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Konovalov, A.

Publications and source records attributed to Konovalov, A..

3 recordsLinked to original sources

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↗

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↗

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↗