Opinion Review
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Psychiatr. Jan 19, 2021; 11(1): 1-12
Published online Jan 19, 2021. doi: 10.5498/wjp.v11.i1.1
How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry
Drozdstoy Stoyanov, Michael HJ Maes
Drozdstoy Stoyanov, Michael HJ Maes, Department of Psychiatry and Medical Psychology, Research Institute, Medical University of Plovdiv, Plovdiv 4000, Bulgaria
Michael HJ Maes, Department of Psychiatry, Deakin University, Geelong 3220, Australia
Author contributions: The authors declare equal contribution to this manuscript.
Conflict-of-interest statement: None to declare.
Open-Access: 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/
Corresponding author: Drozdstoy Stoyanov, DSc, MD, PhD, Full Professor, Department of Psychiatry and Medical Psychology, Research Institute, Medical University of Plovdiv, Vassil Aprilov 15a, Plovdiv 4000, Bulgaria. drozdstoy.stoyanov@mu-plovdiv.bg
Received: November 24, 2020
Peer-review started: November 24, 2020
First decision: December 12, 2020
Revised: December 15, 2020
Accepted: December 26, 2020
Article in press: December 26, 2020
Published online: January 19, 2021

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.

Keywords: Psychiatry, Major depression, Mood disorders, Schizophrenia, Antioxidants, Oxydative stress

Core Tip: The nomothetic network psychiatry approach is a new method which aims to construct causal models of schizophrenia and mood disorders by integrating all features of those mental illnesses into a data-driven model. These features comprise data on risk-resilience, adverse outcome pathways, the cognitome, brainome, symptomatome, staging, and the phenomenome. Partial Least Squares analysis may be employed to train, test, and validate those models and to build pathway-phenotypes or biosignatures. Clustering analysis performed on all illness features, reduced into latent traits scores, may expose relevant new transdiagnostic classes.