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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
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Tractography density affects whole-brain structural architecture and resting-state dynamical modeling.
Kyesam Jung1, Simon B Eickhoff1, Oleksandr V Popovych1
1Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Germany.
Neuroimage
|May 17, 2021
Summary
Standardized processing of diffusion-weighted imaging data is crucial for accurate whole-brain modeling. Varying tractography density impacts structural connectome network properties and model performance, necessitating tailored data processing recommendations.
Area of Science:
- Neuroscience
- Computational Biology
- Medical Imaging
Background:
- Resting-state brain dynamics modeling relies on neuroimaging data.
- Lack of standardized magnetic resonance imaging pipelines affects structural and functional connectome analysis.
Purpose of the Study:
- Investigate how diffusion-weighted data processing parameters influence whole-brain mathematical model validation.
- Explore variations in model validation quality across different simulation conditions.
Main Methods:
- Simulated various whole-brain tractography (WBT) streamline counts for structural connectivity (SC) extraction.
- Utilized cortical parcellations based on functional and anatomical properties.
- Employed distinct model fitting modalities to assess SC impact.
Main Results:
- Graph-theoretical network properties of the structural connectome are sensitive to tractography density.
- Model performance is strongly related to these network properties.
- Optimal WBT streamline counts differ across brain atlases.
Conclusions:
- Model performance can be enhanced by considering network properties and optimal WBT parameters.
- Subject populations can be stratified based on WBT density, enabling personalized data processing and parcellation strategies.
