Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders - promises and limitations / Yihong ZHAO in Journal of Child Psychology and Psychiatry, 57-3 (March 2016)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 57-3 (March 2016) . - p.421-439 Titre : Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders - promises and limitations Type de document : Texte imprimé et/ou numérique Auteurs : Yihong ZHAO, Auteur ; Francisco Xavier CASTELLANOS, Auteur Article en page(s) : p.421-439 Langues : Anglais (eng) Mots-clés : Neuropsychiatric disorders  psychopathology  genetics  brain image  endophenotype  Big Data  classification  inference Index. décimale : PER Périodiques Résumé : Background Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles. Findings A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain–behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources (‘broad’ data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors (‘deep’ data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders. Conclusions We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis. En ligne : http://dx.doi.org/10.1111/jcpp.12503 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=282 [article] Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders - promises and limitations [Texte imprimé et/ou numérique] / Yihong ZHAO, Auteur ; Francisco Xavier CASTELLANOS, Auteur . - p.421-439. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 57-3 (March 2016) . - p.421-439 Mots-clés : Neuropsychiatric disorders  psychopathology  genetics  brain image  endophenotype  Big Data  classification  inference Index. décimale : PER Périodiques Résumé : Background Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles. Findings A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain–behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources (‘broad’ data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors (‘deep’ data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders. Conclusions We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis. En ligne : http://dx.doi.org/10.1111/jcpp.12503 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=282

Perceived social support in adults with autism spectrum disorder and attention-deficit/hyperactivity disorder / Sonia ALVAREZ-FERNANDEZ in Autism Research, 10-5 (May 2017)  

Public ISBD [article]  inAutism Research > 10-5 (May 2017) . - p.866-877 Titre : Perceived social support in adults with autism spectrum disorder and attention-deficit/hyperactivity disorder Type de document : Texte imprimé et/ou numérique Auteurs : Sonia ALVAREZ-FERNANDEZ, Auteur ; Hallie R. BROWN, Auteur ; Yihong ZHAO, Auteur ; Jessica A. RAITHEL, Auteur ; Somer L. BISHOP, Auteur ; Sarah B. KERN, Auteur ; Catherine LORD, Auteur ; Eva PETKOVA, Auteur ; Adriana DI MARTINO, Auteur Article en page(s) : p.866-877 Langues : Anglais (eng) Mots-clés : autism spectrum disorder  attention-deficit/hyperactivity disorder  perceived social support  social cognition  adults Index. décimale : PER Périodiques Résumé : Perceived social support (PSS) has been related to physical and mental well-being in typically developing individuals, but systematic characterizations of PSS in autism spectrum disorder (ASD) are limited. We compared self-report ratings of the multidimensional scale of PSS (MSPSS) among age- and IQ-matched groups of adults (18–58 years) with cognitively high-functioning ASD (N?=?41), or attention-deficit/hyperactivity disorder (ADHD; N?=?69), and neurotypical controls (NC; N?=?69). Accompanying group comparisons, we used machine learning random forest (RF) analyses to explore predictors among a range of psychopathological and socio-emotional variables. Relative to both ADHD and NC, adults with ASD showed lower MSPSS ratings, specifically for the friends subscale (MSPSS-f). Across ASD and ADHD, interindividual differences in autism severity, affective empathy, symptoms of anxiety related to social interactions, hyperactivity/impulsivity, and somatization best predicted MSPSS-f. These relationships did not differ between clinical groups. While group comparisons demonstrated greater impairment in individuals with ASD, analyzing individuals' characteristics revealed cross-diagnoses similarities in regard to their MSPSS-f relationships. This is consistent with the Research Domain Criteria framework, supporting a trans-diagnostic approach as on the path toward “precision medicine.” En ligne : http://dx.doi.org/10.1002/aur.1735 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=307 [article] Perceived social support in adults with autism spectrum disorder and attention-deficit/hyperactivity disorder [Texte imprimé et/ou numérique] / Sonia ALVAREZ-FERNANDEZ, Auteur ; Hallie R. BROWN, Auteur ; Yihong ZHAO, Auteur ; Jessica A. RAITHEL, Auteur ; Somer L. BISHOP, Auteur ; Sarah B. KERN, Auteur ; Catherine LORD, Auteur ; Eva PETKOVA, Auteur ; Adriana DI MARTINO, Auteur . - p.866-877. Langues : Anglais (eng) in Autism Research > 10-5 (May 2017) . - p.866-877 Mots-clés : autism spectrum disorder  attention-deficit/hyperactivity disorder  perceived social support  social cognition  adults Index. décimale : PER Périodiques Résumé : Perceived social support (PSS) has been related to physical and mental well-being in typically developing individuals, but systematic characterizations of PSS in autism spectrum disorder (ASD) are limited. We compared self-report ratings of the multidimensional scale of PSS (MSPSS) among age- and IQ-matched groups of adults (18–58 years) with cognitively high-functioning ASD (N?=?41), or attention-deficit/hyperactivity disorder (ADHD; N?=?69), and neurotypical controls (NC; N?=?69). Accompanying group comparisons, we used machine learning random forest (RF) analyses to explore predictors among a range of psychopathological and socio-emotional variables. Relative to both ADHD and NC, adults with ASD showed lower MSPSS ratings, specifically for the friends subscale (MSPSS-f). Across ASD and ADHD, interindividual differences in autism severity, affective empathy, symptoms of anxiety related to social interactions, hyperactivity/impulsivity, and somatization best predicted MSPSS-f. These relationships did not differ between clinical groups. While group comparisons demonstrated greater impairment in individuals with ASD, analyzing individuals' characteristics revealed cross-diagnoses similarities in regard to their MSPSS-f relationships. This is consistent with the Research Domain Criteria framework, supporting a trans-diagnostic approach as on the path toward “precision medicine.” En ligne : http://dx.doi.org/10.1002/aur.1735 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=307

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