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Computational non-linear dynamical psychiatry: a new methodological paradigm for diagnosis and course of illness.

Abstract：

:The goal of this article is to highlight the significant potential benefits of applying computational mathematical models to the field of psychiatry, specifically in relation to diagnostic conceptualization. The purpose of these models is to augment the current diagnostic categories that utilize a "snapshot" approach to describing mental states. We hope to convey to researchers and clinicians that non-linear dynamics can provide an additional useful longitudinal framework to understand mental illness. Psychiatric phenomena are complex processes that evolve in time, similar to many other processes in nature that have been successfully described and understood within deterministic chaos and non-linear dynamic computational models. Dynamical models describe mental processes and phenomena that change over time, more like a movie than a photograph, with multiple variables interacting over time. The use of these models may help us understand why and how current diagnostic categories are insufficient. They may also provide a new, more descriptive and ultimately more predictive approach leading to better understanding of the interrelationship between psychological, neurobiological, and genetic underpinnings of mental illness.

journal_name

J Psychiatr Res

journal_title

Journal of psychiatric research

authors

Bystritsky A,Nierenberg AA,Feusner JD,Rabinovich M

doi

10.1016/j.jpsychires.2011.10.013

subject

Has Abstract

pub_date

2012-04-01 00:00:00

pages

428-35

issue

4

eissn

0022-3956

issn

1879-1379

pii

S0022-3956(11)00261-5

journal_volume

46

pub_type

杂志文章,评审

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