TY - GEN

T1 - Connectivity Analysis for Measuring of DMN Activity

AU - Zavarzin, Evgeny A.

AU - Savostyanov, Alexander N.

AU - Karpova, Alexandra G.

AU - Milakhina, Natalya S.

N1 - Funding Information: ACKNOWLEDGMENT The development of the data analysis algorithm was supported by the grant of the Russian Science Foundation №22-15-00142. The work of A.N. Savostyanov and N.S. Milakhina on the EEG collection was supported by the foundation budgetary project of the ICG SB RAS №0259-2021-0009. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - Medical informatics is one of the most promising areas of computer science. One of the goals of medical informatics is to develop software to search for disease markers and predictors. The activity of the brain default-mode network is seen as an index to predict the degree of risk of a wide range of mental pathologies, including depression. Usually, the default-mode network activity is measured using functional magnetic resonance imaging. However, to date, there are no reliable tools to effectively assess the functional state of the default-mode network based on electroencephalography analysis. For investigation of brain activity markers of default-mode network activity, an electroencephalography data processing algorithm has been developed in the presented study. Based on the channel correlation of electrodes, specific to the default-mode network, the algorithm obtains connectivity metrics in brain regions and of the network in total. This metric was used for making machine learning models. Models can classify network connectivity metrics to experimental conditions with high precision. Training data was taken from ICBrainDB - an open-access dataset of electroencephalography, psychometry and genetics. In the future, the method we have developed can be applied as a tool for the early diagnosis of depression and other socially significant mental disorders.

AB - Medical informatics is one of the most promising areas of computer science. One of the goals of medical informatics is to develop software to search for disease markers and predictors. The activity of the brain default-mode network is seen as an index to predict the degree of risk of a wide range of mental pathologies, including depression. Usually, the default-mode network activity is measured using functional magnetic resonance imaging. However, to date, there are no reliable tools to effectively assess the functional state of the default-mode network based on electroencephalography analysis. For investigation of brain activity markers of default-mode network activity, an electroencephalography data processing algorithm has been developed in the presented study. Based on the channel correlation of electrodes, specific to the default-mode network, the algorithm obtains connectivity metrics in brain regions and of the network in total. This metric was used for making machine learning models. Models can classify network connectivity metrics to experimental conditions with high precision. Training data was taken from ICBrainDB - an open-access dataset of electroencephalography, psychometry and genetics. In the future, the method we have developed can be applied as a tool for the early diagnosis of depression and other socially significant mental disorders.

KW - connectivity analysis

KW - electroencephalography processing

KW - functional magnetic resonance imaging

KW - machine learning

KW - resting-state functional networks

UR - http://www.scopus.com/inward/record.url?scp=85137330826&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/56d8b758-1a6b-313a-b9ac-4776f4454038/

U2 - 10.1109/EDM55285.2022.9855102

DO - 10.1109/EDM55285.2022.9855102

M3 - Conference contribution

AN - SCOPUS:85137330826

SN - 9781665498043

T3 - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM

SP - 318

EP - 321

BT - Proceedings of the 2022 IEEE 23rd International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

PB - IEEE Computer Society

T2 - 23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

Y2 - 30 June 2022 through 4 July 2022

ER -