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Brain Structure and Function

, Volume 223, Issue 6, pp 2699–2719 | Cite as

Predicting personality from network-based resting-state functional connectivity

  • Alessandra D. Nostro
  • Veronika I. Müller
  • Deepthi P. Varikuti
  • Rachel N. Pläschke
  • Felix Hoffstaedter
  • Robert Langner
  • Kaustubh R. Patil
  • Simon B. Eickhoff
Original Article

Abstract

Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC–personality relations should not be considered independently of gender.

Keywords

Functional networks Gender differences Hormonal influence Machine learning NEO-FFI Resting-state functional connectivity 

Notes

Acknowledgements

This study was supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/4-1, LA 3071/3-1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme “Supercomputing and Modelling for the Human Brain”, and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 7202070 (HBP SGA1).

Compliance with ethical standards

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

429_2018_1651_MOESM1_ESM.docx (5.9 mb)
Supplementary material 1 (DOCX 6044 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Alessandra D. Nostro
    • 1
    • 2
    • 3
  • Veronika I. Müller
    • 1
    • 2
    • 3
  • Deepthi P. Varikuti
    • 1
    • 2
    • 3
  • Rachel N. Pläschke
    • 1
    • 2
    • 3
  • Felix Hoffstaedter
    • 2
    • 3
  • Robert Langner
    • 1
    • 2
    • 3
  • Kaustubh R. Patil
    • 1
    • 3
  • Simon B. Eickhoff
    • 1
    • 2
    • 3
  1. 1.Institute of Systems Neuroscience, Medical FacultyHeinrich-Heine University DüsseldorfDüsseldorfGermany
  2. 2.Institute of Clinical Neuroscience and Medical PsychologyHeinrich-Heine University DüsseldorfDüsseldorfGermany
  3. 3.Institute of Neuroscience and Medicine (INM-1,7)Research Centre JülichJülichGermany

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