls. Here, we explore the first impressions made by boys and girls with ASD and typically developing (TD) peers. METHODS : Ninety-three school-aged children with ASD or TD were matched on IQ ; autistic girls and boys were additionally matched on autism symptom severity using the ADOS-2. Participants completed a 5-minute "get-to-know-you" conversation with a new young adult acquaintance. Immediately after the conversation, confederates rated participants on a variety of dimensions. Our primary analysis compared conversation ratings between groups (ASD boys, ASD girls, TD boys, TD girls). RESULTS : Autistic girls were rated more positively than autistic boys by novel conversation partners (better perceived social communication ability), despite comparable autism symptom severity as rated by expert clinicians (equivalent true social communication ability). Boys with ASD were rated more negatively than typical boys and typical girls by novel conversation partners as well as expert clinicians. There was no significant difference in the first impressions made by autistic girls compared to typical girls during conversations with a novel conversation partner, but autistic girls were rated lower than typical girls by expert clinicians. LIMITATIONS : This study cannot speak to the ways in which first impressions may differ for younger children, adults, or individuals who are not verbally fluent ; in addition, there were more autistic boys than girls in our sample, making it difficult to detect small effects. CONCLUSIONS : First impressions made during naturalistic conversations with non-expert conversation partners could-in combination with clinical ratings and parent report-shed light on the nature and effects of behavioral differences between girls and boys on the autism spectrum.

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4. Zhan Y, Wei J, Liang J, Xu X, He R, Robbins TW, Wang Z. Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model. Am J Psychiatry ;2020 (Jun 16):appiajp202019101091.

OBJECTIVE : Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used noninvasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity. METHODS : Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD : Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112 ; ABIDE-II, N=1,114 ; ADHD-200 sample : N=776 ; OCD local institutional database : N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders. RESULTS : Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively. CONCLUSIONS : The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.

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