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Trait Centrality01:21

Trait Centrality

Trait centrality refers to the degree to which a particular characteristic influences the overall impression of an individual. Some traits exert a disproportionately strong impact on perception, shaping how people interpret other attributes of a person. Solomon Asch first systematically studied this phenomenon in 1946.Asch’s Experiment on Trait CentralityAsch's seminal study demonstrated the centrality of certain traits through a controlled experiment. Participants were presented with a list of...
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Related Experiment Video

Updated: May 28, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Network centrality in the human functional connectome.

Xi-Nian Zuo1, Ross Ehmke, Maarten Mennes

  • 1Laboratory for Functional Connectome and Development, Key Laboratory of Behavioural Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.

Cerebral Cortex (New York, N.Y. : 1991)
|October 5, 2011
PubMed
Summary
This summary is machine-generated.

Brain connectivity network architecture is better understood using centrality measures. Local brain connectivity decreases with age, but global connections remain stable, offering insights into aging and brain disorders.

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Area of Science:

  • Neuroscience
  • Network Science
  • Human Brain Imaging

Background:

  • The voxel-level network architecture of human brain functional connectivity remains poorly understood.
  • Investigating the functional connectome requires understanding network properties at a granular level.

Purpose of the Study:

  • To investigate whole-brain functional network connectivity using various network centrality measures.
  • To provide novel insights into the functional connectome's architecture at the voxel level.

Main Methods:

  • Utilized resting-state functional magnetic resonance imaging (fMRI) data from 1003 healthy adults.
  • Assembled and visualized the voxel-wise functional connectome.
  • Applied and analyzed a broad array of network centrality measures.

Main Results:

  • Demonstrated that different centrality measures capture distinct aspects of functional connectivity.
  • Revealed age-related decreases in degree centrality but not eigenvector centrality in specific brain regions (precuneus, posterior cingulate).
  • Showcased the reliability and phenotypic correlates (age, sex) of centrality measures.

Conclusions:

  • Centrality measures are non-redundant and offer complementary insights into brain network organization.
  • Local connectivity may decline with age, while global connectivity to hubs remains stable.
  • Findings raise questions about physiological mechanisms underlying these changes, relevant to neurodegenerative and psychiatric disorders.