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Related Experiment Video

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Mapping individual structural covariance network in development brain with dynamic time warping.

Hui Sun1, Qinyao Sun2, Yuanyuan Li3,4

  • 1College of Electrical Engineering, Sichuan University, Chengdu 610065, China.

Cerebral Cortex (New York, N.Y. : 1991)
|February 11, 2024
PubMed
Summary

This study introduces a new method using dynamic time warping to map individual brain structural covariance networks. This approach reveals how brain network organization changes from childhood to adulthood, aiding future research on cognition and disease.

Keywords:
brain developmentdynamic time warpingindividualstructural covariance network

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Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Network Science

Background:

  • Brain development involves synchronized structural co-variation, but mapping individual networks remains challenging.
  • Understanding individual structural covariance networks is crucial for insights into brain maturation.

Purpose of the Study:

  • To develop and apply a novel method for mapping individual structural covariance networks.
  • To delineate the developmental trajectories of topological organization in these networks from childhood to early adulthood.

Main Methods:

  • Developed a novel individual structural covariance network method utilizing the dynamic time warping algorithm.
  • Applied the method to a large dataset (655 individuals) from the Human Connectome Project-Development.
  • Analyzed developmental changes in global and nodal network properties.

Main Results:

  • Individual structural covariance networks exhibit small-worldness.
  • Global network properties (small-worldness, efficiency, modularity) linearly increase with age; shortest path length decreases.
  • Nodal properties (betweenness, degree) show age-related increases in language/emotion areas and decreases in visual/sensorimotor/hippocampal regions.
  • Network topological attributes accurately predict individual age.

Conclusions:

  • Dynamic time warping effectively maps individual structural covariance networks, revealing developmental trajectories.
  • This method enhances understanding of brain network maturation and its links to cognition and disease vulnerability.