Biology subjects

Portnova, G.

Publications and source records attributed to Portnova, G..

3 recordsLinked to original sources

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↗

bioRxiv2020

PERCEPTION OF NON-VERBAL PROSODY IN CHILDREN WITH ASD

Paralinguistic features of the speaker, such as prosody, temp, loudness, and dynamics, are an important marker of a persons emotional state. The deficit of processing of emotional prosody could be preferably associated with the impairments in individuals with ASDs social behavior. The following two groups of children participated in our study: 30 preschoolers from 4 to 6 years old in the target group (39.1 {+/-} 6.4 scores by Childhood Autism Rating Scale), 24 preschoolers of the control group from 4 to 6 years in the control group. The prosody stimuli were the combination of syllables, said with intonations of "joy," "angry," "sadness," "fear," and "calm." Fast Fourier transform (FFT) is used to analyze power spectrum density (PSD). The resulting normalized spectrum was integrated over unit width intervals in the range of interest (2 to 20 Hz with a step in 1Hz). Children with ASD, similarly to TD children, showed the most pronounced differenced of EEG in response to prosodics of fear and anger. The significant groups differences in PSD were detected for sad and joy intonations. Indexes of EEG differences between pleasure and painful intonations were significantly higher in the control group than children with ASD and between sadness and calm or joy and calm intonations. This paper makes up two main contributions: In general, we obtained that children with ASD have less response to a human voices emotional intonation. The physical characteristics of stimuli are more critical than a sign of emotions. The effect of EEG spectral power has hemisphere specialization in the healthy control group, but not in ASD children. Since spectral power for negative emotions in the target group is higher, we proposed that ASD children worse recognize positive emotions than negative emotions.

neuroscience↗

bioRxiv2019

Longitudinal Changes of Resting-State Functional Connectivity of Amygdala Following Fear Learning and Extinction

Altered functional connectivity of the amygdala has been observed in a resting state immediately after fear learning, even one day after aversive exposure. The persistence of increased resting-state functional connectivity (rsFC) of the amygdala has been a critical finding in patients with stress and anxiety disorders. However, longitudinal changes in amygdala rsFC have rarely been explored in healthy participants. To address this issue, we studied the rsFC of the amygdala in two groups of healthy volunteers. The control group participated in three fMRI scanning sessions of their resting state at the first visit, one day, and one week later. The experimental group participated in three fMRI sessions on the first day: a resting state before fear conditioning, a fear extinction session, and a resting state immediately after fear extinction. Furthermore, this group experienced scanning after one day and week. The fear-conditioning paradigm consisted of visual stimuli with a distinct rate of partial reinforcement by electric shock. During the extinction, we presented the same stimuli in another sequence without aversive pairing. In the control group, rsFC maps were statistically similar between sessions for the left and right amygdala. However, in the experimental group, the increased rsFC mainly of the left amygdala was observed after extinction, one day, and one week. The between-group comparison also demonstrated an increase in the left amygdala rsFC in the experimental group. Our results indicate that functional connections of the left amygdala influenced by fear learning may persist for several hours and days in the human brain.

neuroscience↗