Association of autistic personality traits with the EEG scores in non-clinical subjects during the facial video viewing

A software information module of the experimental computer platform “EEG_Self-Construct” was developed and tested in the framework of this study. This module can be applied for identification of neurophysiological markers of self-referential processes based on the joint use of EEG and facial video recording to induce the brain’s functional states associated with participants’ personality traits. This module was tested on a group of non-clinical participants with varying degrees of severity of autistic personality traits (APT) according to the Broad Autism Phenotype Questionnaire. The degree of individual severity of APT is a quantitative characteristic of difficulties that a person has when communicating with other people. Each person has some individual degree of severity of such traits. Patients with autism are found to have high rates of autistic traits. However, some individuals with high rates of autistic traits are not accompanied by clinical symptoms. Our module allows inducing the brain’s functional states, in which the EEG indicators of people with different levels of APT significantly differ. In addition, the module includes a set of software tools for recording and analyzing brain activity indices. We have found that relationships between brain activity and the individual level of severity of APT in non-clinical subjects can be identified in resting-state conditions following recognition of self-referential information, while recognition of socially neutral information does not induce processes associated with APT. It has been shown that people with high scores of APT have increased spectral density in the delta and theta ranges of rhythms in the frontal cortical areas of both hemispheres compared to people with lower scores of APT. This could hypothetically be interpreted as an index of reduced brain activity associated with recognition of self-referential information in people with higher scores of autistic traits. The software module we are developing can be integrated with modules that allow identifying molecular genetic markers of personality traits, including traits that determine the predisposition to mental pathologies.

1. Baron-Cohen S. The extreme male brain theory of autism. Trends Cogn. Sci. 2002;6(6):248-254. doi 10.1016/s1364-6613(02)01904-6

2. Baron-Cohen S. Autism: the empathizing-systemizing (E-S) theory. Ann. N.Y. Acad. Sci. 2009;1156:68-80. doi 10.1111/j.1749-6632. 2009.04467.x

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

4. 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

5. Fanelli G., Robinson J., Fabbri C., Bralten J., Mota N.R., Arenella M., Sprooten E., Franke B., Kas M., Andlauer T.F., Serretti A. Shared genetics linking sociability with the brain’s default mode network. medRxiv. [Preprint]. 2024. May 25:2024.05.24.24307883. doi 10.1101/2024.05.24.24307883

6. Frith U. Asperger and his syndrome. In: Frith U. (Ed.). Autism and Asperger Syndrome. Cambridge University Press, 1991;1-36

7. Genovese A., Butler M.G. The autism spectrum: behavioral, psychiatric and genetic associations. Genes (Basel). 2023;14(3):677. doi 10.3390/genes14030677

8. Georgiades S., Bishop S.L., Frazier T. Editorial perspective: longitudinal research in autism − introducing the concept of ‘chronogeneity’. J. Child Psychol. Psychiatry. 2017;58:634-636. doi 10.1111/jcpp.12690

9. Harikumar A., Evans D.W., Dougherty C.C., Carpenter K.L.H., Michael A.M. A review of the default mode network in autism spectrum disorders and attention deficit hyperactivity disorder. Brain Connect. 2021;11(4):253-263. doi 10.1089/brain.2020.0865

10. Harms M.B., Martin A., Wallace G.L. Facial emotion recognition in autism spectrum disorders: a review of behavioral and neuroimaging studies. Neuropsychol. Rev. 2010;20(3):290-322. doi 10.1007/s11065-010-9138-6

11. Hurley L., Parlier M., Reznick J., Piven J. The broad autism phenotype questionnaire. J. Autism Dev. Disord. 2007;37(9):1679-1690. doi 10.1007/s10803-006-0299-3

12. 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

13. Khanin Yu.L. Quick Guide to C.D. Spielberger’s Scale of State and Trait Anxiety. Leningrad, 1976 (in Russian)

14. Knyazev G.G. Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neurosci. Biobehav. Rev. 2007;31(3): 377-395. doi 10.1016/j.neubiorev.2006.10.004

15. Knyazev G.G., Mitrofanova L.G., Bocharov A.V. Validization of Russian version of Goldberg’s ‘‘Big-five factor markers»” inventory. Psikhologicheskii Zhurnal. 2010;31(5):100-110 (in Russian)

16. Knyazev G.G., Mitrofanova L.G., Razumnikova O.M., Barchard K. Adaptation of Russian language version of K. Barchard’s Emotional Intelligence Questionnaire. Psikhologicheskii Zhurnal. 2012;33(4): 112-120 (in Russian)

17. Lavenne-Collot N., Tersiguel M., Dissaux N., Degrez C., Bronsard G., Botbol M., Berthoz A. Self/other distinction in adolescents with autism spectrum disorder (ASD) assessed with a double mirror paradigm. PLoS One. 2023;18(3):e0275018. doi 10.1371/journal.pone.0275018

18. Lin M., Wang Y., Lopez-Naranjo C., Hu S., Reyes R.C.G., Paz-Linares D., Areces-Gonzalez A., Hamid A.I.A., Evans A.C., Savostyanov A.N., Calzada-Reyes A., Villringer A., Tobon-Quinero C.A., Garcia-Agustin D., Yao D., Dong L., Aubet-Vazquez E., Reza F., Razzaq F.A., Omar H., Abdullah J.M., Galler J.R., Ochoa-Gomez J.F., Prichep L.S., Galan-Garcia L., Morales-Chacon L., Valdes-Sosa M.J., Trondle M., Zulkifly M.F.M., Rahman M.R.B.A., Milakhina N.S., Langer N., Rudych P., Koenig T., Virues-Alba T.A., Lei X., Bringas-Vega M.L., Bosch-Bayard J.F., Valdes-Sosa P.A. Harmonized-Multinational qEEG norms (HarMNqEEG). NeuroImage. 2022;256:119190. doi 10.1016/j.neuroimage.2022.119190

19. Lovaas O.I. Behavioral treatment and normal educational and intellectual functioning in young autistic children. J. Consult. Clin. Psychol. 1987;55(1):3-9. doi 10.1037/0022-006x.55.1.3

20. Murray K., Johnston K., Cunnane H., Kerr Ch., Spain D., Gillan N., Hammond N., Murphy D., Happe F. A new test of advanced theory of mind: The “Strange Stories Film Task” captures social processing differences in adults with autism spectrum disorders. Autism Res. 2017;10(6):1120-1132. doi 10.1002/aur.1744

21. 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

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

23. Piven J., Palmer P., Jacobi D., Childress D., Arndt S. Broader autism phenotype: evidence from a family history study of multiple-incidence autism families. Am. J. Psychiatry.1997;154(2):185-190. doi 10.1176/ajp.154.2.185

24. Ronde M., van der Zee E.A., Kas M.J.H. Default mode network dynamics: An integrated neurocircuitry perspective on social dysfunction in human brain disorders. Neurosci. Biobehav. Rev. 2024;164: 105839. doi 10.1016/j.neubiorev.2024.105839

25. 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

26. Savostyanov V.A., Makarova A.A. Reconstruction and analysis of the gene network for regulation of trait anxiety level in mice by means of ANDSystem software. In: IEEE 25th International Conference of Young Professionals in Electron Devices and Materials (EDM), Altai, Russian Federation, 2024;2340-2343. doi 10.1109/EDM61683.2024.10615053

27. Si Q., Tian J., Savostyanov V.A., Lebedkin D.A., Bocharov A.V., Savostyanov A.N. Comparison of brain activity indexes in the Chinese and Russian students under recognition of self- and other-related information. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2024;28(8):982-992. doi 10.18699/vjgb-24-105

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

29. Tsai A.C., Savostyanov A.N., Wu A., Evans J.P., Chien V.S.C., Yang H.-H., Yang D.-Y., Liou M. Recognizing syntactic errors in Chinese and English sentences: brain electrical activity in Asperger’s syndrome. Res. Autism Spectr. Disord. 2013;7(7):889-905. doi 10.1016/j.rasd.2013.02.001

30. Tseng Y.L., Yang H.H., Savostyanov A.N., Chien V.S., Liou M. Voluntary attention in Asperger’s syndrome: brain electrical oscillation and phase-synchronization during facial emotion recognition. Res. Autism Spectr. Disord. 2015;13-14:32-51. doi 10.1016/j.rasd.2015.01.003