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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Structural Brain Network Changes across the Adult Lifespan.

Ke Liu1, Shixiu Yao1, Kewei Chen2,3,4

  • 1College of Information Science and Technology, Beijing Normal UniversityBeijing, China.

Frontiers in Aging Neuroscience
|September 2, 2017
PubMed
Summary

Brain structural networks show significant age-related changes across the adult lifespan. Most networks, including attention and default mode networks, decline linearly with age, with the hippocampus showing the steepest decline.

Keywords:
age-related changesgray matter volumeindependent component analysismagnetic resonance imagingstructural network

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Aging Research

Background:

  • Magnetic resonance imaging (MRI) studies indicate age-related brain structural network alterations.
  • Specific age-associated changes in brain structural networks across the entire adult lifespan remain underexplored.

Purpose of the Study:

  • To identify structural brain networks using covariant gray matter volume.
  • To investigate age-related trajectories of these networks across the adult lifespan.

Main Methods:

  • Multivariate independent component analysis (ICA) was employed.
  • Structural networks were identified based on covariant gray matter volume in 536 healthy adults (aged 20-86).
  • Age-related trajectories of network components were analyzed.

Main Results:

  • Sixteen of twenty independent components (ICs) showed significant age-related changes.
  • Most trajectories demonstrated a linear decline with age.
  • Key affected networks included dorsal attention, default mode, auditory, cerebellum, and hippocampus networks, with the hippocampus showing the most significant decrease.

Conclusions:

  • Structural brain networks undergo significant age-related changes throughout adulthood.
  • Linear decline is a predominant pattern, particularly in attention, default mode, and memory-related networks.
  • Findings offer insights into normal brain aging and a basis for understanding abnormal aging patterns.