Association of autistic personality traits with the EEG scores in non-clinical subjects during the facial video viewing - A. N. Savostyanov; D. A. Kuleshov; D. I. Klemeshova; M. S. Vlasov; A. E. Saprygin (Вавиловский журнал генетики и селекции)
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.