cr: http://fsl.fmrib.ox.ac.uk/fslcourse/2019_Beijing/lectures/Registration/FSL_Registration_Practical.pdf
the story starts from a question...
If we were interested in the functional signal in (for example) the amygdala within the fMRI data in a particular subject, which space would we transform a standard mask of the amygdala into for further processing?
The structural space - we want to make sure we get the most accurate signal from the data, and this is when the image is in structural space
The distortion-corrected functional space - we do our task analysis in functional space, once it has been corrected for distortions
The native functional space - we do our task analysis in functional space, without any registrations applied
the correct answer is no.2. but how come?
0.1. get familiar with transformation files
- highres2standard.mat
- highres2standard_warp.nii.gz
- ...
0.2 three main spaces in a FEAT analysis
- functional (represented by example_func);
- structural (represented by highres);
- MNI (represented by standard)
1. Creating an example mask [in MNI space] [from existing Atlases]
fsleyes -std &
......gui acts
2. Inverting a transform [to get std2highres_warp)
invwarp -w highres2standard_warp -o standard2highres_warp -r highres
3. Applying a transformation
applywarp -i LeftHippMask -r example_func -o LeftHippMaskFunc -w standard2highres_warp --postmat=highres2example_func.mat
4. Thresholding the Mask [now in func space] [to make it binary]
4.1 to choose 1)0.9, tighter; 2)0.5, of similar size/volume as before; 3)0.1, ok to include neighbouring stuctures.
4.2
fslmaths LeftHippMaskFunc -thr 0.9 -bin LeftHippMaskFuncBin
5. Using the Mask
e.g.
fslmeants -i ../filtered_func_data -m LeftHippMaskFuncBin
【Q】why filtered_func_data, the pre-processed fMRI data
