Analysis of resting state functional MRI brain data (rs-fMRI) to extract ROI-wise correlation coefficients. This workflow works with the BIDS neuroimaging standard.
The full workflow includes:
- Conversion of input DICOMs to BIDS structure
- Various standard fMRI preprocessing steps based on fMRIPrep (such as motion correction, slice timing correction, distortion correction, registration to anatomical space, surface registration, and confound estimation).
- Postprocessing involving computation of correlation and partial correlation matrices across various regions.
- ROI-analysis based on segmentation of structural images using either FreeSurfer's recon-all or its compatible, deep learning-based alternative FastSurfer.
- Visualization of network connectivity across six main large-scale functional networks:
- Default Mode Network
- Language Network
- Both Ventral Attention Networks
- Salience Network
- Secondary Visual Network
Report output examples:
Functional connectivity matrix showing correlation coefficient between each pair of ROIs.
Example visualization of functional connectivity between brain regions associated with the Default Mode Network.
Required inputs:
- fMRI
- Resting-state functional 4D time series
- Isotropic resolution recommended
- Must be labeled as 'fMRI' modality
- T1
- 3D anatomical image
- Isotropic resolution recommended
- Must be labelled 'T1' modality
Minimum input requirements:
- DICOM images
Optional inputs:
- Gradient field maps
- Phase image must be tagged 'gfm_phase'
- Magnitude image must be tagged 'gfm_magnitude'
- FreeSurfer recon-all segmentation output
- Segmentation performed on same T1 used as input to the current workflow
Outputs:
- correlation.csv: Connectivity matrix, with values corresponding to corresponding to correlation coefficient between each pair of ROIs.
- partial_correlation.csv: Connectivity matrix, with values corresponding to corresponding to partial correlation coefficient between each pair of ROIs. Partial correlation is calculated by removing the influence of confounding variables. These include 6 motion parameters, 5 compcor components [Behzadi et al. 2007] and the CSF, WM and global signals.
- report.pdf: report file with results summary.
Typical execution time: ~2 hours
References:
- fMRI preprocessing:
Esteban, O., Markiewicz, C.J., Blair, R.W. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16, 111–116 (2019). https://doi.org/10.1038/s41592-018-0235-4 - FastSurfer
- FreeSurfer: [Dale et al. 1999], [Fischl et al. 1999], [Fischl et al. 2000], [Fischl et al. 2002], [Fischl et al. 2004]
- Behzadi Y, Restom K, Liau J, Liu TT, A component-based noise correction method (CompCor) for BOLD and perfusion-based fMRI. NeuroImage. 2007. doi:10.1016/j.neuroimage.2007.04.042