Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Development of the EEG and Genetic Module for the ICBrainDB Experimental Database to Search for Depressive Disorder Markers. / Zorina, Kseniya A.; Ibrahim, Fath A.A.; Mishchenko, Natalia G. et al.
International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society, 2024. p. 2180-2183 (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Development of the EEG and Genetic Module for the ICBrainDB Experimental Database to Search for Depressive Disorder Markers
AU - Zorina, Kseniya A.
AU - Ibrahim, Fath A.A.
AU - Mishchenko, Natalia G.
AU - Zozulya, Artem A.
AU - Saprygin, Alexander E.
AU - Savostyanov, Alexander N.
N1 - Conference code: 25
PY - 2024
Y1 - 2024
N2 - Depression is a mental disorder, with predisposition largely depending on genetic factors. However, analysis of individual mutations does not reliably predict the predisposition to depression. To solve this problem, two approaches are used - a comprehensive analysis of mutations simultaneous in neurotransmitter brain systems for the same control participants and patients. The module contains EEG recordings processed and cleared of artifacts, as well as the amplitudes of the event-related potentials over 128 recording channels. The module also contains results of many genes, and the use of not only genetic, but also neurophysiological methods for searching for disorder markers. Previously, the ICBrainDB database was developed, which contains both neurophysiological and genetic data on healthy people and patients with depression. As a part of the project to create this database, was developed a new module that allows to analyze the data of event-related potentials recorded in healthy people and patients with depression during performance of “Stop Signal Paradigm” task, and at the same time, SNP analysis data on 164 gene loci of evaluation of genetic loci for presence or absence of single nucleotide substitutions. Preliminary analysis of the data using multivariate statistics revealed some neurophysiological markers and a set of mutations associated with the risk of depression. In addition, the analysis of the occurrence of mutations in different groups of healthy people made it possible to classify them by region of residence. The developed module will further identify associations between genetic and neurophysiological markers of depression, which is likely to improve existing methods for early diagnosis of this disorder.
AB - Depression is a mental disorder, with predisposition largely depending on genetic factors. However, analysis of individual mutations does not reliably predict the predisposition to depression. To solve this problem, two approaches are used - a comprehensive analysis of mutations simultaneous in neurotransmitter brain systems for the same control participants and patients. The module contains EEG recordings processed and cleared of artifacts, as well as the amplitudes of the event-related potentials over 128 recording channels. The module also contains results of many genes, and the use of not only genetic, but also neurophysiological methods for searching for disorder markers. Previously, the ICBrainDB database was developed, which contains both neurophysiological and genetic data on healthy people and patients with depression. As a part of the project to create this database, was developed a new module that allows to analyze the data of event-related potentials recorded in healthy people and patients with depression during performance of “Stop Signal Paradigm” task, and at the same time, SNP analysis data on 164 gene loci of evaluation of genetic loci for presence or absence of single nucleotide substitutions. Preliminary analysis of the data using multivariate statistics revealed some neurophysiological markers and a set of mutations associated with the risk of depression. In addition, the analysis of the occurrence of mutations in different groups of healthy people made it possible to classify them by region of residence. The developed module will further identify associations between genetic and neurophysiological markers of depression, which is likely to improve existing methods for early diagnosis of this disorder.
KW - depression
KW - event-related potentials (ERPs)
KW - open data base
KW - single-nucleotide polymorphism (SNPs)
KW - stop-signal paradigm (SSP)
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85201979819&origin=inward&txGid=40b54fa771318c58f0d0e5488871d34d
UR - https://www.mendeley.com/catalogue/711834da-2d16-3ad7-a649-9fd9b2f442db/
U2 - 10.1109/EDM61683.2024.10615163
DO - 10.1109/EDM61683.2024.10615163
M3 - Conference contribution
SN - 9798350389234
T3 - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
SP - 2180
EP - 2183
BT - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
PB - IEEE Computer Society
T2 - 25th IEEE International Conference of Young Professionals in Electron Devices and Materials
Y2 - 28 June 2024 through 2 July 2024
ER -
ID: 60545053