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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)
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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
in Journal of Child Psychology and Psychiatry > 57-3 (March 2016) . - p.421-439[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