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.

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.

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.

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.

A computational investigation of feedforward and feedback processing in metacontrast backward masking.

In human perception studies, visual backward masking has been used to understand the temporal dynamics of subliminal vs. conscious perception. When a brief target stimulus is followed by a masking stimulus after a short interval of <100 ms, performance on the target is impaired when the target and mask are in close spatial proximity. While the psychophysical properties of backward masking have been studied extensively, there is still debate on the underlying cortical dynamics. One prevailing theory suggests that the impairment of target performance due to the mask is the result of lateral inhibition between the target and mask in feedforward processing. Another prevailing theory suggests that this impairment is due to the interruption of feedback processing of the target by the mask. This computational study demonstrates that both aspects of these theories may be correct. Using a biophysical model of V1 and V2, visual processing was modeled as interacting neocortical attractors, which must propagate up the visual stream. If an activating target attractor in V1 is quiesced enough with lateral inhibition from a mask, or not reinforced by recurrent feedback, it is more likely to burn out before becoming fully active and progressing through V2 and beyond. Results are presented which simulate metacontrast backward masking with an increasing stimulus interval and with the presence and absence of feedback activity. This showed that recurrent feedback diminishes backward masking effects and can make conscious perception more likely. One model configuration presented a metacontrast noise mask in the same hypercolumns as the target, and produced type-A masking. A second model configuration presented a target line with two parallel adjacent masking lines, and produced type-B masking. Future work should examine how the model extends to more complex spatial mask configurations.

A multi-pathway hypothesis for human visual fear signaling.

A hypothesis is proposed for five visual fear signaling pathways in humans, based on an analysis of anatomical connectivity from primate studies and human functional connectvity and tractography from brain imaging studies. Earlier work has identified possible subcortical and cortical fear pathways known as the "low road" and "high road," which arrive at the amygdala independently. In addition to a subcortical pathway, we propose four cortical signaling pathways in humans along the visual ventral stream. All four of these traverse through the LGN to the visual cortex (VC) and branching off at the inferior temporal area, with one projection directly to the amygdala; another traversing the orbitofrontal cortex; and two others passing through the parietal and then prefrontal cortex, one excitatory pathway via the ventral-medial area and one regulatory pathway via the ventral-lateral area. These pathways have progressively longer propagation latencies and may have progressively evolved with brain development to take advantage of higher-level processing. Using the anatomical path lengths and latency estimates for each of these five pathways, predictions are made for the relative processing times at selective ROIs and arrival at the amygdala, based on the presentation of a fear-relevant visual stimulus. Partial verification of the temporal dynamics of this hypothesis might be accomplished using experimental MEG analysis. Possible experimental protocols are suggested.

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.

Is attentional blink a byproduct of neocortical attractors?

This study proposes a computational model for attentional blink or "blink of the mind," a phenomenon where a human subject misses perception of a later expected visual pattern as two expected visual patterns are presented less than 500 ms apart. A neocortical patch modeled as an attractor network is stimulated with a sequence of 14 patterns 100 ms apart, two of which are expected targets. Patterns that become active attractors are considered recognized. A neocortical patch is represented as a square matrix of hypercolumns, each containing a set of minicolumns with synaptic connections within and across both minicolumns and hypercolumns. Each minicolumn consists of locally connected layer 2/3 pyramidal cells with interacting basket cells and layer 4 pyramidal cells for input stimulation. All neurons are implemented using the Hodgkin-Huxley multi-compartmental cell formalism and include calcium dynamics, and they interact via saturating and depressing AMPA/NMDA and GABA(A) synapses. Stored patterns are encoded with global connectivity of minicolumns across hypercolumns and active patterns compete as the result of lateral inhibition in the network. Stored patterns were stimulated over time intervals to create attractor interference measurable with synthetic spike traces. This setup corresponds with item presentations in human visual attentional blink studies. Stored target patterns were depolarized while distractor patterns where hyperpolarized to represent expectation of items in working memory. Simulations replicated the basic attentional blink phenomena and showed a reduced blink when targets were more salient. Studies on the inhibitory effect of benzodiazepines on attentional blink in human subjects were compared with neocortical simulations where the GABA(A) receptor conductance and decay time were increased. Simulations showed increases in the attentional blink duration, agreeing with observations in human studies. In addition, sensitivity analysis was performed on key parameters of the model, including Ca(2+)-gated K(+) channel conductance, synaptic depression, GABA(A) channel conductance and the NMDA/AMPA ratio of charge entry.