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Psychiatric Illnesses as Disorders of Network Dynamics


Durstewitz, Daniel; Huys, Quentin J M; Koppe, Georgia (2021). Psychiatric Illnesses as Disorders of Network Dynamics. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9):865-876.

Abstract

This review provides a dynamical systems perspective on mental illness. After a brief introduction to the theory of dynamical systems, we focus on the common assumption in theoretical and computational neuroscience that phenomena at subcellular, cellular, network, cognitive, and even societal levels could be described and explained in terms of dynamical systems theory. As such, dynamical systems theory may also provide a framework for understanding mental illnesses. The review examines a number of core dynamical systems phenomena and relates each of these to aspects of mental illnesses. This provides an outline of how a broad set of phenomena in serious and common mental illnesses and neurological conditions can be understood in dynamical systems terms. It suggests that the dynamical systems level may provide a central, hublike level of convergence that unifies and links multiple biophysical and behavioral phenomena in the sense that diverse biophysical changes can give rise to the same dynamical phenomena and, vice versa, similar changes in dynamics may yield different behavioral symptoms depending on the brain area where these changes manifest. We also briefly outline current methodological approaches for inferring dynamical systems from data such as electroencephalography, functional magnetic resonance imaging, or self-reports, and we discuss the implications of a dynamical view for the diagnosis, prognosis, and treatment of psychiatric conditions. We argue that a consideration of dynamics could play a potentially transformative role in the choice and target of interventions.

Abstract

This review provides a dynamical systems perspective on mental illness. After a brief introduction to the theory of dynamical systems, we focus on the common assumption in theoretical and computational neuroscience that phenomena at subcellular, cellular, network, cognitive, and even societal levels could be described and explained in terms of dynamical systems theory. As such, dynamical systems theory may also provide a framework for understanding mental illnesses. The review examines a number of core dynamical systems phenomena and relates each of these to aspects of mental illnesses. This provides an outline of how a broad set of phenomena in serious and common mental illnesses and neurological conditions can be understood in dynamical systems terms. It suggests that the dynamical systems level may provide a central, hublike level of convergence that unifies and links multiple biophysical and behavioral phenomena in the sense that diverse biophysical changes can give rise to the same dynamical phenomena and, vice versa, similar changes in dynamics may yield different behavioral symptoms depending on the brain area where these changes manifest. We also briefly outline current methodological approaches for inferring dynamical systems from data such as electroencephalography, functional magnetic resonance imaging, or self-reports, and we discuss the implications of a dynamical view for the diagnosis, prognosis, and treatment of psychiatric conditions. We argue that a consideration of dynamics could play a potentially transformative role in the choice and target of interventions.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Scopus Subject Areas:Health Sciences > Radiology, Nuclear Medicine and Imaging
Health Sciences > Neurology (clinical)
Life Sciences > Cognitive Neuroscience
Life Sciences > Biological Psychiatry
Uncontrolled Keywords:Cognitive Neuroscience, Biological Psychiatry, Radiology Nuclear Medicine and imaging, Clinical Neurology
Language:English
Date:1 September 2021
Deposited On:02 Nov 2021 09:09
Last Modified:24 May 2024 01:37
Publisher:Elsevier
ISSN:2451-9022
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.bpsc.2020.01.001
PubMed ID:32249208