Psychiatric Illnesses as Disorders of Network Dynamics

Abstract

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In Search for Biomarkers, Endophenotypes or Biosignatures of Ptsd: What Have We Learned From the South East European Study

“…Major mental disorders reflect deficits in access, engagement and disengagement of large scale brain networks as well as disrupted information processing due to damage or dysfunction of individual nodes or edges. As according to dynamical systems theory (DST) the mental functions and processes are implemented in terms of the neural dynamics, mental illnesses may be viewed as disorders of neural network dynamics which involve alterations of oscillations, synchronization among units of a system, attractor states, phase transitions, or deterministic chaos" (Durstewitz et al 2018). There are four recognizable contexts in current computational psychiatry that can be applied to PTSD: 1.…”

Recurrent Neural Networks in Mobile Sampling and Intervention

“…More powerful and perhaps interesting are so-called latent variable ("generative" or "state space") models, which assume that there is some underlying but itself unobserved process z F z u ). 19 Time series are usually generated by some underlying dynamical system that evolves in time, 20 and it is this underlying system that we are often ultimately interested in. Coming back to our example, we may not be so much interested in the subjective ESM ratings per se, but only because these hint to some underlying psychological or biological dynamical process we would like to tap into.…”

Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy

“…Previous studies have found ADHD-related changes in brain function (Krain and Castellanos 2006;Wilens and Spencer 2010;Kaboodvand et al 2020) and gray matter structure (Batty et al 2010;Carmona et al 2005), nevertheless, no significant changes in the white matter structure have been reported (Batty et al 2010;Carmona et al 2005). Moreover, changes both in brain structure and function in ADHD have previously been suggested to be a disorder of attractor dynamics in computational studies of ADHD (Durstewitz et al 2020;Hauser et al 2016). Dynamical system modeling framework permits high-dimensional and complex brain signals to be scaled down to a low-dimensional attractor space where time-resolved interactions between biophysical signals can be studied (e.g.…”

“…BOLD fMRI signals) as well as the influence from behavioral (e.g. distractibility and emotional stability, (Durstewitz et al 2020)) variables can be parametrized and compared across cohorts. However, to the best of our knowledge, there is no experimental data that supports this hypothesis.…”

“…Second, based on the results obtained in the first step, we aimed to provide insights on how our approach can be used to investigate the whole-brain network effects from locally applied in silico perturbation. Indeed, dynamical systems level analysis of ADHD could potentially have profound implications in finding robust objective targets for interventions and controlling individual responses to brain stimulation via designing goal-directed therapeutic interventions (Deco and Kringelbach 2014;Kaboodvand et al 2020;Kaboodvand et al 2019;Durstewitz et al 2020). To this end, we used an in silico excitation simulation protocol to shift the dynamics of ADHD resting-state networks to that of healthy controls.…”