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
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, 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
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
Core Tip
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.