Psychiatric Illnesses as Disorders of Network Dynamics

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

A biophysical model of decision making in an antisaccade task through variable climbing activity

We present a biophysical model of saccade initiation based on competitive integration of planned and reactive cortical saccade decision signals in the intermediate layer of the superior colliculus. In the model, the variable slopes of the climbing activities of the input cortical decision signals are produced from variability in the conductances of Na+, K+, Ca2+ activated K+, NMDA and GABA currents. These cortical decision signals are integrated in the activities of buildup neurons in the intermediate layer of the superior colliculus, whose activities grow nonlinearly towards a preset criterion level. When the level is crossed, a movement is initiated. The resultant model reproduces the unimodal distributions of saccade reaction times (SRTs) for correct antisaccades and erroneous prosaccades as well as the variability of SRTs (ranging from 80ms to 600ms) and the overall 25% of erroneous prosaccade responses in a large sample of 2006 young men performing an antisaccade task

Amphetamine Exerts Dose-Dependent Changes in Prefrontal Cortex Attractor Dynamics during Working Memory

Modulation of neural activity by monoamine neurotransmitters is thought to play an essential role in shaping computational neurodynamics in the neocortex, especially in prefrontal regions. Computational theories propose that monoamines may exert bidirectional (concentration-dependent) effects on cognition by altering prefrontal cortical attractor dynamics according to an inverted U-shaped function. To date, this hypothesis has not been addressed directly, in part because of the absence of appropriate statistical methods required to assess attractor-like behavior in vivo. The present study used a combination of advanced multivariate statistical, time series analysis, and machine learning methods to assess dynamic changes in network activity from multiple single-unit recordings from the medial prefrontal cortex (mPFC) of rats while the animals performed a foraging task guided by working memory after pretreatment with different doses of d-amphetamine (AMPH), which increases monoamine efflux in the mPFC. A dose-dependent, bidirectional effect of AMPH on neural dynamics in the mPFC was observed. Specifically, a 1.0 mg/kg dose of AMPH accentuated separation between task-epoch-specific population states and convergence toward these states. In contrast, a 3.3 mg/kg dose diminished separation and convergence toward task-epoch-specific population states, which was paralleled by deficits in cognitive performance. These results support the computationally derived hypothesis that moderate increases in monoamine efflux would enhance attractor stability, whereas high frontal monoamine levels would severely diminish it. Furthermore, they are consistent with the proposed inverted U-shaped and concentration-dependent modulation of cortical efficiency by monoamines

Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset

Multiscale Computations on Neural Networks: From the Individual Neuron Interactions to the Macroscopic-Level Analysis

We show how the Equation-Free approach for multi-scale computations can be exploited to systematically study the dynamics of neural interactions on a random regular connected graph under a pairwise representation perspective. Using an individual-based microscopic simulator as a black box coarse-grained timestepper and with the aid of simulated annealing we compute the coarse-grained equilibrium bifurcation diagram and analyze the stability of the stationary states sidestepping the necessity of obtaining explicit closures at the macroscopic level. We also exploit the scheme to perform a rare-events analysis by estimating an effective Fokker-Planck describing the evolving probability density function of the corresponding coarse-grained observables

Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states

A Visual Metaphor Describing Neural Dynamics in Schizophrenia

Background: In many scientific disciplines the use of a metaphor as an heuristic aid is not uncommon. A well known example in somatic medicine is the 'defense army metaphor' used to characterize the immune system. In fact, probably a large part of the everyday work of doctors consists of 'translating' scientific and clinical information (i.e. causes of disease, percentage of succes versus risk of side-effects) into information tailored to the needs and capacities of the individual patient. The ability to do so in an effective way is at least partly what makes a clinician a good communicator. Schizophrenia is a severe psychiatric disorder which affects approximately 1% of the population. Over the last two decades a large amount of molecular-biological, imaging and genetic data have been accumulated regarding the biological underpinnings of schizophrenia. However, it remains difficult to understand how the characteristic symptoms of schizophrenia such as hallucinations and delusions are related to disturbances on the molecular-biological level. In general, psychiatry seems to lack a conceptual framework with sufficient explanatory power to link the mental- and molecular-biological domains. Methodology/Principal Findings: Here, we present an essay-like study in which we propose to use visualized concepts stemming from the theory on dynamical complex systems as a 'visual metaphor' to bridge the mental- and molecular-biological domains in schizophrenia. We first describe a computer model of neural information processing; we show how the information processing in this model can be visualized, using concepts from the theory on complex systems. We then describe two computer models which have been used to investigate the primary theory on schizophrenia, the neurodevelopmental model, and show how disturbed information processing in these two computer models can be presented in terms of the visual metaphor previously described. Finally, we describe the effects of dopamine neuromodulation, of which disturbances have been frequently described in schizophrenia, in terms of the same visualized metaphor. Conclusions/Significance: The conceptual framework and metaphor described offers a heuristic tool to understand the relationship between the mental- and molecular-biological domains in an intuitive way. The concepts we present may serve to facilitate communicatio

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Dopamine Modulates Persistent Synaptic Activity and Enhances the Signal-to-Noise Ratio in the Prefrontal Cortex

The importance of dopamine (DA) for prefrontal cortical (PFC) cognitive functions is widely recognized, but its mechanisms of action remain controversial. DA is thought to increase signal gain in active networks according to an inverted U dose-response curve, and these effects may depend on both tonic and phasic release of DA from midbrain ventral tegmental area (VTA) neurons.We used patch-clamp recordings in organotypic co-cultures of the PFC, hippocampus and VTA to study DA modulation of spontaneous network activity in the form of Up-states and signals in the form of synchronous EPSP trains. These cultures possessed a tonic DA level and stimulation of the VTA evoked DA transients within the PFC. The addition of high (≥1 µM) concentrations of exogenous DA to the cultures reduced Up-states and diminished excitatory synaptic inputs (EPSPs) evoked during the Down-state. Increasing endogenous DA via bath application of cocaine also reduced Up-states. Lower concentrations of exogenous DA (0.1 µM) had no effect on the up-state itself, but they selectively increased the efficiency of a train of EPSPs to evoke spikes during the Up-state. When the background DA was eliminated by depleting DA with reserpine and alpha-methyl-p-tyrosine, or by preparing corticolimbic co-cultures without the VTA slice, Up-states could be enhanced by low concentrations (0.1–1 µM) of DA that had no effect in the VTA containing cultures. Finally, in spite of the concentration-dependent effects on Up-states, exogenous DA at all but the lowest concentrations increased intracellular current-pulse evoked firing in all cultures underlining the complexity of DA's effects in an active network.Taken together, these data show concentration-dependent effects of DA on global PFC network activity and they demonstrate a mechanism through which optimal levels of DA can modulate signal gain to support cognitive functioning