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Predicting personality from network-based resting-state functional connectivity.

Alessandra D Nostro1,2,3, Veronika I Müller4,5,6, Deepthi P Varikuti4,5,6

  • 1Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstraße 1, 40225, Düsseldorf, Germany. nostro@uni-duesseldorf.de.

Brain Structure & Function
|March 25, 2018
PubMed
Summary
This summary is machine-generated.

Brain connectivity patterns predict personality traits like Extraversion and Neuroticism. However, these neurobiology of personality findings are mostly gender-specific, highlighting the importance of considering sex differences.

Keywords:
Functional networksGender differencesHormonal influenceMachine learningNEO-FFIResting-state functional connectivity

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Area of Science:

  • Neuroscience
  • Psychology
  • Behavioral Science

Background:

  • Personality variations are linked to diverse mental functions, including affective, social, executive, and memory systems.
  • Resting-state functional connectivity (RSFC) reflects the intrinsic dynamics of neural networks supporting these mental functions.

Purpose of the Study:

  • To investigate if functional network connectivity can predict individual scores of the five-factor personality model.
  • To explore potential gender differences in the relationship between functional brain networks and personality traits.

Main Methods:

  • Assessed nine meta-analytically derived functional networks (social, affective, executive, mnemonic) in two independent samples.
  • Computed RSFC in a combined sample of 365 males and 365 females.
  • Utilized relevance vector machine for personality score prediction and cross-validation accuracy assessment.

Main Results:

  • RSFC within social, affective, mnemonic, and executive networks significantly predicted Extraversion, Neuroticism, Agreeableness, and Openness.
  • Most RSFC-personality associations were gender-specific, predicting traits only in males or females.
  • Cross-validation accuracy confirmed the predictive power of RSFC patterns for personality traits.

Conclusions:

  • Patterns of RSFC in specific functional brain networks can predict personality traits, offering neurobiological insights.
  • The relationship between RSFC and personality is significantly influenced by gender, necessitating gender-specific analyses.