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How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry

Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and pheno...

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Autores principales: Stoyanov, Drozdstoy, Maes, Michael HJ
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805251/
https://www.ncbi.nlm.nih.gov/pubmed/33511042
http://dx.doi.org/10.5498/wjp.v11.i1.1
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author Stoyanov, Drozdstoy
Maes, Michael HJ
author_facet Stoyanov, Drozdstoy
Maes, Michael HJ
author_sort Stoyanov, Drozdstoy
collection PubMed
description Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current “gold standard” Diagnostic and Statistical Manual of Mental Disorders/International Classification of Diseases taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by assembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, staging, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories.
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spelling pubmed-78052512021-01-27 How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry Stoyanov, Drozdstoy Maes, Michael HJ World J Psychiatry Opinion Review Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current “gold standard” Diagnostic and Statistical Manual of Mental Disorders/International Classification of Diseases taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by assembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, staging, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories. Baishideng Publishing Group Inc 2021-01-19 /pmc/articles/PMC7805251/ /pubmed/33511042 http://dx.doi.org/10.5498/wjp.v11.i1.1 Text en ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
spellingShingle Opinion Review
Stoyanov, Drozdstoy
Maes, Michael HJ
How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry
title How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry
title_full How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry
title_fullStr How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry
title_full_unstemmed How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry
title_short How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry
title_sort how to construct neuroscience-informed psychiatric classification? towards nomothetic networks psychiatry
topic Opinion Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805251/
https://www.ncbi.nlm.nih.gov/pubmed/33511042
http://dx.doi.org/10.5498/wjp.v11.i1.1
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