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Resting-state functional brain connectivity best predicts the personality dimension of openness to experience

Dubois, Julien and Galdi, Paola and Han, Yanting and Paul, Lynn K. and Adolphs, Ralph (2018) Resting-state functional brain connectivity best predicts the personality dimension of openness to experience. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20180620-141439729

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Abstract

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 884 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, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict openness to experience (NEOFAC_O: r=0.24, R^2 =0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R^2 =0.044). Other factors (extraversion, neuroticism, agreeableness and conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors ("α" and "β") from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=0.27, R^2 =0.050), which loads highly on openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/215129DOIDiscussion Paper
ORCID:
AuthorORCID
Dubois, Julien0000-0002-3029-173X
Paul, Lynn K.0000-0002-3128-8313
Adolphs, Ralph0000-0002-8053-9692
Additional Information:The copyright holder for this preprint is the author/funder. This work was supported by NIMH grant 2P50MH094258 (RA), the Carver Mead Seed Fund (RA), and a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation (JD). Author contributions: J. Dubois and P. Galdi developed the overall general analysis framework and conducted some of the initial analyses for the paper. J. Dubois conducted all final analyses and produced all figures. Y. Han helped with literature search and analysis of behavioral data. L. Paul helped with literature search, analysis of behavioral data, and interpretation of the results. J. Dubois and R. Adolphs wrote the initial manuscript and all authors contributed to the final manuscript. All authors contributed to planning and discussion on this project. The authors declare no conflict of interest. Data Sharing: The Young Adult HCP dataset is publicly available at https://www.humanconnectome.org/study/hcp-young-adult. Analysis scripts are available in the following public repository: https://github.com/adolphslab/HCP_MRI-behavior.
Group:Tianqiao and Chrissy Chen Institute for Neuroscience
Funders:
Funding AgencyGrant Number
NIH2P50MH094258
Carver Mead Seed FundUNSPECIFIED
Brain and Behavior Research FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20180620-141439729
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180620-141439729
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87271
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:20 Jun 2018 21:47
Last Modified:20 Jun 2018 22:02

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