01_fmriprep_SubjX.sh→ Run fMRIPrep preprocessing.02_mean_bold_by_network.py→ Extract mean activation per network (DMN, DAN).03_DMNandDAN_seed_connectivity.py→ Compute within-network seed-based connectivity.04_plot_seed_violins.py→ Generate violin plots for group comparisons.
Minimal and modular scripts for resting-state fMRI preprocessing, network activation mapping, seed-based connectivity, and group-level analysis with standardized violin-plot visualizations.
These scripts were designed for reproducible pipelines based on fMRIPrep, Nilearn, and the Schaefer 7-network atlas, and can be adapted to any BIDS-formatted dataset.
| Script | Description |
|---|---|
fmriprep_SubjX.sh |
Minimal fMRIPrep preprocessing (anatomical + functional) with MNI alignment and basic confound regression. |
mean_bold_by_network.py |
Extracts mean activation (BOLD or PSC) within functional networks (DMN, DAN). |
DMNandDAN_seed_connectivity.py |
Computes Fisher-z–transformed seed-based correlations for DMN and DAN networks. |
plot_seed_violins.py |
Generates consistent, color-coded violin plots comparing groups (Welch’s t, Hedges’ g, and 95% CI). |
python >=3.8
pip install numpy pandas nibabel nilearn matplotlib scipy