How to construct neuroscience-informed psychiatric classification?Towards nomothetic networks psychiatry
作者机构：Department of Psychiatry and Medical PsychologyResearch InstituteMedical University of PlovdivPlovdiv 4000Bulgaria Department of PsychiatryDeakin UniversityGeelong 3220Australia
出 版 物：《World Journal of Psychiatry》 (世界精神病学杂志)
年 卷 期：2021年第11卷第1期
页 面：1-12页
核心收录：
学科分类：1002[医学-临床医学] 100205[医学-精神病与精神卫生学] 10[医学]
主 题：Psychiatry Major depression Mood disorders Schizophrenia Antioxidants Oxydative stress
摘 要：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 standardDiagnostic 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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