Biology subjects

Sysoeva, O.

Publications and source records attributed to Sysoeva, O..

5 recordsLinked to original sources

bioRxiv2026

EEG correlates of auditory rise time processing: A systematic review

Rise time (RT) is considered to be one of the most significant acoustical characteristics of auditory speech stimuli. A substantial amount of data has been accumulated on the neurophysiological mechanisms of RT processing under different conditions and in different groups of people, but these data have not been systematised. This review focuses on studies that have investigated electroencephalographic (EEG) markers of RT sensitivity. The present literature search was conducted according to the PRISMA statement in PubMed, Web of Science and APA PsychInfo databases. The resultant review comprised 37 studies that considered diverse aspects of RT processing. The review describes the main stimulation parameters affecting electrophysiological markers of RT processing reflected in different components of event-related potentials, brainstem responses and cortical rhythmic activity. The main finding of this review is that the rise time prolongation leads to a decrease in the amplitude of the main ERP components and an increase in their latencies. However, the sensitivity of the EEG markers varied with the earliest components tracking the subtle difference (few tens of microseconds), while the later components coding the larger one (up to 500 ms). Nevertheless, the observed effects may vary and depend on some aspects of the experimental paradigm, age of participants and speech-related problems. Future research may benefit by addressing understudied clinical groups and ERP components such as P1 and N2, dominated in children.

neuroscience↗

bioRxiv2025

Novelty, category and orientation tuning for printed characters: a magnetoencephalography study with fast periodic visual stimulation

Letter recognition is assumed to involve several levels of analysis, including coarse tuning for category and novelty and more fine tuning, related to letter orientation. We employed an oddball fast periodic visual stimulation (FPVS) paradigm with magnetoencephalography (Elekta VectorView, 306 sensors) to study neural discrimination responses in the source space. Using contrasts between native letters and foreign letters, digits, or inverted native letters, we aimed to isolate the neural responses to visual novelty, category, and orientation during character analysis. The study was conducted with a cohort of 25 adults. The response topography demonstrated bilateral organization, including language-related brain regions as ventral occipitotemporal cortex, inferior parietal cortex and middle temporal areas. Comparing conditions, we revealed right lateralized parietal clusters, associated with novelty tuning, and left lateralized occipitotemporal clusters exhibiting higher activity for letters among digits discrimination, supporting the role of this area in letter processing. No distinct spatial patterns specific to orientation tuning were observed in comparison to novelty and category tuning. We proposed that expertise-dependent orientation-specific tuning mechanisms may operate within an embedded, spatially overlapping with coarse tuning neural framework, characterized by special spatiotemporal patterns.

neuroscience↗

bioRxiv2024

Rapid Semantic Processing: An MEG Study of Narrative Text Reading

Research has shown that semantic analysis occurs at early stages of word processing (less than 200 ms). While traditional studies have focused on isolated words/sentences, our research explores rapid semantic processing during reading stories using event-related potentials and magnetoencephalography. We employed the rapid serial visual presentation paradigm to present texts word by word. Each word presentation lasted 150 ms, enhancing rapid semantic processing. We computed semantic dissimilarity (SD) values for each word and categorized them into quartiles to investigate their effect on brain responses. Our analysis revealed significant ERP differences within early time windows (120-132 ms). Two distinct clusters were identified: positive in the right occipital region and negative in the left temporal region. In both clusters less pronounced responses were registered for words with lowest SD which corresponds to theories of predictive coding. These findings broaden our understanding of rapid semantic processing and suggest new methodology.

neuroscience↗

bioRxiv2024

Novel method to assess group dynamics in rats reveals deficits in behavioral contagion in KM rats

Behavioral copying is a key process in group actions, but it is challenging for individuals with Autism Spectrum Disorder (ASD). We investigated behavioral contagion, or instinctual replication of behaviors, in Krushinky-Molodkina (KM) rats (n=16), a new rodent model for ASD, compared to control Wistar rats (n=15). A randomly chosen healthy Wistar male ("demonstrator rat") was introduced to the homecage of experimental rats ("observers") 10-14 days before the experiments to become a member of the group. For the implementation of the behavioral contagion experiment, we used the IntelliCage system, where rats can live in a group of 5-6 rats and their water visits can be fully controlled. During the experiment, the demonstrator was taken out of IntelliCage for 24 hours of water deprivation and then placed back. As a result, a drinking behavior of the water-deprived demonstrator rat prompted activated behaviors in the whole group. Unlike the Wistar controls, KM observers showed fewer visits to the drinking bottles, particularly lacking inspection visits. The control group, in contrast, exhibited a dynamic, cascade-like visiting of the water corners. The proportion of activated observers in KM rats was significantly lower, as compared to Wistar ones, and they did not mimic other observer rats. KM rats, therefore, displayed an attenuated pattern of behavioral contagion, highlighting social deficits in this ASD model. This study suggests that measuring group dynamics of behavioral contagion in an automated, non-invasive setup offers valuable insights into social behavior in rodents, particularly for studying social deficits in ASD models. HighlightsO_LIThirsty demonstrators triggered an avalanche of observers visits to the water corners C_LIO_LIThe contaged behavior was attenuated in observer KM rats C_LIO_LIBehavioral contagion test provides a new tool for objective, automated phenotyping in rodent models of social deficits C_LI

animal behavior and cognition↗

bioRxiv2021

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE)

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye-movements and muscle artifacts from electroencephalography (EEG). Due to other possible EEG contaminations, a rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts involvement. Noteworthy, as also revealed by our study, experts opinion about the nature of a component often disagrees highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm not only to remove artifacts but also to find specific patterns in EEG signals using ICA decomposition based on accumulated experts knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on estimation of IC time-series, IC amplitude topography and spectral power distribution. The platform allows supervised ML model training and re-training on available data subsamples for better performance in specific tasks (i.e. movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model shows that it can be used not only for detection of noisy IC but also for automatic identifications of components related to the functional brain oscillations such as alpha and mu-rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will be used for continuous improvement of ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

neuroscience↗