Psychiatric illnesses as disorders of network dynamics

• Verfasst von: Durstewitz, Daniel [VerfasserIn]
• Verfasst von: Huys, Quentin J. M. [VerfasserIn]
• Verfasst von: Koppe, Georgia [VerfasserIn]
• Titel: Psychiatric illnesses as disorders of network dynamics
• Verf.angabe: Daniel Durstewitz, Quentin J.M. Huys, and Georgia Koppe
• Jahr: 2021
• Umfang: 12 S.
• Fussnoten: First published: 16 January 2020 ; Gesehen am 01.12.2021
• Titel Quelle: Enthalten in: Biological psychiatry. Cognitive neuroscience and neuroimaging
• Ort Quelle: Amsterdam [u.a.] : Elsevier Inc., 2016
• Jahr Quelle: 2021
• Band/Heft Quelle: 6(2021), 9 vom: Sept., Seite 865-876
• ISSN Quelle: 2451-9030
• DOI: doi:10.1016/j.bpsc.2020.01.001
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Attractor
• Sach-SW: Chaos
• Sach-SW: Dynamical systems
• Sach-SW: Machine learning
• Sach-SW: Recurrent neural networks
• Sach-SW: Schizophrenia
• K10plus-PPN: 1780015585

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.