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      Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples

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          Abstract

          Objective

          To estimate whether the “Diagnostic and Statistical Manual of Mental Disorders” (DSM) is biologically accurate for the diagnosis of Attention Deficit/ Hyperactivity Disorder (ADHD) using a biological-based classifier built by a special method of multivariate analysis of a large dataset of a small sample (much more variables than subjects), holding neurophysiological, behavioral, and psychological variables.

          Methods

          Twenty typically developing boys and 19 boys diagnosed with ADHD, aged 10–13 years, were examined using the Attentional Network Test (ANT) with recordings of event-related potentials (ERPs). From 774 variables, a reduced number of latent variables (LVs) were extracted with a clustering of variables method (CLV), for further reclassification of subjects using the k-means method. This approach allowed a multivariate analysis to be applied to a significantly larger number of variables than the number of cases.

          Results

          From datasets including ERPs from the mid-frontal, mid-parietal, right frontal, and central scalp areas, we found 82% of agreement between DSM and biological-based classifications. The kappa index between DSM and behavioral/psychological/neurophysiological data was 0.75, which is regarded as a “substantial level of agreement”.

          Discussion

          The CLV is a useful method for multivariate analysis of datasets with much less subjects than variables. In this study, a correlation is found between the biological-based classifier and the DSM outputs for the classification of subjects as either ADHD or not. This result suggests that DSM clinically describes a biological condition, supporting its validity for ADHD diagnostics.

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          Most cited references28

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          A review of electrophysiology in attention-deficit/hyperactivity disorder: II. Event-related potentials.

          This article reviews the event-related potential (ERP) literature in relation to attention-deficit/hyperactivity disorder (AD/HD). ERP studies exploring various aspects of brain functioning in AD/HD are reviewed, ranging from early preparatory processes to a focus on the auditory and visual attention systems, and the frontal inhibition system. Implications of these data for future research and development in AD/HD are considered. A complex range of ERP deficits has been associated with the disorder. Differences have been reported in preparatory responses, such as the contingent negative variation. In the auditory modality, AD/HD-related differences are apparent in all components from the auditory brain-stem response to the late slow wave. The most robust of these is the reduced posterior P3 in the auditory oddball task. There are fewer studies of the visual attention system, but similar differences are reported in a range of components. Results suggesting an inhibitory processing deficit have been reported, with recent studies of the frontal inhibitory system indicating problems of inhibitory regulation. The research to date has identified a substantial number of ERP correlates of AD/HD. Together with the robust AD/HD differences apparent in the EEG literature, these data offer potential to improve our understanding of the specific brain dysfunction(s) which result in the disorder. Increased focus on the temporal locus of the information processing deficit(s) underlying the observed range of ERP differences is recommended. Further work in this field may benefit from a broader conceptual approach, integrating EEG and ERP measures of brain function.
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            Validation of a Blood-Based Laboratory Test to Aid in the Confirmation of a Diagnosis of Schizophrenia

            We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51 analytes which could distinguish schizophrenia (n = 250) from control (n = 230) subjects. In the next stage, these analytes were developed as a refined 51-plex immunoassay panel for validation using a large independent cohort of schizophrenia (n = 577) and control (n = 229) subjects. The resulting test yielded an overall sensitivity of 83% and specificity of 83% with a receiver operating characteristic area under the curve (ROC-AUC) of 89%. These 51 immunoassays and the associated decision rule delivered a sensitive and specific prediction for the presence of schizophrenia in patients compared to matched healthy controls.
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              What kind of science for psychiatry?

              Psychiatry has invested its hopes in neuroscience as a path to understanding mental disorders and developing more effective treatments and ultimately cures. Recently, the U.S. NIMH has elaborated this vision through a new framework for mental health research, the Research Domain Criteria (RDoC). This framework aims to orient mental health research toward the discovery of underlying neurobiological and biobehavioral mechanisms of mental disorders that will eventually lead to definitive treatments. In this article we consider the rationale of the RDoC and what it reveals about implicit models of mental disorders. As an overall framework for understanding mental disorders, RDoC is impoverished and conceptually flawed. These limitations are not accidental but stem from disciplinary commitments and interests that are at odds with the larger concerns of psychiatry. A multilevel, ecosocial approach to biobehavioral systems is needed both to guide relevant neuroscience research and insure the inclusion of social processes that may be fundamental contributors to psychopathology and recovery.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                12 June 2019
                2019
                : 7
                : e7074
                Affiliations
                [1 ]Laboratory of Neurobiology and Clinical Neurophysiology, National Institute of Women, Children and Adolescents’ Health Fernandes Figueira, Oswaldo Cruz Foundation , Rio de Janeiro, Brazil
                [2 ]Clinical Research Unit, National Institute of Women, Children, and Adolescents’ Health Fernandes Figueira, Oswaldo Cruz Foundation , Rio De Janeiro, Brazil
                [3 ]Laboratoy of Psychophysiology, Institute of Biological Sciences, Federal University of Juiz de Fora , Juiz de Fora, Brazil
                [4 ]StatSC, Oniris, INRA , Nantes, France
                Article
                7074
                10.7717/peerj.7074
                6571005
                31223531
                b4d7ee62-5c59-4f7a-8014-f1b3e751c5a3
                ©2019 Abramov et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 15 November 2018
                : 2 May 2019
                Funding
                Funded by: National Institute Fernandes Figueira
                Award ID: IFF-008-Fio-13-3-2
                This work was supported by the Programa de Incentivo a Pesquisa (Research Incentive Program - PIP) of the National Institute Fernandes Figueira (project IFF-008-Fio-13-3-2). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Neuroscience
                Cognitive Disorders
                Psychiatry and Psychology

                adhd,diagnostic,diagnostic and statistical manual of mental disorders,multivariate analysis,clustering of variables around latent components,event-related potentials,attentional networks test

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