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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Threshold Selection for Brain Connectomes.

Nicholas Theis1, Jonathan Rubin2, Joshua Cape3,4

  • 1Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.

Brain Connectivity
|May 11, 2023
PubMed
Summary
This summary is machine-generated.

We introduce a novel objective function thresholding method for brain connectomes derived from magnetic resonance imaging (MRI). This method effectively reduces noise and preserves essential network structures for better data interpretation.

Keywords:
biomedical imaginggraph theorymagnetic resonance imagingnetwork thresholdingnetworks

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Area of Science:

  • Neuroscience
  • Network Science
  • Data Analysis

Background:

  • Brain connectomes, derived from magnetic resonance imaging (MRI), capture macroscale structural and functional data.
  • Noise in connectomes can obscure structure-function relationships, hindering data interpretation.
  • Current thresholding methods for reducing network density lack consensus and can yield inconsistent results.

Purpose of the Study:

  • To compare existing brain connectome thresholding methods.
  • To introduce and evaluate a novel "objective function" thresholding method.
  • To improve the reliability of functional connectivity data analysis.

Main Methods:

  • Assessed thresholding performance using normalized mutual information (NMI) to compare community structure between original and perturbed networks.
  • Evaluated density and clustering coefficient (CC) of baseline versus thresholded networks.
  • Applied and validated methods on simulated and empirical functional connectivity data.

Main Results:

  • The proposed objective function-based thresholding demonstrated superior performance in maintaining similarity between original and thresholded networks.
  • NMI and CC analyses confirmed the effectiveness of objective function thresholding on simulated functional networks.
  • Objective function thresholding preserved community structure and clustering features of the original network.

Conclusions:

  • Objective function thresholding offers a robust approach to reducing network density in functional connectivity data.
  • This method enhances the preservation of essential network properties compared to existing techniques.
  • It provides a reliable strategy for improving the interpretation of brain connectome data.