Biology subjects

Konovalov, A.

Publications and source records attributed to Konovalov, A..

7 recordsLinked to original sources

bioRxiv2026

Neural Dynamics of Belief and Value Computations Guiding Strategic Social Decisions

Successful strategic behavior must be grounded in beliefs about the opponent and her intentions. While many potential models have been proposed to explain choices in such situations, the neural mechanisms that govern learning and choice in complex strategic contexts remain poorly understood. Here, we use a computational model that combines dynamic learning and choice mechanisms to explain both choices and response times of human participants engaged in a competitive strategic task. Using electroencephalography (EEG), we identify temporally structured stages of neural processing that support an evolving value-based decision process, corresponding to first- and second-order belief updates and evidence accumulation related to the comparison of action values. Gamma-band phase coupling between central and parietal EEG signals varied with individual winning rate, suggesting that strategic behavior involves coordinated information transfer across spatially remote areas. Together, our data characterize the temporally evolving neural dynamics of belief and valuation processes that underlie strategic choice and provide neural validation for assumptions embedded in computational models of this behavior.

neuroscience↗

bioRxiv2025

Biological effects of truffle extracts on bacteria, fungi, and microalgae

One of the current challenges on a global scale is the problem of antibiotic resistance. Excessive and inappropriate use of drugs in healthcare and agriculture has accelerated the development of diseases caused by antibiotic-resistant bacteria. In an effort to combat such resistance, it is extremely important to explore alternative sources of new molecules with biological activities. Truffles have become a promising source of new compounds because they contain a wide range of biologically active ingredients. In addition, they are a consortium of symbiotic microorganisms that produce natural products. The aim of this study was to study the antibiotic and growth stimulating effects of methanolic extracts of truffle fruiting bodies collected in Russia in 2020-2022. The extracts demonstrated inhibitory activity against St. carnosus, E. persicina, and kanamycin-resistant E. coli. Also, for the first time, we have demonstrated that truffle extracts have biological activity in terms of stimulating the growth of C. vulgaris. We have observed the effects of short-term and long-term growth stimulation. Thus, truffles can become a promising source of new active ingredients in medicine, agronomy, and different life sciences.

ecology↗

bioRxiv2025

Separable neurocomputational mechanisms underlying multisensory learning

Efficient control of behavior requires multisensory learning from information distributed across senses. However, most neurocomputational studies have focused on unisensory signals. Here, we identify distinct but interacting neurocomputational mechanisms that support learning of multisensory associations. We designed a task in which behaviorally relevant information was available only from combinations of visual cues with either auditory or tactile cues. In 58 participants undergoing fMRI, we dissociated three processes: multisensory statistical learning (SL), modeled as stimulus-locked Shannon surprise; reinforcement learning (RL), modeled as feedback-locked signed reward prediction errors (RPEs); and feedback-locked unsigned RPEs (uRPEs), reflecting surprise about reward outcomes. Behaviorally, response times scaled with Shannon surprise (SL) while accuracy improved with feedback (RL). Model-based fMRI revealed dissociable but complementary networks: RPEs engaged ventral striatum, vmPFC, and left angular gyrus; surprise recruited bilateral angular gyrus, dlPFC, and precuneus; and uRPEs involved insula, dorsomedial prefrontal, and lateral frontoparietal cortices. Several of these regions extend beyond canonical learning circuits and may contribute to learning when behavior depends on information distributed across sensory modalities. All three networks were modality-general, showing comparable strength for audiovisual and visuotactile learning. Notably, left angular gyrus tracked both Shannon surprise and RPE, suggesting its potential role for integrating structural and value information. These findings indicate that the brain engages distinct but complementary systems for structure-based, reward-based, and outcomesurprise computations. By combining behavioral modeling and fMRI with a novel task design, we provide a framework for dissecting the neurocomputational architecture of multisensory learning.

neuroscience↗

bioRxiv2025

Neural Encoding of Immediate and Instrumental Value During Planning

Planning is a key executive function that enables humans to anticipate future outcomes by mentally simulating action sequences and balancing immediate gains against long-term goals. While we developed a partial understanding of neural mechanisms of planning in spatial navigation and reward-based reinforcement learning, little is known about the relative neural encoding of the instantaneous and instrumental value of the same action. We used a novel fMRI task in which participants repeatedly chose between two options, each with an immediate monetary value and a known future-oriented instrumental value. Our results show that striatal activity is positively correlated with instrumental value, whereas activity in the dorsomedial prefrontal cortex and bilateral insula is negatively correlated with the instantaneous value. These dissociable patterns support specialization in valuation neural circuits during planning.

neuroscience↗

bioRxiv2023

Salience-based information integration: An overarching function of the "social brain"?

Behavior in social contexts is routinely accompanied by neural activity in a brain network comprising the bilateral temporoparietal junction (TPJ), dorsomedial and dorsolateral prefrontal cortex (dmPFC and dlPFC), and precuneus. This network - often referred to as the "social brain network" (SBN) - is thought to have evolved in response to the information processing demands of life in social groups. However, its precise functional contributions to behavior are unclear, since many of its areas are also activated in non-social contexts requiring, for example, attentional orienting or context updating. Here we argue that these results may reflect a basic neural mechanism implemented by areas in this network that is commonly required in both social and non-social contexts: Integrating multiple sensory and memory inputs into salient configurations, such as social constellations or perceptual Gestalts. We tested this hypothesis using a numeracy paradigm that orthogonally varied the salience of sensory target configurations and the required motor responses. Even in this non-social task, several regions of the SBN (TPJ, dmPFC, and precuneus) showed higher activity when the goal required the brain to attend to more versus less salient perceptual configurations. This activation pattern was specific to configuration salience and did not reflect general task demand or switching to new contexts. Taken together, these results suggest that the integration of information into salient configurations may be a key function of SBN regions, thus offering a new perspective on the widespread recruitment of these areas across social and non-social contexts.

neuroscience↗

bioRxiv2022

A unified neural account of contextual and individual differences in altruism

Altruism is critical for cooperation and productivity in human societies, but is known to vary strongly across contexts and individuals. The origin of these differences is largely unknown, but may in principle reflect variations in different types of neurocognitive processes that temporally unfold during altruistic decision making (ranging from initial perceptual processing via value computations to final integrative choice mechanisms). Here, we address this question by examining altruistic choices in different inequality contexts with computational modeling and EEG. Our results show that across all contexts and individuals, wealth distribution choices recruit a similar late decision process evident in model-predicted evidence accumulation signals over parietal regions. Contextual and individual differences in behavior related instead to initial processing of stimulus-locked inequality-related value information in centroparietal and centrofrontal sensors, as well as to gamma-band synchronization of these value-related signals with parietal response-locked evidence-accumulation signals. Our findings suggest separable biological bases for individual and contextual differences in altruism and emphasize that these reflect differences in processing of choice-relevant information.

neuroscience↗

bioRxiv2021

ExhauFS: exhaustive search-based feature selection for classification and survival regression

MotivationFeature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing. ResultsWe present ExhauFS - the user-friendly command-line implementation of the exhaustive search approach for classification and survival regression. Aside from tool description, we included three application examples in the manuscript to comprehensively review the implemented functionality. First, we executed ExhauFS on a toy cervical cancer dataset to illustrate basic concepts. Then, a multi-cohort microarray and RNA-seq breast cancer datasets were used to construct gene signatures for 5-year recurrence classification. Finally, Cox survival regression models were used to fit isomiR signatures for overall survival prediction for patients with colorectal cancer. AvailabilitySource codes and documentation of ExhauFS are available on GitHub: https://github.com/s-a-nersisyan/ExhauFS. Contactsnersisyan@hse.ru

bioinformatics↗