Journal Home > Volume 9 , Issue 4
Background:

The personality-brain association mechanism has been a topic of interest in the field of neuroscience. Usually, the previous research strategy was to first group the population based on different personality traits, and then explore the brain mechanisms corresponding to different personality groups. At present, a "brain-first" research strategy, which uses data-driven approaches instead of personality traits to first group the population, has been adopted to further enhance study objectivity.

Methods:

Here, we used a data-driven approach following the "brain-first" research strategy to deeply mine the resting-state brain functional magnetic resonance imaging data of 119 healthy participants, classified subjects into different groups based on brain image characteristics, and used the Sixteen Personality Factor Questionnaire to explain the variabilities of resting-state brain characteristics between different groups.

Results:

We have identified 3 personality–brain connections, including the privateness–left frontoparietal network, liveliness–sensory–motor network, and vigilance–sensory–motor network.

Conclusion:

We conclude that the above-mentioned three personality factors are based on brain neural activity, independent of the subjective experience of the personality scale creator, and have stronger explanatory power of brain imaging features.


menu
Abstract
Full text
Outline
About this article

Personality–brain connection: Based on resting-state functional magnetic resonance imaging data-driven exploration

Show Author's information Hong Li1,2,3,§( )Junjie Wang4,5,§
Department of Mental Health, Shanxi Medical University, Taiyuan 030001, Shanxi, China
Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan 030012, Shanxi, China
Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi, China
School of Psychology, Capital Normal University, Beijing 100048, China
Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China

§ These authors contributed equally to this work.

Abstract

Background:

The personality-brain association mechanism has been a topic of interest in the field of neuroscience. Usually, the previous research strategy was to first group the population based on different personality traits, and then explore the brain mechanisms corresponding to different personality groups. At present, a "brain-first" research strategy, which uses data-driven approaches instead of personality traits to first group the population, has been adopted to further enhance study objectivity.

Methods:

Here, we used a data-driven approach following the "brain-first" research strategy to deeply mine the resting-state brain functional magnetic resonance imaging data of 119 healthy participants, classified subjects into different groups based on brain image characteristics, and used the Sixteen Personality Factor Questionnaire to explain the variabilities of resting-state brain characteristics between different groups.

Results:

We have identified 3 personality–brain connections, including the privateness–left frontoparietal network, liveliness–sensory–motor network, and vigilance–sensory–motor network.

Conclusion:

We conclude that the above-mentioned three personality factors are based on brain neural activity, independent of the subjective experience of the personality scale creator, and have stronger explanatory power of brain imaging features.

Keywords: data-driven, resting-state fMRI, personality traits, left frontoparietal network, sensory–motor network

References(44)

[1]
Wang JJ, Hu Y, Li H, et al. Connecting openness and the resting-state brain network: a discover-validate approach. Front Neurosci 2018, 12: 762.
[2]
Canli T, Amin Z. Neuroimaging of emotion and personality: scientific evidence and ethical considerations. Brain Cogn 2002, 50(3): 414–431.
[3]
DeYoung CG, Gray JR. Personality neuroscience: Explaining individual differences in affect, behaviour and cognition. In The Cambridge Handbook of Personality Psychology. Corr PJ, Matthews G., Eds. Cambridge: Cambridge University Press, 2012, pp 323–346.
DOI
[4]
Diethelm O, Simons DJ. Electroencephalographic findings in psychopathic personalities. J Nerv Ment Dis 1945, 102: 611–614.
[5]
Schmidtke JI, Heller W. Personality, affect and EEG: predicting patterns of regional brain activity related to extraversion and neuroticism. Pers Individ Differ 2004, 36(3): 717–732.
[6]
Stough C, Donaldson C, Scarlata B, et al. Psychophysiological correlates of the NEO PI-R openness, agreeableness and conscientiousness: preliminary results. Int J Psychophysiol 2001, 41(1): 87–91.
[7]
Johnson DL, Wiebe JS, Gold SM, et al. Cerebral blood flow and personality: a positron emission tomography study. Am J Psychiatry 1999, 156(2): 252–257.
[8]
O'Gorman RL, Kumari V, Williams SCR, et al. Personality factors correlate with regional cerebral perfusion. NeuroImage 2006, 31(2): 489–495.
[9]
Ryan JP, Sheu LK, Gianaros PJ. Resting state functional connectivity within the cingulate cortex jointly predicts agreeableness and stressor-evoked cardiovascular reactivity. NeuroImage 2011, 55(1): 363–370.
[10]
Wang T, Chen ZC, Zhao G, et al. Linking inter-individual differences in the conflict adaptation effect to spontaneous brain activity. NeuroImage 2014, 90: 146–152.
[11]
Wei LQ, Duan XJ, Zheng CY, et al. Specific frequency bands of amplitude low-frequency oscillation encodes personality. Hum Brain Mapp 2014, 35(1): 331–339.
[12]
Cohn MD, Pape LE, Schmaal L, et al. Differential relations between juvenile psychopathic traits and resting state network connectivity. Hum Brain Mapp 2015, 36(6): 2396–2405.
[13]
Kunisato Y, Okamoto Y, Okada G, et al. Personality traits and the amplitude of spontaneous low-frequency oscillations during resting state. Neurosci Lett 2011, 492(2): 109–113.
[14]
Wei LQ, Duan XJ, Yang Y, et al. The synchronization of spontaneous BOLD activity predicts extraversion and neuroticism. Brain Res 2011, 1419: 68–75.
[15]
Liu WY, Weber B, Reuter M, et al. The Big Five of Personality and structural imaging revisited: a VBM -DARTEL study. Neuroreport 2013, 24(7): 375–380.
[16]
Yang Z, LaConte S, Weng XC, et al. Ranking and averaging independent component analysis by reproducibility (RAICAR). Hum Brain Mapp 2008, 29(6): 711–725.
[17]
Yang Z, Zuo XN, Wang PP, et al. Generalized RAICAR: discover homogeneous subject (sub) groups by reproducibility of their intrinsic connectivity networks. NeuroImage 2012, 63(1): 403–414.
[18]
Yang Z, Chang CT, Xu T, et al. Connectivity trajectory across lifespan differentiates the precuneus from the default network. NeuroImage 2014, 89: 45–56.
[19]
Tian F, Wang JJ, Xu C, et al. Focusing on the differences of resting-state brain networks, using a data-driven approach to explore the functional neuroimaging characteristics of extraversion trait. Front Neurosci 2018, 12: 109.
[20]
Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 1971, 9(1): 97–113.
[21]
Shephard DA. The 1975 declaration of Helsinki and consent. Can Med Assoc J 1976, 115(12): 1191–1192.
[22]
The description and measurement of personality. Ment Health: Lond 1947, 7(2): 51–52.
[23]
Sells SB, Cattell RB. Personality and motivation structure and measurement. Am J Psychol 1958, 71(3): 620.
[24]
Zuo XN, Xu T, Jiang LL, et al. Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. NeuroImage 2013, 65: 374–386.
[25]
Cox RW. AFNI: what a long strange trip it’s been. NeuroImage 2012, 62(2): 743–747.
[26]
Jenkinson M, Beckmann CF, Behrens TEJ, et al. Fsl. NeuroImage 2012, 62(2): 782–790.
[27]
Fischl B. FreeSurfer. NeuroImage 2012, 62(2): 774–781.
[28]
Zuo XN, Xing XX. Effects of non-local diffusion on structural MRI preprocessing and default network mapping: statistical comparisons with isotropic/anisotropic diffusion. PLoS One 2011, 6(10): e26703.
[29]
Avants B, Gee JC. Geodesic estimation for large deformation anatomical shape averaging and interpolation. NeuroImage 2004, 23(Suppl 1): S139–S150.
[30]
Yang Z, Xu Y, Xu T, et al. Brain network informed subject community detection in early-onset schizophrenia. Sci Rep 2014, 4: 5549.
[31]
Damoiseaux JS, Beckmann CF, Sanz Arigita EJ, et al. Reduced resting-state brain activity in the “default network” in normal aging. Cereb Cortex 2008, 18(8): 1856–1864.
[32]
Beckmann CF, DeLuca M, Devlin JT, et al. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 2005, 360(1457): 1001–1013.
[33]
Damoiseaux JS, Rombouts SARB, Barkhof F, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA 2006, 103(37): 13848–13853.
[34]
Goel V, Dolan RJ. Explaining modulation of reasoning by belief. Cognition 2003, 87(1): B11–B22.
[35]
Goel V, Buchel C, Frith C, et al. Dissociation of mechanisms underlying syllogistic reasoning. NeuroImage 2000, 12(5): 504–514.
[36]
Noveck IA, Goel V, Smith KW. The neural basis of conditional reasoning with arbitrary content. Cortex 2004, 40(4/5): 613–622.
[37]
Langdon D, Warrington EK. The role of the left hemisphere in verbal and spatial reasoning tasks. Cortex 2000, 36(5): 691–702.
[38]
Biswal B, Yetkin FZ, Haughton VM, et al. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995, 34(4): 537–541.
[39]
Sugiura M, Kawashima R, Nakagawa M, et al. Correlation between human personality and neural activity in cerebral cortex. NeuroImage 2000, 11(5 Pt 1): 541–546.
[40]
Cloninger CR. A systematic method for clinical description and classification of personality variants. A proposal. Arch Gen Psychiatry 1987, 44(6): 573–588.
[41]
Cloninger CR, Svrakic DM, Przybeck TR. A psychobiological model of temperament and character. Arch Gen Psychiatry 1993, 50(12): 975–990.
[42]
Goldin PR, Manber T, Hakimi S, et al. Neural bases of social anxiety disorder: emotional reactivity and cognitive regulation during social and physical threat. Arch Gen Psychiatry 2009, 66(2): 170–180.
[43]
Liao W, Chen HF, Feng Y, et al. Selective aberrant functional connectivity of resting state networks in social anxiety disorder. NeuroImage 2010, 52(4): 1549–1558.
[44]
Kilts CD, Kelsey JE, Knight B, et al. The neural correlates of social anxiety disorder and response to pharmacotherapy. Neuropsychopharmacology 2006, 31(10): 2243–2253.
Publication history
Copyright
Rights and permissions

Publication history

Received: 25 April 2023
Revised: 24 June 2023
Accepted: 26 June 2023
Published: 05 December 2023
Issue date: December 2023

Copyright

© The authors 2023.

Rights and permissions

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Return