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

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 D 1 and D 2 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 D 1 and D 2 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.

The Neurobiological Underpinnings of Obsessive-Compulsive Symptoms in Psychosis, Translational Issues for Treatment-Resistant Schizophrenia

Almost 25% of schizophrenia patients suffer from obsessive-compulsive symptoms (OCS) considered a transdiagnostic clinical continuum. The presence of symptoms pertaining to both schizophrenia and obsessive-compulsive disorder (OCD) may complicate pharmacological treatment and could contribute to lack or poor response to the therapy. Despite the clinical relevance, no reviews have been recently published on the possible neurobiological underpinnings of this comorbidity, which is still unclear. An integrative view exploring this topic should take into account the following aspects: (i) the implication for glutamate, dopamine, and serotonin neurotransmission as demonstrated by genetic findings; (ii) the growing neuroimaging evidence of the common brain regions and dysfunctional circuits involved in both diseases; (iii) the pharmacological modulation of dopaminergic, serotoninergic, and glutamatergic systems as current therapeutic strategies in schizophrenia OCS; (iv) the recent discovery of midbrain dopamine neurons and dopamine D1- and D2-like receptors as orchestrating hubs in repetitive and psychotic behaviors; (v) the contribution of N-methyl-D-aspartate receptor subunits to both psychosis and OCD neurobiology. Finally, we discuss the potential role of the postsynaptic density as a structural and functional hub for multiple molecular signaling both in schizophrenia and OCD pathophysiology.

How Obsessive-Compulsive Symptoms Correlate with other Symptoms and Quality of Life in Schizophrenia? A Cross-Sectional Study

Background

Early psychiatrists inferred that obsessive-compulsive symptoms (OCS) represent a defense against psychosis and prevent the progression of schizophrenia. However, recent clinical research has yielded inconsistent results across studies regarding the link between OCS and the symptom domains of schizophrenia and the influence OCS have on a patient's functioning and quality of life (QOL).This study aimed to determine whether OCS has any association with the symptom severity, functioning, and QOL of schizophrenia patients.

Methods

This cross-sectional study done in a tertiary care hospital consecutively recruited 85 outpatients of schizophrenia in clinical remission over one year. Positive and Negative Syndrome Scale (PANSS), Yale-Brown Obsessive Compulsive Scale (Y-BOCS), WHO Quality of Life Brief Version (WHO QOL-BREF), and Global Assessment of Functioning (GAF) scales were applied.

Results

Eighteen patients (21.2%) had OCS (Y-BOCS score of ≥8). Patients with OCS had higher PANSS scores (mean±SD of total score 77.16± 16.4, P < 0.001), suggestive of more severe psychosis, poor functioning overall (mean GAF score 37.35 ± 15.62, P < 0.001), and poor QOL in the psychological domain (P < 0.001). In addition, significant associations were present between the Y-BOCS score and the PANSS (r = 0.65), GAF (r = -0.61), and WHO QOL BREF scores in the physical (r = -0.39), and psychological domains (r = -0.41) (P < 0.001 on all of the above).

Conclusions

Patients with OCS have more severe symptoms and a lower QOL. A significant association exists between the intensity of OCS and schizophrenia symptoms, global functioning, and QOL.

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.