Related Experiment Video
Updated: Jun 9, 2025
17:06
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
26.2K
Spherical-deconvolution informed filtering of tractograms changes laterality of structural connectome
Yifei He1, Yoonmi Hong2, Ye Wu1
1School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China.
Neuroimage
|October 30, 2024
Summary
Tractography filtering significantly impacts brain connectivity laterality, especially with probabilistic tracking. Understanding these changes is crucial for accurate brain asymmetry studies.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Brain Connectivity
Background:
- Diffusion MRI tractography visualizes brain connections for lateralization studies.
- Tractography filtering methods aim to improve anatomical accuracy by reducing false positives.
- The effect of filtering on brain connectome lateralization is not well understood.
Purpose of the Study:
- To investigate the relationship between fiber filtering and changes in brain structural connectivity laterality.
- To assess how different tracking algorithms and filtering methods influence laterality indices.
Main Methods:
- Constructed raw tractography using three tracking algorithms.
- Applied SIFT and SIFT2 filtering methods across various parameters.
- Computed laterality indices for microstructural (AD, FA, RD, T1/T2) and structural (fiber length, connectivity) features.
Main Results:
- Tractography filtering can alter laterality in over 10% of connections (up to 25% for probabilistic tracking).
- Deterministic tracking showed minimal laterality changes (approx. 6%).
- Filtering methods and biological features exhibited variable patterns of laterality change.
Conclusions:
- Fiber filtering significantly impacts brain structural connectivity laterality.
- Findings highlight the need for improved tractography filtering methods for reliable brain asymmetry measurements.
- Results offer insights for developing more robust neuroimaging analysis techniques.
