Bold_spatial_preprocessing2 pipeline¶
An example of fMRI data pre-processing¶
Pipeline insight
- The Bold_spatial_preprocessing2 pipeline combines the following bricks:
- Normalize12 Estimate & write
(default values: jobtype = ‘estwrite’, bias_regularization = 0.0001, bias_fwhm = 30, write_interp = 4)
- Normalize12 Write
(default values: jobtype = ‘write’, write_interp = 4)
Inputs parameters
- anat_file (an existing uncompressed file):
An anatomical image (valid extensions: [.nii]). Ex. 3D T1 sequence sush as T1 turbo field echo.
ex. /home/username/data/raw_data/Anat.nii
- func_files (A list of items which are an existing uncompressed file)
Functional images (valid extensions: [.nii]). Ex. 4D T2* sequence sush as echo planar imaging.
ex. ['/home/username/data/raw_data/Func.nii']
Outputs parameters:
- normalized_anat (a list of items which are a pathlike object or string representing a file):
The final normalised anatomical image.
ex. /home/username/data/derived_data/wAnat.nii
- realignment_parameters (a list of items which are a pathlike object or string representing a file)
The estimated translation and rotation parameters during the realign stage.
ex. /home/username/data/derived_data/rp_Func.txt
- smoothed_func (a list of items which are a file name)
The final, realigned then coregistered then normalised then smoothed, functional images.
ex. /home/username/data/derived_data/swrFunc.nii