Evgeny A. Zavarzin

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.

In this study, we collected and systemized diverse information related to depressive and anxiety disorders as the first step on the way to investigate the associations between molecular genetics, electrophysiological, behavioral, and psychological characteristics of people. Keeping that in mind, we developed an internet resource including a database and tools for primary presentation of the collected data of genetic factors, the results of electroencephalography (EEG) tests, and psychological questionnaires. The sample of our study was 1010 people from different regions of Russia. We created the integrated ICBrainDB database that enables users to easily access, download, and further process information about individual behavioral characteristics and psychophysiological responses along with inherited trait data. The data obtained can be useful in training neural networks and in machine learning construction processes in Big Data analysis. We believe that the existence of such a resource will play an important role in the further search for associations of genetic factors and EEG markers of depression.