Dynamical principles of emotion-cognition interaction: mathematical images of mental disorders

Allostasis and metastasis: The yin and yang of childhood self-regulation

Most research has studied self-regulation by presenting experimenter-controlled test stimuli and measuring change between baseline and stimulus. In the real world, however, stressors do not flash on and off in a predetermined sequence, and there is no experimenter controlling things. Rather, the real world is continuous and stressful events can occur through self-sustaining interactive chain reactions. Self-regulation is an active process through which we adaptively select which aspects of the social environment we attend to from one moment to the next. Here, we describe this dynamic interactive process by contrasting two mechanisms that underpin it: the "yin" and "yang" of self-regulation. The first mechanism is allostasis, the dynamical principle underlying self-regulation, through which we compensate for change to maintain homeostasis. This involves upregulating in some situations and downregulating in others. The second mechanism is metastasis, the dynamical principle underling dysregulation. Through metastasis, small initial perturbations can become progressively amplified over time. We contrast these processes at the individual level (i.e., examining moment-to-moment change in one child, considered independently) and also at the inter-personal level (i.e., examining change across a dyad, such as a parent-child dyad). Finally, we discuss practical implications of this approach in improving the self-regulation of emotion and cognition, in typical development and psychopathology.

Computational Predictions for OCD Pathophysiology and Treatment: A Review

Obsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is not clearly understood. Computational psychiatry is an emerging field in which behavior and its neural correlates are quantitatively analyzed and computational models are developed to improve understanding of disorders by comparing model predictions to observations. The aim is to more precisely understand psychiatric illnesses. Such computational and theoretical approaches may also enable more personalized treatments. Yet, these methodological approaches are not self-evident for clinicians with a traditional medical background. In this mini-review, we summarize a selection of computational OCD models and computational analysis frameworks, while also considering the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks or machine learning methods for modeling, analyzing and classifying patient data. Bayesian interpretations of probability for model selection were also included. The computational dissection of the underlying pathology is expected to narrow the explanatory gap between the phenomenological nosology and the neuropathophysiological background of this heterogeneous disorder. It may also contribute to develop biologically grounded and more informed dimensional taxonomies of psychopathology.

Linear and Nonlinear EEG-Based Functional Networks in Anxiety Disorders

Electrocortical network dynamics are integral to brain function. Linear and nonlinear connectivity applications enrich neurophysiological investigations into anxiety disorders. Discrete EEG-based connectivity networks are unfolding with some homogeneity for anxiety disorder subtypes. Attenuated delta/theta/beta connectivity networks, pertaining to anterior-posterior nodes, characterize panic disorder. Nonlinear measures suggest reduced connectivity of ACC as an executive neuro-regulator in germane "fear circuitry networks" might be more central than considered. Enhanced network complexity and theta network efficiency at rest define generalized anxiety disorder, with similar tonic hyperexcitability apparent in social anxiety disorder further extending to task-related/state functioning. Dysregulated alpha connectivity and integration of mPFC-ACC/mPFC-PCC relays implicated with attentional flexibility and choice execution/congruence neurocircuitry are observed in trait anxiety. Conversely, state anxiety appears to recruit converging delta and beta connectivity networks as panic, suggesting trait and state anxiety are modulated by discrete neurobiological mechanisms. Furthermore, EEG connectivity dynamics distinguish anxiety from depression, despite prevalent clinical comorbidity. Rethinking mechanisms implicated in the etiology, maintenance, and treatment of anxiety from the perspective of EEG network science across micro- and macroscales serves to shed light and move the field forward.

Embodied gestalts: Unstable visual phenomena become stable when they are stimuli for competitive action selection

An animal's environment is rich with affordances. Different possible actions are specified by visual information while competing for dominance over neural dynamics. Affordance competition models account for this in terms of winner-takes-all cross-inhibition dynamics. Multistable phenomena also reveal how the visual system deals with ambiguity. Their key property is spontaneous instability, in forms such as alternating dominance in binocular rivalry. Theoretical models of self-inhibition or self-organized instability posit that the instability is tied to some kind of neural adaptation and that its functional significance is to enable flexible perceptual transitions. We hypothesized that the two perspectives are interlinked. Spontaneous instability is an intrinsic property of perceptual systems, but it is revealed when they are stripped from the constraints of possibilities for action. To test this, we compared a multistable gestalt phenomenon against its embodied version and estimated the neural adaptation and competition parameters of an affordance transition dynamic model. Wertheimer's (Zeitschrift fur Psychologie 61, 161-265, 1912) optimal (β) and pure (φ) forms of apparent motion from a stroboscopic point-light display were endowed with action relevance by embedding the display in a visual object-tracking task. Thus, each mode was complemented by its action, because each perceptual mode uniquely enabled different ways of tracking the target. Perceptual judgment of the traditional apparent motion exhibited spontaneous instabilities, in the form of earlier switching when the frame rate was changed stepwise. In contrast, the embodied version exhibited hysteresis, consistent with affordance transition studies. Consistent with our predictions, the parameter for competition between modes in the affordance transition model increased, and the parameter for self-inhibition vanished.

Brain space and time in mental disorders: Paradigm shift in biological psychiatry

Contemporary psychiatry faces serious challenges because it has failed to incorporate accumulated knowledge from basic neuroscience, neurophilosophy, and brain-mind relation studies. As a consequence, it has limited explanatory power, and effective treatment options are hard to come by. A new conceptual framework for understanding mental health based on underlying neurobiological spatial-temporal mechanisms of mental disorders (already gained by the experimental studies) is beginning to emerge.

Discrete Sequential Information Coding: Heteroclinic Cognitive Dynamics

Discrete sequential information coding is a key mechanism that transforms complex cognitive brain activity into a low-dimensional dynamical process based on the sequential switching among finite numbers of patterns. The storage size of the corresponding process is large because of the permutation capacity as a function of control signals in ensembles of these patterns. Extracting low-dimensional functional dynamics from multiple large-scale neural populations is a central problem both in neuro- and cognitive- sciences. Experimental results in the last decade represent a solid base for the creation of low-dimensional models of different cognitive functions and allow moving toward a dynamical theory of consciousness. We discuss here a methodology to build simple kinetic equations that can be the mathematical skeleton of this theory. Models of the corresponding discrete information processing can be designed using the following dynamical principles: (i) clusterization of the neural activity in space and time and formation of information patterns; (ii) robustness of the sequential dynamics based on heteroclinic chains of metastable clusters; and (iii) sensitivity of such sequential dynamics to intrinsic and external informational signals. We analyze sequential discrete coding based on winnerless competition low-frequency dynamics. Under such dynamics, entrainment, and heteroclinic coordination leads to a large variety of coding regimes that are invariant in time.

Nested positive feedback loops in the maintenance of major depression: An integration and extension of previous models

Several theories of Major Depressive Disorder (MDD) have previously been proposed, focusing largely on either a psychological (i.e., cognitive/affective), biological, or neural/computational level of description. These theories appeal to somewhat distinct bodies of work that have each highlighted separate factors as being of considerable potential importance to the maintenance of MDD. Such factors include a range of cognitive/attentional information-processing biases, a range of structural and functional brain abnormalities, and also dysregulation within the autonomic, endocrine, and immune systems. However, to date there have been limited efforts to integrate these complimentary perspectives into a single multi-level framework. Here we review previous work in each of these MDD research domains and illustrate how they can be synthesized into a more comprehensive model of how a depressive episode is maintained. In particular, we emphasize how plausible (but insufficiently studied) interactions between the various MDD-related factors listed above can lead to a series of nested positive feedback loops, which are each capable of maintaining an individual in a depressive episode. We also describe how these different feedback loops could be active to different degrees in different individual cases, potentially accounting for heterogeneity in both depressive symptoms and treatment response. We conclude by discussing how this integrative model might extend understanding of current treatment mechanisms, and also potentially guide the search for markers to inform treatment selection in individual cases.

Hierarchical dynamics of informational patterns and decision-making

Traditional studies on the interaction of cognitive functions in healthy and disordered brains have used the analyses of the connectivity of several specialized brain networks-the functional connectome. However, emerging evidence suggests that both brain networks and functional spontaneous brain-wide network communication are intrinsically dynamic. In the light of studies investigating the cooperation between different cognitive functions, we consider here the dynamics of hierarchical networks in cognitive space. We show, using an example of behavioural decision-making based on sequential episodic memory, how the description of metastable pattern dynamics underlying basic cognitive processes helps to understand and predict complex processes like sequential episodic memory recall and competition among decision strategies. The mathematical images of the discussed phenomena in the phase space of the corresponding cognitive model are hierarchical heteroclinic networks. One of the most important features of such networks is the robustness of their dynamics. Different kinds of instabilities of these dynamics can be related to 'dynamical signatures' of creativity and different psychiatric disorders. The suggested approach can also be useful for the understanding of the dynamical processes that are the basis of consciousness.

Correlations Between Hysteretic Categorical and Continuous Judgments of Perceptual Stimuli Supporting a Unified Dynamical Systems Approach to Perception

We report from two variants of a figure-ground experiment that is known in the literature to involve a bistable perceptual domain. The first variant was conducted as a two-alternative forced-choice experiment and in doing so tested participants on a categorical measurement scale. The second variant involved a Likert scale measure that was considered to represent a continuous measurement scale. The two variants were conducted as a single within-subjects experiment. Measures of bistability operationalized in terms of hysteresis size scores showed significant positive correlations across the two response conditions. The experimental findings are consistent with a dualistic interpretation of self-organizing perceptual systems when they are described on a macrolevel by means of so-called amplitude equations. This is explicitly demonstrated for a Lotka-Volterra-Haken amplitude equation model of task-related brain activity. As a by-product, the proposed dynamical systems perspective also sheds new light on the anchoring problem of producing numerical, continuous judgments.

Metastable Pain-Attention Dynamics during Incremental Exhaustive Exercise

Background: Pain attracts attention on the bodily regions. Attentional allocation toward pain results from the neural communication across the brain-wide network "connectome" which consists of pain-attention related circuits. Connectome is intrinsically dynamic and spontaneously fluctuating on multiple time-scales. The present study delineates the pain-attention dynamics during incremental cycling performed until volitional exhaustion and investigates the potential presence of nested metastable dynamics. Method: Fifteen young and physically active adults completed a progressive incremental cycling test and reported their discomfort and pain on a body map every 15 s. Results: The analyses revealed that the number of body locations with perceived pain and discomfort increased throughout five temporal windows reaching an average of 4.26 ± 0.59 locations per participant. A total of 37 different locations were reported and marked as painful for all participants throughout the cycling task. Significant differences in entropy were observed between all temporal windows except the fourth and fifth windows. Transient dynamics of bodily locations with perceived discomfort and pain were spanned by three principal components. The metastable dynamics of the body pain locations groupings over time were discerned by three time scales: (1) the time scale of shifts (15 s); (2) the time scale of metastable configurations (100 s), and (3) the observational time scale (1000 s). Conclusion: The results of this study indicate that body locations perceived as painful increase throughout the incremental cycling task following a switching metastable and nested dynamics. These findings support the view that human brain is intrinsically organized into active, mutually interacting complex and nested functional networks, and that subjective experiences inherent in pain perception depict identical dynamical principles to the neural tissue in the brain.

Heteroclinic dynamics of coupled semiconductor lasers with optoelectronic feedback

Generalized Lotka-Volterra (GLV) equations are important equations used in various areas of science to describe competitive dynamics among a population of N interacting nodes in a network topology. In this Letter, we introduce a photonic network consisting of three optoelectronically cross-coupled semiconductor lasers to realize a GLV model. In such a network, the interaction of intensity and carrier inversion rates, as well as phases of laser oscillator nodes, result in various dynamics. We study the influence of asymmetric coupling strength and frequency detuning between semiconductor lasers and show that inhibitory asymmetric coupling is required to achieve consecutive amplitude oscillations of the laser nodes. These studies were motivated primarily by the dynamical models used to model brain cognitive activities and their correspondence with dynamics obtained among coupled laser oscillators.

Body-scaled perception is subjected to adaptation when repetitively judging opportunities for grasping

Experimental evidence is given that the perceptual system adapts to repetitive task execution in a perceptual two-choice judgment task. Participants were tested with respect to their perception of opportunities for plank grasping. Participants had to report whether planks were perceived as objects being graspable with either one hand or two hands. When the plank size was gradually increased and subsequently decreased, transitions from one hand judgments to two hands judgments and vice versa were observed. Analysis of the transition scores revealed that the perceptual judgments were body-scaled, as it is known in the literature. However, judgments were also found to be context dependent. Judgment transition scores were affected in a systematic way by the kind of and the number of previously made judgments. The latter quantitative impact was observed in three related experiments and suggests that perceptual judgments about opportunities for action adapt to task repetition. Overall, the experimental findings are consistent with the predictions of a dynamical systems model, which assumes that perceptual judgments are emergent properties of a self-organizing process that involves inhibitory top-down feedback.

A SYNERGETIC GAIT TRANSITION MODEL FOR HYSTERETIC GAIT TRANSITIONS FROM WALKING TO RUNNING

A model for gait transitions from walking to running is proposed. The model is based on the theory of pattern formation and synergetics. Walking and running are considered as spatiotemporal patterns, while walk-to-run and run-to-walk transitions are regarded as bifurcations. Consequently, the model is cast in the form of coupled amplitude equations as known in the literature on pattern formation. It is shown that the model can reproduce hysteretic gait transitions that have been observed in experimental studies with humans walking on treadmills when locomotion speed is gradually increased and decreased. The control parameter is an appropriately rescaled velocity measure, the so-called Froude number, which is a body-scaled parameter that takes leg length into account. It is shown that the model can reproduce observed gait transitions for individuals and populations. In particular, probabilistic functions describing gait transitions on the population level can be defined that resemble the experimentally determined probabilistic function.

Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis

Contemporary psychiatry faces major challenges. Its syndrome-based disease classification is not based on mechanisms and does not guide treatment, which largely depends on trial and error. The development of therapies is hindered by ignorance of potential beneficiary patient subgroups. Neuroscientific and genetics research have yet to affect disease definitions or contribute to clinical decision making. In this challenging setting, what should psychiatric research focus on? In two companion papers, we present a list of problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders. These problems are loosely grouped into challenges concerning nosology and diagnosis (this Personal View) and problems related to pathogenesis and aetiology (in the companion Personal View). Motivated by successful examples in other disciplines, particularly the list of Hilbert's problems in mathematics, this subjective and eclectic list of priority problems is intended for psychiatric researchers, helping to re-focus existing research and providing perspectives for future psychiatric science.

Perception adapts via top-down regulation to task repetition: A Lotka-Volterra-Haken modeling analysis of experimental data

Two experiments are reported in which participants perceived different physical quantities: size and speed. The perceptual tasks were performed in the context of motor performance problems. Participants perceived the size of objects in order to grasp the objects single handed or with both hands. Likewise, participants perceived the speed of a moving treadmill in order to control walking or running at that speed. In both experiments, the perceptual tasks were repeatedly performed by the participants while the to-be-perceived quantity was gradually varied from small to large objects (Experiment 1) and from low to high speeds (Experiment 2). Hysteresis with negative sign was found when participants were not allowed to execute the motor component, that is, when the execution stage was decoupled from the planning stage. No such effect was found in the control condition, when participants were allowed to execute the motor action. Using a Lotka-Volterra-Haken model for two competing neural populations, it is argued that the observations are consistent with the notion that the repetitions induce an adaptation effect of the perceptual system via top-down regulation. Moreover, the amount of synaptic modulation involved in the adaptation is estimated from participant data.

Learning of Chunking Sequences in Cognition and Behavior

We often learn and recall long sequences in smaller segments, such as a phone number 858 534 22 30 memorized as four segments. Behavioral experiments suggest that humans and some animals employ this strategy of breaking down cognitive or behavioral sequences into chunks in a wide variety of tasks, but the dynamical principles of how this is achieved remains unknown. Here, we study the temporal dynamics of chunking for learning cognitive sequences in a chunking representation using a dynamical model of competing modes arranged to evoke hierarchical Winnerless Competition (WLC) dynamics. Sequential memory is represented as trajectories along a chain of metastable fixed points at each level of the hierarchy, and bistable Hebbian dynamics enables the learning of such trajectories in an unsupervised fashion. Using computer simulations, we demonstrate the learning of a chunking representation of sequences and their robust recall. During learning, the dynamics associates a set of modes to each information-carrying item in the sequence and encodes their relative order. During recall, hierarchical WLC guarantees the robustness of the sequence order when the sequence is not too long. The resulting patterns of activities share several features observed in behavioral experiments, such as the pauses between boundaries of chunks, their size and their duration. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson’s disease and Schizophrenia.

Because chunking is a hallmark of the brain’s organization, efforts to understand its dynamics can provide valuable insights into the brain and its disorders. For identifying the dynamical principles of chunking learning, we hypothesize that perceptual sequences can be learned and stored as a chain of metastable fixed points in a low-dimensional dynamical system, similar to the trajectory of a ball rolling down a pinball machine. During a learning phase, the interactions in the network evolve such that the network learns a chunking representation of the sequence, as when memorizing a phone number in segments. In the example of the pinball machine, learning can be identified with the gradual placement of the pins. After learning, the pins are placed in a way that, at each run, the ball follows the same trajectory (recall of the same sequence) that encodes the perceptual sequence. Simulations show that the dynamics are endowed with the hallmarks of chunking observed in behavioral experiments, such as increased delays observed before loading new chunks.

Towards a complex system understanding of bipolar disorder: A map based model of a complex winnerless competition

Bipolar disorder is characterized by repeated erratic episodes of mania and depression, which can be understood as pathological complex system behavior involving cognitive, affective and psychomotor disturbance. In order to illuminate dynamical aspects of the longitudinal course of the illness, we propose here a novel complex model based on the notion of competition between recurrent maps, which mathematically represent the dynamics of activation in excitatory (Glutamatergic) and inhibitory (GABAergic) pathways. We assume that manic and depressive states can be considered stable sub attractors of a dynamical system through which the mood trajectory moves. The model provides a theoretical framework which can account for a number of complex phenomena of bipolar disorder, including intermittent transition between the two poles of the disorder, rapid and ultra-rapid cycling of episodes and manicogenic effects of antidepressants.

Stress and anxiety: counterpart elements of the stress/anxiety complex

The relationship between stress and anxiety is complicated. Stress initially arises from one's environment; anxiety overlays physiological arousal, cognitive appraisals, emotional states, and behavioral responses. Both are components of a stress-anxiety complex, which has evolved to enable individuals to adapt to their environment and achieve equilibrium. Anxiety disorders, which result when this mechanism goes awry, occur along a spectrum. One of the main variables affecting anxiety disorders is the extent of stress. Each anxiety disorder should be evaluated along a stress axis, leading to improved case conceptualization and intervention strategies.

Robust sequential working memory recall in heterogeneous cognitive networks

Psychiatric disorders are often caused by partial heterogeneous disinhibition in cognitive networks, controlling sequential and spatial working memory (SWM). Such dynamic connectivity changes suggest that the normal relationship between the neuronal components within the network deteriorates. As a result, competitive network dynamics is qualitatively altered. This dynamics defines the robust recall of the sequential information from memory and, thus, the SWM capacity. To understand pathological and non-pathological bifurcations of the sequential memory dynamics, here we investigate the model of recurrent inhibitory-excitatory networks with heterogeneous inhibition. We consider the ensemble of units with all-to-all inhibitory connections, in which the connection strengths are monotonically distributed at some interval. Based on computer experiments and studying the Lyapunov exponents, we observed and analyzed the new phenomenon—clustered sequential dynamics. The results are interpreted in the context of the winnerless competition principle. Accordingly, clustered sequential dynamics is represented in the phase space of the model by two weakly interacting quasi-attractors. One of them is similar to the sequential heteroclinic chain—the regular image of SWM, while the other is a quasi-chaotic attractor. Coexistence of these quasi-attractors means that the recall of the normal information sequence is intermittently interrupted by episodes with chaotic dynamics. We indicate potential dynamic ways for augmenting damaged working memory and other cognitive functions.

Putting reins on the brain. How the body and environment use it

Radical embodied cognitive neuroscience (RECN) will probably rely on dynamical systems theory (DST) and complex systems theory for methods and formalism. Yet, there have been plenty of non-radical neurodynamicists out there for quite some time. How much of their work fits with radical embodied cognitive science, what do they need RECN for, and what are the inconsistencies between RECN and established neurodynamics that would have to be resolved? This paper is both theoretical hypothesis and review. First, it provides a brief overview of the typical, purely structural considerations why the central nervous systems (CNS) should be treated as a nonlinear dynamical system and what this entails. The reader will learn about the circular causality enclosing brain and behavior and different attempts to formalize this circularity. Then, three different attempts at linking dynamics and theory of brain function are described in more detail and criticized. A fourth method based on ecological psychology could fix some of the issues that the others encounter. It is argued that studying self-organization of the brain without taking its ecological embedding into account is insufficient. Finally, based on existing theoretical work we propose two roles that the CNS has to be fulfilling in order to allow an animal to behave adequately in its niche. In its first role the CNS has to be enslaved easily by patterns of behavior that guide the animal through its environment. In the second role the brain has to flexibly switch among patterns, what can be called the metastable circuit breaker. The relevance of this idea is supported using certain motor symptoms of Parkinson's disease (PD). These symptoms can be explained as consequent to an excessive stability of the (metastable) circuit breaker.

A non-linear dynamical approach to belief revision in cognitive behavioral therapy

Belief revision is the key change mechanism underlying the psychological intervention known as cognitive behavioral therapy (CBT). It both motivates and reinforces new behavior. In this review we analyze and apply a novel approach to this process based on AGM theory of belief revision, named after its proponents, Carlos Alchourrón, Peter Gärdenfors and David Makinson. AGM is a set-theoretical model. We reconceptualize it as describing a non-linear, dynamical system that occurs within a semantic space, which can be represented as a phase plane comprising all of the brain's attentional, cognitive, affective and physiological resources. Triggering events, such as anxiety-producing or depressing situations in the real world, or their imaginal equivalents, mobilize these assets so they converge on an equilibrium point. A preference function then evaluates and integrates evidentiary data associated with individual beliefs, selecting some of them and comprising them into a belief set, which is a metastable state. Belief sets evolve in time from one metastable state to another. In the phase space, this evolution creates a heteroclinic channel. AGM regulates this process and characterizes the outcome at each equilibrium point. Its objective is to define the necessary and sufficient conditions for belief revision by simultaneously minimizing the set of new beliefs that have to be adopted, and the set of old beliefs that have to be discarded or reformulated. Using AGM, belief revision can be modeled using three (and only three) fundamental syntactical operations performed on belief sets, which are expansion; revision; and contraction. Expansion is like adding a new belief without changing any old ones. Revision is like adding a new belief and changing old, inconsistent ones. Contraction is like changing an old belief without adding any new ones. We provide operationalized examples of this process in action.

Chunking dynamics: heteroclinics in mind

Recent results of imaging technologies and non-linear dynamics make possible to relate the structure and dynamics of functional brain networks to different mental tasks and to build theoretical models for the description and prediction of cognitive activity. Such models are non-linear dynamical descriptions of the interaction of the core components—brain modes—participating in a specific mental function. The dynamical images of different mental processes depend on their temporal features. The dynamics of many cognitive functions are transient. They are often observed as a chain of sequentially changing metastable states. A stable heteroclinic channel (SHC) consisting of a chain of saddles—metastable states—connected by unstable separatrices is a mathematical image for robust transients. In this paper we focus on hierarchical chunking dynamics that can represent several forms of transient cognitive activity. Chunking is a dynamical phenomenon that nature uses to perform information processing of long sequences by dividing them in shorter information items. Chunking, for example, makes more efficient the use of short-term memory by breaking up long strings of information (like in language where one can see the separation of a novel on chapters, paragraphs, sentences, and finally words). Chunking is important in many processes of perception, learning, and cognition in humans and animals. Based on anatomical information about the hierarchical organization of functional brain networks, we propose a cognitive network architecture that hierarchically chunks and super-chunks switching sequences of metastable states produced by winnerless competitive heteroclinic dynamics.

Current diagnosis and treatment of anxiety disorders

Anxiety disorders are the most prevalent mental health conditions. Although they are less visible than schizophrenia, depression, and bipolar disorder, they can be just as disabling. The diagnoses of anxiety disorders are being continuously revised. Both dimensional and structural diagnoses have been used in clinical treatment and research, and both methods have been proposed for the new classification in the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-5). However, each of these approaches has limitations. More recently, the emphasis in diagnosis has focused on neuroimaging and genetic research. This approach is based partly on the need for a more comprehensive understanding of how biology, stress, and genetics interact to shape the symptoms of anxiety. Anxiety disorders can be effectively treated with psychopharmacological and cognitive-behavioral interventions. These inter ventions have different symptom targets; thus, logical combinations of these strategies need to be further studied in order to improve future outcomes. New developments are forthcoming in the field of alternative strategies for managing anxiety and for treatment-resistant cases. Additional treatment enhancements should include the development of algorithms that can be easily used in primary care and with greater focus on managing functional impairment in patients with anxiety.

Nonlinear relationships between anxiety and visual processing of own and others' faces in body dysmorphic disorder

Individuals with body dysmorphic disorder (BDD) often experience anxiety, as well as perceptual distortions of appearance. Anxiety has previously been found to impact visual processing. This study therefore tested the relationship between anxiety and visual processing of faces in BDD. Medication-free participants with BDD (N=17) and healthy controls (N=16) viewed photographs of their face and a familiar face during functional magnetic resonance imaging. Blood-oxygen-level dependent signal changes in regions involved in anxiety (amygdala) and detailed visual processing (ventral visual stream-VVS) were regressed on anxiety scores. Significant linear relationships between activity in the amygdala and VVS were found in both healthy controls and individuals with BDD. There was a trend of a quadratic relationship between anxiety and activity in the right VVS and a linear relationship between anxiety and activity in the left VVS for the BDD sample, and this was stronger for own-face stimuli versus familiar-face. Results suggest that anxiety symptoms in BDD may be associated with activity in systems responsible for detailed visual processing. This may have clinical implications related to heightened perceptual distortions associated with anxiety.

Computational non-linear dynamical psychiatry: a new methodological paradigm for diagnosis and course of illness

The goal of this article is to highlight the significant potential benefits of applying computational mathematical models to the field of psychiatry, specifically in relation to diagnostic conceptualization. The purpose of these models is to augment the current diagnostic categories that utilize a "snapshot" approach to describing mental states. We hope to convey to researchers and clinicians that non-linear dynamics can provide an additional useful longitudinal framework to understand mental illness. Psychiatric phenomena are complex processes that evolve in time, similar to many other processes in nature that have been successfully described and understood within deterministic chaos and non-linear dynamic computational models. Dynamical models describe mental processes and phenomena that change over time, more like a movie than a photograph, with multiple variables interacting over time. The use of these models may help us understand why and how current diagnostic categories are insufficient. They may also provide a new, more descriptive and ultimately more predictive approach leading to better understanding of the interrelationship between psychological, neurobiological, and genetic underpinnings of mental illness.

Time scale hierarchies in the functional organization of complex behaviors

Traditional approaches to cognitive modelling generally portray cognitive events in terms of ‘discrete’ states (point attractor dynamics) rather than in terms of processes, thereby neglecting the time structure of cognition. In contrast, more recent approaches explicitly address this temporal dimension, but typically provide no entry points into cognitive categorization of events and experiences. With the aim to incorporate both these aspects, we propose a framework for functional architectures. Our approach is grounded in the notion that arbitrary complex (human) behaviour is decomposable into functional modes (elementary units), which we conceptualize as low-dimensional dynamical objects (structured flows on manifolds). The ensemble of modes at an agent’s disposal constitutes his/her functional repertoire. The modes may be subjected to additional dynamics (termed operational signals), in particular, instantaneous inputs, and a mechanism that sequentially selects a mode so that it temporarily dominates the functional dynamics. The inputs and selection mechanisms act on faster and slower time scales then that inherent to the modes, respectively. The dynamics across the three time scales are coupled via feedback, rendering the entire architecture autonomous. We illustrate the functional architecture in the context of serial behaviour, namely cursive handwriting. Subsequently, we investigate the possibility of recovering the contributions of functional modes and operational signals from the output, which appears to be possible only when examining the output phase flow (i.e., not from trajectories in phase space or time).

In most established approaches to cognitive modelling, cognitive events are treated as ‘discrete’ states, thus passing by the continuous nature of cognitive processes. In contrast, some novel approaches explicitly acknowledge cognition’s temporal structure but provides no entry points into cognitive categorization of events and experiences. We attempt to incorporate both aspects in a new framework, which departs from the established idea that complex (human) behaviour is made up of elementary functional ‘building blocks’, referred to as modes. We model these as mathematical objects that are inherently dynamic (i.e., account for change over time). A mechanism sequentially selects the modes required and binds them together to compose complex behaviours. These modes may be subjected to brief inputs. The ensemble of these three ingredients, which influence one another and operate on different time scales, constitutes a functional architecture. We illustrate the architecture via cursive handwriting simulations, and investigate the possibility of recovering the contributions of the architecture from the written word. This appears possible only when focussing on the dynamic modes.

Robust transient dynamics and brain functions

In the last few decades several concepts of dynamical systems theory (DST) have guided psychologists, cognitive scientists, and neuroscientists to rethink about sensory motor behavior and embodied cognition. A critical step in the progress of DST application to the brain (supported by modern methods of brain imaging and multi-electrode recording techniques) has been the transfer of its initial success in motor behavior to mental function, i.e., perception, emotion, and cognition. Open questions from research in genetics, ecology, brain sciences, etc., have changed DST itself and lead to the discovery of a new dynamical phenomenon, i.e., reproducible and robust transients that are at the same time sensitive to informational signals. The goal of this review is to describe a new mathematical framework - heteroclinic sequential dynamics - to understand self-organized activity in the brain that can explain certain aspects of robust itinerant behavior. Specifically, we discuss a hierarchy of coarse-grain models of mental dynamics in the form of kinetic equations of modes. These modes compete for resources at three levels: (i) within the same modality, (ii) among different modalities from the same family (like perception), and (iii) among modalities from different families (like emotion and cognition). The analysis of the conditions for robustness, i.e., the structural stability of transient (sequential) dynamics, give us the possibility to explain phenomena like the finite capacity of our sequential working memory - a vital cognitive function -, and to find specific dynamical signatures - different kinds of instabilities - of several brain functions and mental diseases.