Assessing the dependence of brain activity on individual single-nucleotide variability of genetic markers of major depressive disorder using principal component analysis.

Major depressive disorder (MDD) is one of the most widespread mental illnesses, which necessitates the search for factors of increased predisposition to this disorder. Single nucleotide polymorphisms in genes of the brain's neurotransmitter systems are often considered as molecular genetic markers of MDD. Indicators of individual single nucleotide variability in neurotransmitter genes are used to assess the risk of MDD before its symptomatology at the behavioral level. However, the predictive capabilities of analyzing genomic variations to assess the risk of depression are not yet sufficiently reliable and are complemented by behavioral and neurophysiological information about patients. Neurophysiological markers of MDD provide the most reliable estimates of the severity of pathological symptoms, but they reflect a person's state at the time of examination, and not a predisposition to the occurrence of this pathological state and do not allow assessing the risk of its appearance in the future. Major depressive disorder is often accompanied by abnormalities in a person's ability to control motor responses, including the ability to voluntary suppress inappropriate behavior. The "stop-signal paradigm" (SSP) is an experimental method for assessing the functional balance between the inhibitory and activation systems of the brain during targeted movements. Combined with EEG recording, this experimental method allows for the consideration of not only participants' behavioral characteristics, such as speed or accuracy of responses, but also the brain's neurophysiological features associated with behavior control. The objective of this study was to evaluate the relationship between EEG responses in the stop-signal paradigm and individual single nucleotide variability in candidate genes for MDD detection. Dimensionality in the original genetic and neurophysiological experimental data was reduced by principal component analysis (PCA) to subsequently detect an association between EEG response components recorded during the control of random motor responses and single nucleotide variations in genes, the variability of which is asso-ciated with MDD risk. Variability in these genes has been shown to be associated with the amplitude of brain responses under the conditions of test subjects using the PCA method. The results obtained can be used to develop systems for the early diagnosis of depression, identify individual patterns of impairment in the brain, select methods for correcting the disease and control the effectiveness of therapy.