A branch of computational psychiatry aims to handle the problems which arose due to the heterogeneity and comorbidity of mental illnesses by capturing phenotypic differences behind symptoms. In order to more accurately describe the mechanisms of the mind and its disturbances, it uses the tools and methods of computational cognitive science. In this paper, we introduce the reader to the (mathematical) language in which (Bayesian) predictive coding was written, which holds one of the possible explanations of the perceptual differences found on the autism or the psychosis spectrum. By representing prior knowledge and sampled sensory information as (prior and likelihood, respectively) distributions, the Bayes-rule allows us to calculate the inferred posterior distribution. The aforementioned terms are introduced through simple examples, such as coin tosses (inferring how biased it is) or making a diagnosis, however, this framework might also help us understand the more complex mechanisms of the mind. Studies show that visual perception can be examined following Bayesian formalization. The likelihood information can be manipulated with bistable or noisy stimuli, whereas through instructions and/or cues, prior expectations are formed. With the use of perceptual and response models fitted to cognitive task performance, it becomes possible to identify parameters which shed light on the features of information processing that characterize the perceptual alterations in certain clinical populations. At the end of the paper we draw attention to the limitations of computational modelling.

Keywords: cognitive sciences, perception, schizophrenia, autism spectrum disorder

Prescribing antidepressant medication is currently the most effective way of treating major depression, but only very few patients achieve permanent improvement. Therefore, it is important to identify objectively measurable markers for effective, personalized therapy. The aim of this review article is to collect all the markers that are robustly predictive of the outcome of therapy. We searched for systematic review articles that have simultaneously investigated the effects of as many different markers as possible on the response to antidepressant therapy in major depressive patients. From these we extracted markers that have been found to be significant by at least two independent review studies and that have proven replicable also within each of these reviews. A separate search was performed for meta-analyses of pharmacogenetic genome-wide association studies. Based on our results, onset time, symptom severity, presence of anhedonia, early treatment response, comorbid anxiety, alcohol consumption, frontal EEG theta activity, hippocampal volume, activity of anterior cingulate cortex, as well as a peripheral marker, serum BDNF levels have proven replicable predictors of antidepressant response. Pharmacogenomic studies to date have not yielded replicable results. Predictors identified as robust by our study may provide a starting point for future machine learning models within a ‘big data’ database of major depressive patients.

(Neuropsychopharmacol Hung 2022; 24(1): 17–28)

Keywords: major depression, treatment response, precision medicine, anhedonia, qEEG, hippocampus, ACC, BDNF

Objectives: Clozapine-induced myocarditis may be a hypersensitivity reaction due to titration that was too rapid for a patient’s clozapine metabolism. Obesity, infections, and inhibitors (e.g., valproate) may lead to clozapine poor metabolizer (PM) status. The hypothesis that 4 patients with clozapine-induced myocarditis from two United States hospitals were clozapine PMs was tested by studying their minimum therapeutic clozapine doses and titrations. Methods: Using methodology from a prior myocarditis case series of 9 Turkish patients, we studied: 1) the concentration-to-dose (C/D) ratio; 2) minimum therapeutic dose required to reach 350 ng/ml (a marker for PM status); and 3) titration speed. Results: All 4 patients were possible clozapine PMs (their respective minimum therapeutic doses were: 134, 84, 119 and 107 mg/day). The identified possible contributors to clozapine PM status were: 1) valproate in Cases 1, 2 and 4; 2) obesity and a urinary tract infection in Case 2; and 3) obesity and very rapid titration in Case 4. Case 3, who was given a normal US titration, appeared to be a genetic clozapine PM. He developed clozapineinduced drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome after rechallenge using 12.5 mg/day > 3 months later. The results were similar to 9 Turkish cases, all of which were PMs (6 on valproate, 4 with obesity, 1 with infection and 1 possibly genetic). Conclusions: Future studies using clozapine levels and considering the role of clozapine PM status should explore whether or not all cases of clozapine-induced myocarditis could be explained by lack of individualized titration.

(Neuropsychopharmacol Hung 2022; 24(1): 29–41)

Keywords: clozapine titration, clozapine adverse effects, drug interaction, drug monitoring, clozapine induced myocarditis, schizophrenia, valproic acid

Background: The COVID-19 pandemic brought about great uncertainty and significant changes in our people’s everyday lives. In times of such crises, it is natural to seek explanations to overcome our fears and uncertainties, contributing to an increase to believe in conspiracy theories which, by yielding explanations, decrease uncertainty and ambiguity and may thus have an effect on mental well-being. In spite of this, the majority of research on conspiracy theories focused on their social effects with little attention to psychological effects. Thus, the aim of our present study was to examine the association between belief in conspiracy theories and different aspects of mental health during the COVID-19 pandemic in a general population sample. Methods: Our analyses included data from the Hungarian leg of the COMET-G (COVID-19 MEntal health international for the General population) study. The Hungarian sample included participants who completed a detailed questionnaire assessing belief in seven conspiracy theory items, as well as STAI-S and CES-D to measure state anxiety and depression, respectively, and answered questions related to their change in depression, anxiety and suicidal thoughts during the pandemic. Association between the individual beliefs as well as a composite Conspiracy Theory Belief Score (CTBS) and mental health measures was analysed using linear regression models. Results: Overall, belief in conspiracy theories was relatively moderate in our sample. Sex and age appeared to have a significant effect on the Overall Conspiracy Theory Belief Score (CTBS), with women having a higher score and scores increasing with age. Some of the individual beliefs also showed associations with age and sex. State anxiety and depression was not significantly associated with CTBS, however in case of depression some individual items were, and symptom clusters within CES-D also showed a pattern of association with some of the individual items. As far as changes in mental health during the pandemic is concerned, no association between overall beliefs and changes in anxiety or depression was found. However, higher overall belief in conspiracy theories was associated with a decrease in suicidal thoughts. Discussion: In our study, we explored the association between conspiracy theories and mental well-being as well as its changes during the COVID-19 pandemic. We found a specific pattern of association between belief in distinct theories and some aspects of depression, as well as lower increase in suicidal ideation in association with increased belief in conspiracy theories. Understanding the role of belief in theories can be key to designing mental health interventions when reacting to unforeseen events in the future.

(Neuropsychopharmacol Hung 2022; 24(1): 42–55)

Keywords: COVID-19, conspiracy theories, depression, anxiety, suicide, suicidal thoughts, mental health, mental health change