Connectivity Analysis for Measuring of DMN Activity

Evgeny A. Zavarzin, Alexander N. Savostyanov, Alexandra G. Karpova, Natalya S. Milakhina

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

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.

Язык оригиналаанглийский
Название основной публикацииProceedings of the 2022 IEEE 23rd International Conference of Young Professionals in Electron Devices and Materials, EDM 2022
ИздательIEEE Computer Society
Страницы318-321
Число страниц4
ISBN (электронное издание)9781665498043
DOI
СостояниеОпубликовано - 2022
Событие23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022 - Altai, Российская Федерация
Продолжительность: 30 июн. 20224 июл. 2022

Серия публикаций

НазваниеInternational Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
Том2022-June
ISSN (печатное издание)2325-4173
ISSN (электронное издание)2325-419X

Конференция

Конференция23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022
Страна/TерриторияРоссийская Федерация
ГородAltai
Период30.06.202204.07.2022

Предметные области OECD FOS+WOS

  • 1.02 КОМПЬЮТЕРНЫЕ И ИНФОРМАЦИОННЫЕ НАУКИ
  • 3 МЕДИЦИНСКИЕ НАУКИ И ЗДРАВООХРАНЕНИЕ

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