Biology subjects

Karvelis, P.

Publications and source records attributed to Karvelis, P..

4 recordsLinked to original sources

bioRxiv2025

Ketamine and Psilocybin Differentially Impact Sensory Learning During the Mismatch Negativity

Ketamine and psilocybin show potential as therapies for various mental illnesses, including major depressive disorder. However, further investigation into their neural mechanisms is required to understand their effects on the brain. By combining computational modelling with electroencephalography (EEG), we examine the effects of ketamine and psilocybin on hierarchical sensory pwPE learning in the context of the auditory mismatch negativity, an event-related potential consistently shown to be reduced under psychotomimetic interventions. We employed a Bayesian framework and re-analyzed a previously acquired EEG dataset (Schmidt et al., 2012) by modelling single-trial EEG data using the Hierarchical Gaussian Filter. Using a placebo-controlled within-subject crossover design, healthy subjects were administered either S-ketamine or psilocybin during an auditory roving paradigm of pure sinusoidal tones. Our findings elucidate distinct neural impacts of ketamine and psilocybin on sensory learning: ketamine led to a larger reduction in the effect of sensory precision compared to placebo from 207 to 316 ms peaking at 277 ms in the frontal central channels, while psilocybin showed no significant effect. Both drugs reduced the expression of belief precision between 160 to 184 ms, peaking at 172 ms. For higher-level volatility pwPEs, ketamine reduced the expression at 312 ms while psilocybin had a null effect. For perception of elementary imagery, ketamine had a greater effect than psilocybin on sensory and volatility precision, while psilocybin had a greater effect on volatility pwPEs. Our findings suggest hallucinogens have distinct effects on sensory learning that could inform tailored therapies for major depression.

neuroscience↗

bioRxiv2025

Comparative Computational Modeling of Approach-Avoidance Biases in Suicidal Populations via Hierarchical Bayesian Inference

Pavlovian "approach or avoid" impulses are critical behavioral biases that, in excess, are linked to multiple psychiatric conditions. To investigate how such biases contribute to suicidal thoughts and behaviors, we analyzed data from two clinical populations completing an aversive Go/NoGo task. This task disentangles motor action (Go or NoGo) from outcome valence (escape from, or avoidance of, an aversive stimulus), enabling the isolation of Pavlovian biases from instrumental learning processes. We compared multiple computational models that had previously been proposed to explain Pavlovian tendencies, including reinforcement learning, active inference, and drift diffusion-based approaches. We employed a hierarchical Bayesian inference procedure that treats model identity as a random factor at the individual level, allowing an unbiased determination of which mechanisms most accurately captured participants behavior. Across both datasets, models featuring Pavlovian context biases plus a value-decay mechanism best accounted for performance. By contrast, policy-based Pavlovian models and more complex approaches, such as those integrating working memory or active inference, were supported by fewer study participants. These findings suggest that reflexive biases exert a persistent influence on decision-making, and that value decay plays a critical role in shaping behavior over time. Our results demonstrate the importance of systematically comparing and accounting for relevant cognitive processes to explain observed task behaviors. Understanding the factors contributing to task performance may help clarify how Pavlovian tendencies relate to psychopathology, including, in our case, elevated suicide risk. Finally, we illustrate how a complete hierarchical model selection framework can be applied to identify the most plausible mechanisms underlying Pavlovian biases, offering a robust approach for advancing our understanding of task behaviors and establishing clinical utility in future studies. Author summaryAutomatic "approach or avoid" reactions shape behavior, particularly in stressful or negative situations. In this study, we explored how these reflex-like tendencies might contribute to suicidal thoughts and behaviors. Two clinical groups completed a computerized task measuring responses to unpleasant sounds. Participants made either active responses (pressing a button to stop a sound) or passive responses (refraining from pressing to avoid starting a sound), allowing us to examine the interplay of automatic impulses and learning from past experiences. Our analysis showed that behavior was best explained by a model combining stable "approach or avoid" impulses with a forgetting process that reduced reliance on past experiences over time. More complex models involving strategies or memory-based control were less effective. These findings suggest that individuals with suicidal tendencies may rely on persistent reflex-like behaviors and over-index recent outcomes, compromising their ability to learn in uncertain environmental conditions. Understanding these cognitive processes provides insights into why some individuals feel trapped in harmful patterns of thought and behavior. Our work highlights how identifying shared traits in clinical populations using model-based methods can inform targeted mental health interventions and improve our understanding of cognitive functioning across disorders.

neuroscience↗

bioRxiv2018

Acquisition of visual priors and induced hallucinations in chronic schizophrenia

Prominent theories suggest that symptoms of schizophrenia stem from learning deficiencies resulting in distorted internal models of the world. To further test these theories, we here use a visual statistical learning task known to induce rapid implicit learning of the stimulus statistics (Chalk et al., 2010). In this task, participants are presented with a field of coherently moving dots and need to report the presented direction of the dots (estimation task) and whether they saw any dots or not (detection task). Two of the directions were more frequently presented than the others. In controls, the implicit acquisition of the stimuli statistics influences their perception in two ways: 1-motion directions are perceived as being more similar to the most frequently presented directions than they really are (estimation biases); 2-in the absence of stimuli, participants sometimes report perceiving the most frequently presented directions (a form of hallucinations). Such behaviour is consistent with probabilistic inference, i.e. combining learnt perceptual priors with sensory evidence. We investigated whether patients with chronic, stable, treated schizophrenia (n=20) differ from controls (n=23) in the acquisition of the perceptual priors and/or their influence on perception. We found that, although patients were slower than controls, they showed comparable acquisition of perceptual priors, correctly approximating the stimulus statistics. This suggests that patients have no statistical learning deficits in our task. This may reflect our patients relative wellbeing on antipsychotic medication. Intriguingly, however, patients made significantly fewer hallucinations of the most frequently presented directions than controls and fewer prior-based lapse estimations. This suggests that prior expectations had less influence on patients perception than on controls when stimuli were absent or below perceptual threshold.

neuroscience↗

bioRxiv2017

Autistic traits, but not schizotypy, predict overweighting of sensory information in Bayesian visual integration

Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference i.e. how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: i) how proposed theories differ in accounts of ASD vs. schizophrenia and ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due not to weaker prior expectations but to more precise sensory representations.

neuroscience↗