Predicting individual personality styles using macrostructural information: A multivariate pattern study

Advances in neuroimaging techniques have uncovered the relationships between neuroanatomy and personality traits like neuroticism and extraversion. Nevertheless, few studies have examined the reliable biological markers that identify individuals with different personality styles. Therefore, we evaluated the diagnostic capability of structural magnetic resonance imaging with a sample of 192 healthy adults by using a machine learning algorithm to divide them into four groups according to brain structural characteristics. Our results showed that personality styles, but not personality traits, could be discriminated with the extraversion candidate brain system (e. g., the frontal cortex, hippocampus, amygdala) and the neuroticism candidate brain system (e. g., the insula, the right anterior cingulate cortex). These findings indicate that personality styles that consider person-centered traits may more effectively predict individuals' mental health, providing a potential brain biomarker for early individual psychological risk assessments.