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The backbone network of dynamic functional connectivity.

Nima Asadi1, Ingrid R Olson2, Zoran Obradovic1

  • 1Department of Computer and Information Sciences, College of Science and Technology, Temple University, Philadelphia, PA, USA.

Network Neuroscience (Cambridge, Mass.)
|January 13, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data-driven method to identify essential connections in dynamic brain functional connectivity. The approach filters out random links, revealing true network structures in resting-state fMRI data.

Keywords:
Autism spectrum disorderBackbone networkDynamic functional connectivityNull modelOptimization

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Area of Science:

  • Neuroscience
  • Network Science
  • Data Science

Background:

  • Temporal networks are crucial for analyzing brain functional connectivity.
  • Identifying spurious links (noise confounds) in these networks is a significant challenge.
  • Existing methods are limited for dynamic networks with continuous weights.

Purpose of the Study:

  • To develop a data-driven procedure for uncovering irreducible ties in dynamic functional connectivity.
  • To differentiate essential temporal links from those arising by chance from local node properties.
  • To apply this framework to resting-state fMRI data.

Main Methods:

  • A null model estimates latent characteristics of temporal link distributions via optimization.
  • A statistical test filters links reducible to local node activities and properties.
  • The framework is applied to resting-state fMRI data with continuous weights.

Main Results:

  • Successfully identified irreducible ties in dynamic functional connectivity.
  • Demonstrated the framework's effectiveness on a real-world resting-state fMRI dataset.
  • Provided insights into the advantages and aspects of the proposed approach.

Conclusions:

  • The proposed data-driven procedure effectively reveals significant temporal links in brain functional connectivity.
  • This method enhances the analysis of dynamic networks, particularly in neuroimaging.
  • Offers a robust approach to filter noise and understand true network dynamics.