Back

Cortical travelling waves may underpin variation in personality traits

Bailey, N. W.; Bonfim Pacheco, L.; Smillie, L. D.

2025-01-16 neuroscience
10.1101/2025.01.15.633292 bioRxiv
Show abstract

ObjectivesPersonality traits must relate to stable neural processes, yet few robust neural correlates of personality have been discovered. Recent methodological advances enable measurement of cortical travelling waves, which likely underpin information flow between brain regions. Here, we explore whether cortical travelling waves relate to personality traits from the "Big Five" taxonomy. MethodWe assessed personality traits and recorded resting electroencephalography (EEG) from 300 participants. We computed travelling wave strength using a 3D fast Fourier transform and explored relationships between alpha travelling waves and personality traits. ResultsTrait Agreeableness and Openness/Intellect had significant relationships to travelling waves that passed multiple-comparison controls (pFDR = 0.019, pFDR = 0.036). Agreeableness related to interhemispheric waves travelling from the right hemisphere along central lines (rho = 0.263, p < 0.001, BF10 = 356.350). This relationship was unique to the compassion aspect (t = 3.719, p <0.001) rather than politeness aspect of Agreeableness (t = 0.897, p = 0.370). Openness/Intellect related to backwards travelling waves along midline electrodes (rho = 0.197, p < 0.001, BF10 = 13.800), which was confirmed for the Openness aspect (rho = 0.216, p < 0.001, BF10 = 26.444) but not the Intellect aspect (rho = 0.093, p = 0.109, BF10 = 0.344). ConclusionsGreater cortical travelling wave strength from right temporal regions may partly underpin variation in trait compassion, and backwards travelling wave strength along midline electrodes may mark trait openness. Further research is needed to investigate the mechanistic role of travelling waves in personality traits and other individual differences.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.