Biology subjects

Skvortsova, V.

Publications and source records attributed to Skvortsova, V..

5 recordsLinked to original sources

bioRxiv2026

Dissociable Microstructural Correlates of Learning Rate and Learning Noise in Gamified Reward-Based Decision-Making

Humans learn which actions yield the highest rewards through trial and error, gradually forming expectations about outcomes. Yet, people differ substantially in how quickly and precisely they learn. Such individual variability may partly be explained by differences in the brains microstructural organisation. In this large-scale study, 248 participants completed a gamified reward-learning task and underwent quantitative MRI to assess whole-brain microstructural indices of myelination (R1) and cortical iron (R2*). Using computational modelling, we quantified participants learning rates and learning noise, reflecting variability in how reward information is updated over time. Whole-brain voxel-based quantification analyses revealed that increased myelination in the cerebellum was associated with a higher learning rate, whereas learning noise was linked to increased myelination and iron concentration in the precentral gyrus. Together, these findings show that reward learning is not a unitary process but is instead shaped by distinct neurobiological pathways that support learning precision and noise. This work highlights how microstructural variation in sensorimotor and associative cortices contributes to stable versus variable reward learning behaviour across individuals. Significance StatementThis study deepens our understanding of the neural microstructures involved in reward-related decision-making by employing advanced quantitative imaging and computational modelling to investigate brain microstructures associated with individual differences in noisy reward learning and decision-making, using a gamified task. Our results reveal distinct brain microstructures for learning rate and learning noise, which have not been reported before, thereby providing a new understanding of what may be involved in reward-based decision-making. These results provide new potential target pathways for future clinical research in psychiatric disorders where reward processing is implicated, such as ADHD and OCD.

neuroscience↗

bioRxiv2025

Dopamine drives a positive reward bias on human reinforcement learning

Formal theories of reinforcement learning (RL) prescribe a clearly defined function for dopamine, namely modulating learning via reward prediction errors (RPEs). Yet, empirical evidence in humans remains scarce, and recent advances introducing noisy RL cast doubt on a simple one-to-one mapping between neurotransmitters and computational mechanisms. Here, we detail a double-blind, placebo-controlled, randomised pharmacological study using the dopamine precursor L-DOPA, while healthy volunteers performed a volatile two-armed bandit task. Behaviourally, L-DOPA decreased switching behaviour following below-average rewards. Algorithmic RL modelling of human behaviour supported a dual effect of L-DOPA on the rate and precision of learning. By leveraging recurrent neural networks (RNNs) as implementational models of RL, we explain this dual effect through a single inference-time modulation, whereby L-DOPA triggers a positive reward bias at the input of the recurrent layer that implements RL. Our findings highlight a unifying mechanism at the implementation level that explain seemingly disparate algorithmic effects of dopamine.

neuroscience↗

bioRxiv2025

DrugForm-DTA: Towards real-world drug-target binding Affinity Model

Drug-target affinity (DTA) prediction is a fundamental problem in drug discovery. Computational methods for predicting DTA can greatly assist drug design by decreasing the search space and reducing the number of protein-ligand complexes with low affinity. Currently DTA approaches often do not require protein three-dimensional (3D) structural information, which is often not accessible. In this study we present the DrugForm-DTA model, which uses only structure-less representations of ligand and protein. It is a Transformer-based neural network with protein encoding based on ESM, and small molecule ligand encoding obtained with Chemformer. We evaluated the model on standard benchmarks Davis and KIBA, and revealed superior performance of DrugForm-DTA with best result for KIBA (MSE=0.117). Moreover, we developed a ready-to-use model using BindingDB dataset that was subjected to high-quality filtering and transformation. Overall, our method predicts drug-target affinity values with a confidence level comparable to a single in-vitro experiment. Also, we compared DrugForm-DTA against molecular modeling methods and revealed higher efficacy of the developed model for drug-target affinity predictions. Our investigation provides a high accuracy neural network model with performance comparable to experi-mental measurements, filtered and reassessed BindingDB dataset for further usage, and demonstrates outstanding applicability of the proposed method for DTA prediction. Author summaryPredicting drug-target binding affinity is a crucial task in computational drug discovery. Here we present DrugForm-DTA, a ready-to-use Transformer-based model which requires only protein amino acid sequence and ligand SMILES as inputs. The model shows excellent results at commonly used benchmarks. We believe that the success in solving machine learning tasks depends on data quality and quantity, so the main focus of our work is the training data, but not a sophisticated neural architecture. We used BindingDB database as the source of experimental affinity measurements and prepared a refined and purified dataset, suitable for training machine learning models. Trained on this dataset, the DrugForm-DTA model demonstrates accuracy, comparable to a single in-vitro experiment. We also reveal that our model outperforms molecular modeling methods in estimating binding affinity. Both the prepared dataset and the trained model is published and freely available.

bioinformatics↗

bioRxiv2018

Computational noise in reward-guided learning drives behavioral variability in volatile environments

When learning the value of actions in volatile environments, humans often make seemingly irrational decisions which fail to maximize expected value. We reasoned that these non-greedy decisions, instead of reflecting information seeking during choice, may be caused by computational noise in the learning of action values. Here, using reinforcement learning (RL) models of behavior and multimodal neurophysiological data, we show that the majority of non-greedy decisions stems from this learning noise. The trial-to-trial variability of sequential learning steps and their impact on behavior could be predicted both by BOLD responses to obtained rewards in the dorsal anterior cingulate cortex (dACC) and by phasic pupillary dilation - suggestive of neuromodulatory fluctuations driven by the locus coeruleus-norepinephrine (LC-NE) system. Together, these findings indicate that most of behavioral variability, rather than reflecting human exploration, is due to the limited computational precision of reward-guided learning.

neuroscience↗

bioRxiv2017

How context alters value: Price information recruits the brain’s valuation and affective regulation system for shaping experienced taste pleasantness

Informational cues such as the price of a wine can trigger expectations about its taste quality and thereby modulate the sensory experience on a reported and neural level. Yet it is unclear how the brain translates such expectations into sensory pleasantness. We used multilevel mediation analysis of neural and behavioral data obtained in participants who tasted identical wines cued with different prices. We found that the brain's valuation system (BVS) in concert with the anterior prefrontal cortex explained the effect of price cues on taste pleasantness ratings. The sensitivity of the BVS to rewards outside the taste domain moderated the strength of these effects. Moreover, brain mediators of price cue effects overlapped with brain regions previously found to be involved in placebo analgesia. These findings provide novel evidence for the fundamental role that neural pathways linked to motivation and affective regulation play for the effect of informational cues on sensory experiences.

neuroscience↗