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Related Experiment Video

Updated: May 13, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Extraversion is encoded by scale-free dynamics of default mode network.

Xu Lei¹, Zhiying Zhao, Hong Chen

¹Key Laboratory of Cognition and Personality (Ministry of Education), Chongqing 400715, China. xlei@swu.edu.cn

|March 5, 2013

Summary

Personality traits are linked to brain activity patterns. Specifically, longer memory in the default mode network (DMN) detected by resting-state fMRI correlates with lower extraversion in healthy individuals.

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Area of Science:

Neuroscience
Cognitive Neuroscience
Psychology

Background:

Resting-state functional Magnetic Resonance Imaging (rsfMRI) is crucial for studying neurological and psychiatric conditions.
Emerging evidence links brain activity scaling properties to personality traits.
The relationship between personality and the scale-free dynamics of the default mode network (DMN) remains unexplored.

Purpose of the Study:

To investigate the association between personality traits and the scale-free dynamics of the default mode network (DMN).

Main Methods:

rsfMRI data from 20 healthy individuals were analyzed.
The Hurst exponent, a measure of time-series long memory, was estimated within the DMN.
DMN data were extracted using both independent component analysis (ICA) and region of interest (ROI) methods.

Main Results:

A significant association was found between the Hurst exponent in the DMN and extraversion scores from the Eysenck Personality Questionnaire.
Specifically, longer memory (higher Hurst exponent) in the DMN was correlated with lower extraversion.
These findings held true regardless of whether ICA or ROI methods were used to define the DMN.

Conclusions:

Individual differences in personality are associated with the scaling dynamics of the DMN.
This association may be related to the efficiency of online information processing.
Personality traits might be reflected in the scaling properties of resting-state brain networks.