Computational insights on asymmetrical D1 and D2 receptor-mediated chunking: implications for OCD and Schizophrenia.

Repetitive thoughts and motor programs including perseveration are bridge symptoms characteristic of obsessive compulsive disorder (OCD), schizophrenia and in the co-morbid overlap of these conditions. The above pathologies are sensitive to altered activation and kinetics of dopamine D1 and D2 receptors that differently influence sequence learning and recall. Recognizing start and stop elements of motor and cognitive behaviors has crucial importance. During chunking, frequent components of temporal strings are concatenated into single units. We extended a published computational model (Asabuki et al. 2018), where two populations of neurons are connected and simulated in a reservoir computing framework. These neural pools were adopted to represent D1 and D2 striatal neuronal populations. We investigated how specific neural and striatal circuit parameters can influence start/stop signaling and found that asymmetric intra-network connection probabilities, synaptic weights and differential time constants may contribute to signaling of start/stop elements within learned sequences. Asymmetric coupling between the striatal D1 and D2 neural populations was also demonstrated to be beneficial. Our modeling results predict that dynamical differences between the two dopaminergic striatal populations and the interaction between them may play complementary roles in chunk boundary signaling. Start and stop dichotomies can arise from the larger circuit dynamics as well, since neural and intra-striatal connections only partially support a clear division of labor.

Why Does Tardive Dyskinesia Have Oro-facial Predominance? A Network Analysis.

Tardive dyskinesia is a involuntary hyperkinetic disorder which usually occurs in older patients after long-term treatment with antipsychotic drugs. These dyskinesias are mostly irreversible and are frequently expressed in the tongue, cheeks, mandible, perioral area and other regions of the face. In this theoretical study we asked the question, why does tardive dyskinesia often have orofacial predominance? What might be the underlying neural network structure which contributes to this propensity? Graph analysis of high-level cortico-striato-thalamo-cortical network structure suggests a connectivity bottleneck. The number of walks of different lengths from the substantia nigra pars reticulata (SNr) to other vertices, as well as the returning cycles are the lowest in the network, which may indicate a higher damage susceptibility of this node. Analysis was also performed on published data from a recent high resolution histological study on cortico-striato-thalamo-cortical networks in rodents. Finer network partitioning and adjacency matrices demonstrated that the SNr has a heterogeneous connectivity structure and the number of local walks from nodes neighboring orofacial neural representation is higher, indicating possible early compensatory escape routes. However, with more extensive SNr damage the larger circuit compensation might be limited. This area of inquiry is important for future research, because identifying key vulnerable structures may provide more targeted therapeutical interventions.

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.

Neural dynamics in co-morbid schizophrenia and OCD: A computational approach.

The co-morbidity of obsessive-compulsive disorder (OCD) and schizophrenia is higher than what would be expected by chance and the common underlying neuropathophysiology is not well understood. Repetitive stereotypes and routines can be caused by perseverative thoughts and motor sequences in both of these disorders. We extended a previously published computational model to investigate cortico-striatal network dynamics. Given the considerable overlap in symptom phenomenology and the high degree of co-morbidity between OCD and schizophrenia, we examined the dynamical consequences of functional connectivity variations in the overlapping network. This was achieved by focusing on the emergence of network oscillatory activity and examining parameter sensitivity. Opposing activity levels are present in orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC) in schizophrenia and OCD. We found that with over-compensation of the primary pathology, emergence of the other disorder can occur. The oscillatory behavior is delicately modulated by connections between the OFC/ACC to the ventral and dorsal striatum and by the coupling between the ACC and dorsolateral prefrontal cortex (DLPFC). Modulation on cortical self-inhibition (e.g. serotonin reuptake inhibitor treatment) together with dopaminergic input to the striatum (e.g. anti-dopaminergic medication) has non-trivial complex effects on the network oscillatory behavior, with an optimal modulatory window. Additionally, there are several disruption mechanisms and compensatory processes in the cortico-striato-thalamic network which may contribute to the underlying neuropathophysiology and clinical heterogeneity in schizo-obsessive spectrum disorders. Our mechanistic model predicts that dynamic over-compensation of the primarily occuring neuropathophysiology can lead to the secondary co-morbid disease.

Cortico-striatal language pathways dynamically adjust for syntactic complexity: A computational study.

A growing body of literature supports a key role of fronto-striatal circuits in language perception. It is now known that the striatum plays a role in engaging attentional resources and linguistic rule computation while also serving phonological short-term memory capabilities. The ventral semantic and the dorsal phonological stream dichotomy assumed for spoken language processing also seems to play a role in cortico-striatal perception. Based on recent studies that correlate deep Broca-striatal pathways with complex syntax performance, we used a previously developed computational model of frontal-striatal syntax circuits and hypothesized that different parallel language pathways may contribute to canonical and non-canonical sentence comprehension separately. We modified and further analyzed a thematic role assignment task and corresponding reservoir computing model of language circuits, as previously developed by Dominey and coworkers. We examined the models performance under various parameter regimes, by influencing how fast the presented language input decays and altering the temporal dynamics of activated word representations. This enabled us to quantify canonical and non-canonical sentence comprehension abilities. The modeling results suggest that separate cortico-cortical and cortico-striatal circuits may be recruited differently for processing syntactically more difficult and less complicated sentences. Alternatively, a single circuit would need to dynamically and adaptively adjust to syntactic complexity.

Pathological neural attractor dynamics in slowly growing gliomas supports an optimal time frame for white matter plasticity.

Neurological function in patients with slowly growing brain tumors can be preserved even after extensive tumor resection. However, the global process of cortical reshaping and cerebral redistribution cannot be understood without taking into account the white matter tracts. The aim of this study was to predict the functional consequences of tumor-induced white matter damage by computer simulation. A computational model was proposed, incorporating two cortical patches and the white matter connections of the uncinate fasciculus. Tumor-induced structural changes were modeled such that different aspects of the connectivity were altered, mimicking the biological heterogeneity of gliomas. The network performance was quantified by comparing memory pattern recall and the plastic compensatory capacity of the network was analyzed. The model predicts an optimal level of synaptic conductance boost that compensates for tumor-induced connectivity loss. Tumor density appears to change the optimal plasticity regime, but tumor size does not. Compensatory conductance values that are too high lead to performance loss in the network and eventually to epileptic activity. Tumors of different configurations show differences in memory recall performance with slightly lower plasticity values for dense tumors compared to more diffuse tumors. Simulation results also suggest an optimal noise level that is capable of increasing the recall performance in tumor-induced white matter damage. In conclusion, the model presented here is able to capture the influence of different tumor-related parameters on memory pattern recall decline and provides a new way to study the functional consequences of white matter invasion by slowly growing brain tumors.

Dopamine induced switch in the subthreshold dynamics of the striatal cholinergic interneurons: a numerical study.

The striatum is a part of the basal ganglia, which are a group of nuclei in the brain associated with motor control, cognition and learning. Striatal cholinergic interneurons (AchNs) play a crucial role in these functions. AchNs are tonically active in vivo and in vitro, and are able to fire in the absence of synaptic inputs. AchNs respond to sensory stimuli and sensorimotor learning by transiently suppressing their firing activity. This pause is dopamine signal sensitive, but the neurophysiological mechanism of the dopaminergic influence is under debate. Both the regular spiking response as well as the pause response are influenced by the inwardly rectifying outward G(kir), a slow hyperpolarization activated noninactivating G(h), and calcium and calcium-dependent potassium conductances [Wilson, C., Goldberg, J., 2006. Origin of the slow afterhyperpolarization and slow rhythmic bursting in striatal cholinergic interneurons. J. Neurophysiol. 95(1), 196-204; Wilson, C., 2005. The mechanism of intrinsic amplification of hyperpolarizations and spontaneous bursting in striatal cholinergic interneurons. Neuron 45(4), 575-585]. Recent experimental evidence has shown that dopaminergic modulations on G(h), G(kir) and calcium conductances influence the AchN's excitability [Deng, P., Zhang, Y., Xu, Z., 2007. Involvement of I(h) in dopamine modulation of tonic firing in striatal cholinergic interneurons. J. Neurosci. 27(12), 3148-3156; Aosaki, T., Kiuchi, K., Kawaguchi, Y., 1998. Dopamine D(1)-like receptor activation excites rat striatal large aspiny neurons in vitro. J. Neurosci. 18(14), 5180-5190]. We employed computational models of the AchN to analyze the conductance based dopaminergic changes. We analyzed the robustness of these subthreshold oscillations and how they are affected by dopaminergic modulation. Our results predict that these conductances allow the dopamine to switch the AchN between stable oscillatory and fixed-point behaviors. The present approach and results show that dopamine receptors (D(1) and D(2)) mediate opposing effects on this switch and therefore on the suprathreshold excitability as well. The switching effect of the dopaminergic signal is the major qualitative feature that can serve as a building block for higher network-level descriptions. To our knowledge this is the first paper that synthesizes the growing body of experimental literature about the dopaminergic modulation of the AchNs into a modelling framework.

Effect of synaptic plasticity on sensory coding and steady-state filtering properties in the electric sense.

Our modeling study examines short-term plasticity at the synapse between afferents from electroreceptors and pyramidal cells in the electrosensory lateral lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus. It focusses on steady-state filtering and coherence-based coding properties. While developed for electroreception, our study exposes general functional features for different mixtures of depression and facilitation. Our computational model, constrained by the available in vivo and in vitro data, consists of a synapse onto a deterministic leaky integrate-and-fire (LIF) neuron. The synapse is either depressing (D), facilitating (F) or both (FD), and is driven by a sinusoidally or randomly modulated Poisson process. Due to nonlinearity, numerically computed input-output transfer functions are used to determine the filtering properties. The gain of the response at each sinusoidally modulated frequency is computed by dividing the fitted amplitudes of the input and output cycle histograms of the LIF models. While filtering is always low-pass for F alone, D alone exhibits a gain resonance (non-monotonicity) at a frequency that decreases with increasing recovery time constant of synaptic depression (tau(d)). This resonance is mitigated by the presence of F. For D, F and FD, coherence improves as the synaptic conductance time constant (tau(g)) increases, yet the mutual information per spike decreases. The information per spike for D and F follows opposite trends as their respective time constants increase. The broadband but non-monotonic gain and coherence functions seen in vivo suggest that D and perhaps FD dynamics are involved at this synapse. Our results further predict that the likely synaptic configuration is a slower tau(g), e.g. via a mixture of AMPA and NMDA synapses, and a relatively smaller synaptic facilitation time constant (tau(f)) and larger tau(d) (with tau(f) smaller than tau(d) and tau(g)). These results are compatible with known physiology.

Role of hyperpolarization-activated conductances in the lateral superior olive: a modeling study.

This modeling study examines the possible functional roles of two hyperpolarization-activated conductances in lateral superior olive (LSO) principal neurons. Inputs of these LSO neurons are transformed into an output, which provides a firing-rate code for a certain interaural sound intensity difference (IID) range. Recent experimental studies have found pharmacological evidence for the presence of both the Gh conductance as well as the inwardly rectifying outward GKIR conductance in the LSO. We addressed the question of how these conductances influence the dynamic range (IID versus firing rate). We used computer simulations of both a point-neuron model and a two-compartmental model to investigate this issue, and to determine the role of these conductances in setting the dynamic range of these neurons. The width of the dynamic regime, the frequency-current (f-I) function, first-spike latency, subthreshold oscillations and the interplay between the two hyperpolarization activated conductances are discussed in detail. The in vivo non-monotonic IID-firing rate function in a subpopulation of LSO neurons is in good correspondence with our simulation predictions. Two compartmental model simulation results suggest segregation of Gh and GKIR conductances on different compartments, as this spatial configuration could explain certain experimental results.