Identifying the Role of Genetic (Hereditary) and Neurological Factors (Brain Waves) in Predicting Anger Reactions and Angry Temperament in Adults
Authors
- Pooran Tavakkoli Targhi Department of Psychology, Refah University, Tehran, Iran.
-
Salehe Piryaei
Associate professor, Department of Educational Psychology, Alzahra University, Tehran, Iran.
https://orcid.org/0000-0003-3615-8937
DOI:
https://doi.org/10.22100/ijhs.v10i4.1156Keywords:
Biomarker, Neurological Marker, Aggression, AdultsAbstract
Background: Aggression is undoubtedly influenced by genetics. In this study, the primary goal was to explore how brain waves can predict aggressive behavior in adults.
Methods: The research is a correlation study that investigates aggression by analyzing biological, neurological, and behavioral markers. The study involved a sample of 100 individuals in Tehran, ranging in age from 18 to 22, who were purposefully chosen between 2022 and 2023. The method used to collect data in this study involved using A genome-wide association study (GWA study, or GWAS), quantitative electroencephalography (QEEG), and the Spielberger State-Trait Anger Inventory (SWAS) Form 2. The data was examined through descriptive statistics, inferential statistics, and regression analysis utilizing the SPSS 26 software.
Results: The study found a strong link between genetic predisposition for anger reactions and moods. Genetic predisposition for anger reactions also showed a connection to environmental factors influencing angry moods. Angry reactions predicted 12% of changes in angry moods. However, no significant correlation was found between alpha waves in the brain and angry responses or moods. Similarly, there was no significant correlation between biomarkers, delta waves, and alpha waves in different brain regions. Lastly, a positive relationship was observed between genetic predisposition for angry reactions and beta waves in specific brain regions.
Conclusions: The findings suggest that biological and neurological markers can be valuable for predicting adult aggressive behaviors. Utilizing comprehensive prediction models that take these markers into account can help identify and support individuals at risk of aggression. This research can also guide the development of educational and therapeutic programs to reduce aggression and support mental well-being.
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