  This review provides a dynamical systems perspective on psychiatric symptoms
and disease, and discusses its potential implications for diagnosis, prognosis,
and treatment. After a brief introduction into the theory of dynamical systems,
we will focus on the idea that cognitive and emotional functions are
implemented in terms of dynamical systems phenomena in the brain, a common
assumption in theoretical and computational neuroscience. Specific
computational models, anchored in biophysics, for generating different types of
network dynamics, and with a relation to psychiatric symptoms, will be briefly
reviewed, as well as methodological approaches for reconstructing the system
dynamics from observed time series (like fMRI or EEG recordings). We then
attempt to outline how psychiatric phenomena, associated with schizophrenia,
depression, PTSD, ADHD, phantom pain, and others, could be understood in
dynamical systems terms. Most importantly, we will try to convey that the
dynamical systems level may provide a central, hub-like level of convergence
which 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.
If this assessment is correct, it may have profound implications for the
diagnosis, prognosis, and treatment of psychiatric conditions, as it puts the
focus on dynamics. We therefore argue that consideration of dynamics should
play an important role in the choice and target of interventions.
