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Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

Wan Li1, Chunlan Yang2, Feng Shi3

  • 1College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.

Brain Topography
|April 18, 2018
PubMed
Summary

Aging impacts brain networks, showing reduced connectivity in specific regions and altered hub function. Individual brain network analysis using IBASPM offers insights into normal aging brain changes.

Keywords:
Cortical brain networkGraph theoryIBASPMNormal agingRegional gray matter volume

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Aging Research

Background:

  • Normal aging is associated with cognitive decline, affecting memory and executive functions.
  • The cortical brain connectome is a key area for studying age-related brain alterations.
  • Individual atlas-based volume measurement tools like IBASPM aid in examining these changes.

Purpose of the Study:

  • To investigate age-related alterations in cortical brain networks using IBASPM-extracted regional gray matter volumes.
  • To identify specific changes in brain connectivity associated with normal aging.

Main Methods:

  • Utilized the IBASPM toolkit for individual atlas-based gray matter volume extraction.
  • Constructed cortical brain networks based on these regional volumes.
  • Analyzed network properties, including connection strength and hub distribution, in young and aging groups.

Main Results:

  • Identified reduced connection strength between the superior temporal pole and middle temporal pole in the right hemisphere.
  • Observed alterations in global hubs: left fusiform gyrus in the young and right Rolandic operculum in the aging group.
  • Found significantly reduced inter-module connection within a specific module in the aging group.

Conclusions:

  • The findings align with previous research on normal aging phenomena.
  • Brain networks constructed with IBASPM can provide supplementary information on age-related brain changes.
  • Emphasized the importance of individualizing morphometric feature extraction in future brain network research.