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The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI
Fatma Gargouri1,2,3, Fathi Kallel3, Sebastien Delphine1
1Institut du Cerveau et de la Moelle Épinière, Centre de NeuroImagerie de Recherche, Paris, France.
Frontiers in Computational Neuroscience
|March 3, 2018
Summary
Preprocessing steps significantly impact resting state functional MRI (rs-fMRI) analysis. Optimal ordering, particularly applying smoothing and tCompCor last, maximizes network efficiency and retains small-world properties for better brain connectivity insights.
Area of Science:
- Neuroimaging
- Brain Connectivity Analysis
- Graph Theory in Neuroscience
Background:
- Resting state functional MRI (rs-fMRI) measures spontaneous brain activity.
- Functional connectivity measures are sensitive to BOLD signal data processing quality.
- Graph theory is increasingly used to analyze complex brain networks.
Purpose of the Study:
- To investigate the influence of preprocessing steps and their order on small-world topology and network efficiency in rs-fMRI.
- To optimize preprocessing strategies for maximizing local and global efficiency while minimizing network cost.
- To understand how preprocessing choices affect graph theory measures in brain connectivity.
Main Methods:
- Applied standard rs-fMRI preprocessing steps: slice-timing, realignment, smoothing, filtering, and tCompCor.
- Evaluated different orders of these preprocessing steps on graph theory measures.
- Analyzed data from 54 healthy subjects to assess impacts on small-world properties and network efficiency.
Main Results:
- The order of preprocessing steps significantly impacted graph measures.
- CSR and SCR strategies (realignment, smoothing, tCompCor) showed highest global efficiency.
- FSCR strategy (realignment, tCompCor, smoothing, filtering) yielded the highest local efficiency.
Conclusions:
- Graph theory measures of functional connectivity are dependent on the preprocessing pipeline's order.
- Using smoothing and tCompCor as final steps is optimal for global efficiency.
- Additional filtering as a final step enhances local efficiency in rs-fMRI analysis.
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