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Estimation of Clean and Centered Brain Network Atlases using Diffusive-Shrinking Graphs with Application to

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Summary

This study introduces a new framework for creating a clean and well-centered brain network atlas from multiple subjects. The method improves the representation of average brain networks, aiding in the identification of abnormalities.

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Area of Science:

  • Neuroimaging
  • Network Science
  • Data Analysis

Background:

  • Existing methods for brain image normalization do not extend to deriving network atlases from complex brain network manifolds.
  • A reliable mean representation of population brain networks is crucial for identifying deviations from healthy atlases.

Purpose of the Study:

  • To propose a novel framework for estimating a clean and well-centered network atlas from a population of brain networks.
  • To enable the creation of a reliable mean brain network representation for detecting abnormalities.

Main Methods:

  • Constructing a tensor from individual subject brain network matrices.
  • Employing tensor robust principal component analysis for denoising and extracting a low-rank tensor.
  • Building a graph representation of networks and using non-linear diffusion for progressive centering.

Main Results:

  • The proposed framework produced more centered network atlases compared to baseline methods.
  • Further denoising enhanced the centeredness of the atlases, particularly for noisy functional connectivity networks.
  • Demonstrated effectiveness on developing functional and morphological brain networks across different ages.

Conclusions:

  • The novel framework effectively generates clean and well-centered brain network atlases.
  • The method offers a significant improvement for averaging brain networks and identifying deviations.
  • Applicable to various types of brain network data, including noisy functional connectivity networks.