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Related Experiment Video

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Linking Functional Connectivity and Structural Connectivity Quantitatively: A Comparison of Methods.

Haiqing Huang1, Mingzhou Ding1

  • 1J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida , Gainesville, Florida.

Brain Connectivity
|November 25, 2015
PubMed
Summary
This summary is machine-generated.

Linking brain structural and functional connectivity is challenging. This study found that edge weight better quantifies anatomical connections, and conditional Granger causality (CGC) better reflects functional interactions between brain regions.

Keywords:
central executive networkdefault mode networkdiffusion-weighted imagingfunctional MRIfunctional connectivitystructural connectivity

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Area of Science:

  • Neuroscience
  • Brain Connectivity
  • Diffusion MRI
  • Resting-state fMRI

Background:

  • Structural connectivity forms the basis of brain functional connectivity.
  • Quantitatively linking these two types of connectivity remains a significant challenge in neuroscience.
  • Existing methods for quantifying structural (e.g., fractional anisotropy, edge weight) and functional (e.g., cross-correlation, Granger causality) connectivity have limitations.

Purpose of the Study:

  • To quantitatively link structural and functional connectivity between specific brain regions.
  • To evaluate the efficacy of different connectivity measures in representing the relationship between anatomical and functional brain networks.
  • To identify optimal metrics for assessing brain connectivity.

Main Methods:

  • Utilized diffusion-weighted imaging (DWI) and resting-state functional magnetic resonance imaging (rs-fMRI) data.
  • Selected two pairs of regions of interest (ROIs): posterior cingulate cortex and medial prefrontal cortex (Default Mode Network), and left dorsal lateral prefrontal cortex and left inferior parietal lobule (Central Executive Network).
  • Quantified structural connectivity using mean fractional anisotropy (FA) and edge weight; functional connectivity using zero-lag cross-correlation, partial correlation, and pairwise/conditional Granger causality (CGC).

Main Results:

  • Zero-lag cross-correlation, partial correlation, and pairwise Granger causality showed no significant correlation with mean FA or edge weight.
  • Conditional Granger causality (CGC) demonstrated a significant correlation with edge weight, but not with mean FA.
  • These findings suggest differential suitability of various metrics for representing brain connectivity.

Conclusions:

  • Edge weight may be a more appropriate measure for quantifying the strength of anatomical connections between ROIs.
  • Conditional Granger causality (CGC), by accounting for common input and indirect influences, appears to be a more suitable metric for quantifying functional interactions mediated by structural pathways.
  • This study provides insights into selecting appropriate measures for linking structural and functional brain networks.