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Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
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Brain working memory network indices as landmarks of intelligence.

Mohammadreza Khodaei1, Paul J Laurienti1,2, Dale Dagenbach3

  • 1Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

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Summary

Higher intelligence is linked to more efficient brain network changes during working memory tasks. This study reveals how brain network properties like global efficiency and assortativity correlate with cognitive abilities.

Keywords:
Brain networkDegree differenceFluid and crystalized intelligenceGeneral intelligenceGlobal efficiencyIntelligenceLeverage centralityResting stateWorking memoryfMRI

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Area of Science:

  • Neuroscience
  • Network Science
  • Cognitive Neuroscience

Background:

  • Identifying neural correlates of intelligence is a key neuroscience goal.
  • Network neuroscience offers insights into brain function and behavior.
  • Previous studies often used univariate methods and focused on resting-state networks.

Purpose of the Study:

  • To investigate the association between network assortativity and intelligence.
  • To elucidate critical working memory task network topological properties related to intelligence differences.
  • To address limitations of previous network studies on intelligence.

Main Methods:

  • Employed a mixed-modeling framework for analyzing multi-task brain networks.
  • Utilized fMRI data from 379 subjects (Human Connectome Project) during resting state and a 2-back working memory task.
  • Extracted topological network features (global efficiency, degree, leverage centrality, modularity, clustering coefficient) and analyzed their relationship with intelligence scores.

Main Results:

  • General intelligence scores correlate with changes in network topological properties during working memory compared to resting state.
  • High intelligence groups showed a greater increase in positive association between global efficiency and connection strength from rest to working memory.
  • Increased negative association between degree difference and leverage centrality with connection strength was observed in high intelligence groups during working memory, suggesting higher network resilience and assortativity.

Conclusions:

  • Intelligence is significantly associated with hallmark properties of brain networks during working memory.
  • Brain network dynamics during cognitive tasks, particularly working memory, are crucial for understanding intelligence.
  • Findings suggest efficient information flow and network resilience contribute to higher intelligence.