Enhancing functional connectivity analysis in task-based fMRI using the BOLD-filter method: Greater network and...

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Functional Magnetic Resonance Imaging (fMRI)

Background:

• Task-based fMRI (tb-fMRI) investigates brain connectivity during specific tasks.
• tb-fMRI offers greater specificity than resting-state fMRI (rs-fMRI) for studying task-relevant networks.
• Preprocessing is crucial for accurate functional connectivity (FC) analysis in tb-fMRI.

Purpose of the Study:

• To evaluate the BOLD-filter method, originally for rs-fMRI, as a preprocessing step for tb-fMRI.
• To enhance the sensitivity and specificity of detecting task-induced brain activity.
• To improve the robustness and interpretability of FC analysis in tb-fMRI.

Main Methods:

• Applied the BOLD-filter to tb-fMRI data as a preprocessing technique.
• Compared BOLD-filter preprocessing with conventional methods for FC analysis.
• Analyzed the impact on activation voxel detection and the clarity of FC network patterns.

Main Results:

• BOLD-filter substantially improved the isolation of task-evoked BOLD signals.
• Identified significantly more activation voxels compared to conventional preprocessing.
• Revealed clearer task-related FC network patterns, including gender-specific differences, not seen with standard methods.

Conclusions:

• The BOLD-filter enhances the robustness and interpretability of FC analysis in tb-fMRI.
• This method effectively isolates meaningful functional networks, offering advantages over conventional approaches.
• BOLD-filter is a valuable improvement for characterizing task-induced brain activity in tb-fMRI.