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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Cross-paradigm connectivity: reliability, stability, and utility.

Hengyi Cao1, Oliver Y Chen2, Sarah C McEwen3,4

  • 1Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT, USA. hengyi.cao@yale.edu.

Brain Imaging and Behavior
|May 4, 2020
PubMed
Summary
This summary is machine-generated.

Cross-paradigm connectivity (CPC) analysis reveals a stable, individual brain "trait" architecture. This method enhances reliability and identification accuracy compared to single-paradigm approaches, offering insights into intrinsic brain networks.

Keywords:
Cross-paradigm connectivityFunctional connectomeIndividual identifiabilityReliabilityStability

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Area of Science:

  • Neuroscience
  • Functional Neuroimaging
  • Network Science

Background:

  • Functional neuroimaging typically examines network connectivity within specific paradigms.
  • The brain possesses an intrinsic, paradigm-independent "trait" architecture.
  • Cross-paradigm connectivity (CPC) was previously proposed to quantify shared connectivity patterns.

Purpose of the Study:

  • To examine the reliability, stability, and individual identifiability of CPC.
  • To assess CPC using generalizability theory and connectome fingerprinting.
  • To investigate the brain's intrinsic "trait" structure using multi-paradigm data.

Main Methods:

  • Utilized fMRI scans from healthy subjects across multiple sites and days.
  • Collected data using a battery of five paradigms.
  • Employed generalizability theory and connectome fingerprinting for analysis.

Main Results:

  • CPC matrices demonstrated higher reliability in connectivity diversity and individual identification accuracy compared to single-paradigm matrices.
  • CPC showed higher stability but lower reliability in connectivity strength.
  • Inclusion of more paradigms in CPC analysis improved all assessed metrics.

Conclusions:

  • Multi-paradigm CPC matrices reflect the brain's "trait" structure more effectively than single-paradigm data.
  • CPC analysis offers enhanced reliability, stability, and individual identifiability for functional brain networks.
  • Results support the feasibility and utility of CPC for studying intrinsic brain networks.