Electrophysiological signatures of resting state networks under new environmental conditions.

It is assumed that cognitive processes are provided by the regulatory interactions of different brain networks. The three most stable resting state networks, among which the default mode network (DMN), the central executive network (CEN) and the salience network (SN) are considered to be the key neurocognitive networks for understanding higher cognitive functions. Peculiarities of changes in the connectivity of resting state networks of an individual entering a new environment and after a year of adaptation in this environment remain poorly studied. The aim of this study was to investigate the peculiarities of the connectivity of resting state networks calculated in EEG data in students right after moving to an unfamiliar environment and one year after moving. 128-channel EEGs were recorded in the resting state in 45 students (all men) aged from 18 to 29 years, who moved to the North region of Russia (Yakutsk, Republic of Sakha (Yakutia)). Resting state networks were calculated by the seed-based method. The subjects had increased SN connectivity with the sensorimotor cortex and the posterior node of DMN (posterior cingulate cortex and precuneus) in the condition when they were exposed to a new unfamiliar environment, compared to the condition after a year in the same environment. In general, the obtained data are consistent with the notion of increased SN functioning when encountering new significant stimuli and tasks, i.e. new environmental conditions, and the representation of SN as closely related to the function of homeostasis regulation according to organism's internal goals and environmental requirements.

ICBrainDB: An Integrated Database for Finding Associations between Genetic Factors and EEG Markers of Depressive 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.