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Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after

Francisco J Román1, Yasser Iturria-Medina2, Kenia Martínez3

  • 1Universidad Autónoma de Madrid, Madrid, Spain; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, USA.

Neurobiology of Learning and Memory
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PubMed
Summary

Cognitive training enhanced brain connectivity in specific networks related to working memory and attention. This structural brain change, tracked using network-based statistics and graph theory, supports improved cognitive function.

Keywords:
Brain plasticityConnectomeGraph-theory indicesNetwork-based statistics (NBS)

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Brain Imaging

Background:

  • The brain's structural connectome reflects changes due to experience and training.
  • Understanding how cognitive training impacts brain structure is crucial for developing effective interventions.

Purpose of the Study:

  • To investigate structural brain changes associated with cognitive training using network-based statistics (NBS) and graph theory.
  • To analyze alterations in the brain's anatomical connectome following a working memory intervention.

Main Methods:

  • Diffusion-weighted imaging was used to reconstruct the anatomical connectome.
  • Network-based statistics (NBS) and graph theory indices (clustering, path length, efficiency, strength) were applied to analyze connectivity changes.
  • Cognitive function was assessed before and after the intervention in experimental and control groups.

Main Results:

  • The training group showed enhanced connectivity in a network supporting working memory, interference resolution, and inhibition.
  • Significant group differences in network strength and global efficiency were found in the NBS-derived sub-network.
  • Structural brain changes correlated with behavioral improvements in cognitive function.

Conclusions:

  • The connectome approach, utilizing NBS and graph theory, effectively tracks structural changes in specific brain sub-networks due to cognitive training.
  • These findings provide a quantitative understanding of the topological architecture of brain networks affected by cognitive interventions.