Predicting personality traits from resting-state fMRI

Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 867 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the "Big Five", as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds such as age, sex, and IQ, we used a cross-validated framework, together with test-retest replication, to quantify how well the neuroimaging data could predict each of the five personality factors, as well as two superordinate factors ("α" and "β"). To obtain a more comprehensive set of findings, we tested three different preprocessing pipelines for the fMRI data, two brain parcellation schemes, and two different linear models for prediction. Across all 24 results (test/retest; 3 processing pipelines; 2 parcellation schemes; 2 models) we found no consistent evidence for predictability for any of the five personality traits with the exception of Openness, and the superordinate β factor ("personal growth/ plasticity"). As a benchmark, we showed that we could replicate prior reports of predicting IQ from the same dataset. Best predictions in all cases were around r=0.2, thus only accounting for about 4% of the variance. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.