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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm
Matteo Bastiani1, Nadim Jon Shah, Rainer Goebel
1Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands. matteo.bastiani@maastrichtuniversity.nl
Neuroimage
|June 16, 2012
Summary
Choosing diffusion MRI models and tractography methods significantly impacts human brain connectome network analysis. Understanding these effects is crucial for reliable structural network studies and valid comparisons.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Biophysics
Background:
- Reconstructing the human brain's structural connectome using Diffusion Weighted Imaging (DWI) is complex.
- The choice of modeling and algorithms significantly influences derived network properties.
Purpose of the Study:
- To investigate how intra-voxel fiber direction modeling and tractography algorithms affect structural network indices.
- To assess the impact of different algorithmic choices on network density, small-worldness, and global efficiency.
Main Methods:
- Compared single vs. multiple intra-voxel fiber directions.
- Evaluated deterministic vs. probabilistic tractography.
- Assessed local vs. global measure-of-fit for fiber trajectories.
- Utilized a connectome dissection quality control (QC) approach with Tract Specific Density Coefficients (TSDCs).
Main Results:
- Tractography choices substantially affect global network density, influencing graph indices.
- Significant (tens of percent) effects on graph indices persist even after controlling for global density.
- Algorithmic choices impact the sensitivity and specificity of reconstructed connections.
Conclusions:
- The selection of diffusion models and tractography methods has a profound impact on human connectome studies.
- Careful consideration of these methodological choices is essential for the sensitivity and validity of structural network research.
- Standardization and transparent reporting of methods are critical for comparing findings across studies.
