Comparison of brain activity metrics in Chinese and Russian students while perceiving information referencing self or others

Neurocomputing technology is a field of interdisciplinary research and development widely applied in modern digital medicine. One of the problems of neuroimaging technology is the creation of methods for studying human brain activity in socially oriented conditions by using modern information approaches. The aim of this study is to develop a methodology for collecting and processing psychophysiological data, which makes it possible to estimate the functional states of the human brain associated with the attribution of external information to oneself or other people. Self-reference is a person’s subjective assessment of information coming from the external environment as related to himself/herself. Assigning information to other people or inanimate objects is evaluating information as a message about someone else or about things. In modern neurophysiology, two approaches to the study of self-referential processing have been developed: (1) recording brain activity at rest, then questioning the participant for self-reported thoughts; (2) recording brain activity induced by self-assigned stimuli. In the presented paper, a technology was tested that combines registration and analysis of EEG with viewing facial video recordings. The novelty of our approach is the use of video recordings obtained in the first stage of the survey to induce resting states associated with recognition of information about different subjects in later stages of the survey. We have developed a software and hardware module, i. e. a set of related programs and procedures for their application consisting of blocks that allow for a full cycle of registration and processing of psychological and neurophysiological data. Using this module, brain electrical activity (EEG) indicators reflecting individual characteristics of recognition of information related to oneself and other people were compared between groups of 30 Chinese (14 men and 16 women, average age 23.2 ± 0.4 years) and 32 Russian (15 men, 17 women, average age 22.1 ± 0.4 years) participants. We tested the hypothesis that differences in brain activity in functional rest intervals between Chinese and Russian participants depend on their psychological differences in collectivism scores. It was revealed that brain functional activity depends on the subject relevance of the facial video that the participants viewed between resting-state intervals. Interethnic differences were observed in the activity of the anterior and parietal hubs of the default-mode network and depended on the subject attribution of information. In Chinese, but not Russian, participants significant positive correlations were revealed between the level of collectivism and spectral density in the anterior hub of the default-mode network in all experimental conditions for a wide range of frequencies. The developed software and hardware module is included in an integrated digital platform for conducting research in the field of systems biology and digital medicine.

1. Beck A.T., Steer R.A., Brown G.K. Manual for the Beck Depression Inventory II. San Antonio, TX: Psychological Corporation, 1996 Bradley K.A., Colcombe S., Henderson S.E., Alonso C.M., Milham M.P., Gabbay V. Neural correlates of self-perceptions in adolescents with major depressive disorder. Dev. Cogn. Neurosci. 2016; 19:87-97. doi 10.1016/j.dcn.2016.02.007

2. Cross S.E., Bacon P.L., Morris M.L. The relational-interdependent selfconstrual and relationships. J. Pers. Soc. Psychol. 2000;78(4):791-808

3. Delorme A., Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 2004;134(1):9-21. doi 10.1016/j.jneumeth.2003.10.009

4. Fu X., Tamozhnikov S.S., Saprygin A.E., Istomina N.A., Klemeshova D.I., Savostyanov A.N. Convolutional neural networks for classifying healthy individuals practicing or not practicing meditation according to the EEG data. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2023;27(7):851-858. doi 10.18699/VJGB-23-98

5. Haxby J.V., Gobbini M.I., Furey M.L. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science. 2001;293(5539):2425-2430. doi 10.1126/science.1063736

6. Hu C., Di X., Eickhoff S.B., Zhang M., Peng K., Guo H., Sui J. Distinct and common aspects of physical and psychological self-representation in the brain: a meta-analysis of self-bias in facial and selfreferential judgements. Neurosci. Biobehav. Rev. 2016;61:197-207. doi 10.1016/j.neubiorev.2015.12.003

7. Ivanov R., Kazantsev F., Zavarzin E., Klimenko A., Milakhina N., Matushkin Y.G., Savostyanov A., Lashin S. ICBrainDB: an integrated database for finding associations between genetic factors and EEG markers of depressive disorders. J. Pers. Med. 2022;12(1):53. doi 10.3390/jpm12010053

8. Khanin Y.L. A Brief Guide to the C.D. Spielberger State and Trait Anxiety Scale. Leningrad, 1976 (in Russian)

9. Knyazev G.G., Savostyanov A.N., Volf N.V., Liou M., Bocharov A.V. EEG correlates of spontaneous self-referential thoughts: a cross-cultural study. Int. J. Psychophysiol. 2012;86(2):173-181. doi 10.1016/j.ijpsycho.2012.09.002

10. Knyazev G.G., Savostyanov A.N., Bocharov A.V., Tamozhnikov S.S., Saprigyn A.E. Task-positive and task-negative networks and their relation to depression: EEG beamformer analysis. Behav. Brain Res. 2016;306:160-169. doi 10.1016/j.bbr.2016.03.033

11. Knyazev G.G., Savostyanov A.N., Bocharov A.V., Levin E.A., Rudych P.D. Intrinsic connectivity networks in the self- and otherreferential processing. Front. Hum. Neurosci. 2020;14:579703. doi 10.3389/fnhum.2020.579703

12. Knyazev G.G., Savostyanov A.N., Bocharov A.V., Saprigyn A.E. Representational similarity analysis of self-versus other-processing: effect of trait aggressiveness. Aggress. Behav. 2024;50(1):e22125. doi 10.1002/ab.22125

13. Markus H.R., Kitayama S. Culture and the self: implications for cognition, emotion, and motivation. Psychol. Rev. 1991;98(2):224-253. doi 10.1037/0033-295X.98.2.224

14. Neff K.D., McGehee P. Self-compassion and psychological resilience among adolescents and young adults. Self Identity. 2010;9(3):225-240. doi 10.1080/15298860902979307

15. Northoff G., Bermpohl F. Cortical midline structures and the self. Trends Cogn. Sci. 2004;8(3):102-107. doi 10.1016/j.tics.2004.01.004

16. Northoff G., Heinzel A., De Greck M., Bermpohl F., Dobrowolny H., Panksepp J. Self-referential processing in our brain – a meta-analysis of imaging studies on the self. NeuroImage. 2005;31(1):440-457. doi 10.1016/j.neuroimage.2005.12.002

17. Pascual-Margui R.D. Standardized low-resolution brain electromagnetic tomography (sLORETA). Technical details. Methods Find. Exp. Clin. Pharmacol. 2002;24(Suppl.D):5-12

18. Quevedo K., Harms M., Sauder M., Scott H., Mohamed S., Thomas K.M., Schallmo M.-P., Smyda G. The neurobiology of self face recognition among depressed adolescents. J. Affect. Disord. 2018; 229:22-31. doi 10.1016/j.jad.2017.12.023

19. Raichle M.E. The brain’s default mode network. Annu. Rev. Neurosci. 2015;38:433-447. doi 10.1146/annurev-neuro-071013-014030

20. Savostyanov A.N., Vergunov E.G., Saprygin A.E., Lebedkin D.A. Validation of a face image assessment technology to study the dynamics of human functional states in the EEG resting-state paradigm. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2022;26(8):765-772. doi 10.18699/VJGB-22-92

21. Singelis T.M. The measurement of independent and interdependent self-construals. Personality Social Psychol. Bull. 1994;20(5):580-591. doi 10.1177/0146167294205014

22. Spielberger C.D., Gorsuch R.L., Lushene R.E. Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press, 1970

23. Yakovleva E.V. Theory of reference and theory of psyhosys tematics. Izvestiia Rossiiskogo Gosudarstvennogo Pedagogicheskogo Universiteta im. A.I. Gertsena = Izvestia: Herzen University Journal of Humanities and Sciences. 2011;131:226-233 (in Russian)