ROI analyses using FSL
March 27, 2013
Overview
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Creating ROIs
- Creating atlas based ROIs
- Creating functional ROIs
- Drawing ROIs in FSLview
Registering ROI in standard space to single subject space
Extracting data from the ROI (mean intensity, variance, zstat, and
timecourse)
Creating an atlas based ROI
- Each atlas is a single volume/mask, with regions delineated by intensities
- Using fslview, open the 2mm MNI standard brain
> fslview &
> File > Open Standard > /usr/local/packages/fsl5.0.1/data/standard/ MNI152_T1_2mm_brain
- Add the AAL atlas (aal_MN_v4.nii) from
/usr/local/packages/MATLAB/WFU_PickAtlas_3.0.1/wfu_pickatlas/MNI_atla
s_templates/
>File > Open > /usr/local/packages/MATLAB/WFU_PickAtlas_3.0.1/wfu_pic
katlas/MNI_atlas_templates/aal_MN_v4.nii
- Find the region you are interested in extracting (for example Right
Precentral Gyrus)
- You can also use the atlas tools to help you with your anatomy:
>Tools > Toolbars > Atlas Tools
* Note the intensity of the region you will need this value to extract
Extract ROI from atlas
- For example: Extract right precentral gyrus ROI from the atlas using fslmaths
- Helpful hint: type fslmaths on the command line to see info on its usage
- On the command line:
fslmaths
/usr/local/packages/MATLAB/WFU_PickAtlas_3.0.1/wfu_pickatlas/MNI_atlas_templates/aal_MNI_V
4.nii –thr 2 –uthr 2 right_precentral
- Input is the full atlas image path
- thr: threshold input image (zero everything below)
- uthr: threshold input image (zero everything above)
- right_precentral is the output name, will be in folder you run the command from
unless you specify a full path
Creating ROIs from Functional Activations
- ROIs can be created in subject space using cluster masks from fsl 1st level
analyses to create individual functional ROI for each subject
- Example: from first level functional analyses
> fslview &
> File > Open > run02.feat/cluster_mask_zstat1.nii.gz
* Note the intensity of the region you will need this value to extract
Creating ROIs from functional activations
fslmaths run02.feat/cluster_mask_zstat1.nii.gz –thr 6 –uthr 6 example_ROI
Create ROIs from functional activations
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ROIs can be created in standard space using cluster masks from fsl 3rd
level analyses to create group ROIs that can be applied to each subject
Drawing ROIs using FSLview
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Can be done in subject or standard space
This example is in standard space
fslview
File > Create Mask
Use pencil button to fill in voxels you want to have a value of 1
File > Save
Register ROIs to subject space
If you create an ROI in standard space, and you want to extract information from
the ROI in subject space (from a first level analysis), register the ROI to the
subject’s preprocessed data
Since feat was already run, we know the transformation between the BOLD data
and the MNI brain: run01.feat/reg/example_func2standard.mat
What we need is the inverse transformation matrix:
convert_xfm inverse run02.feat/reg/example_func2standard.mat omat run02.feat/reg/Std2Example_func.mat
Apply the transformation matrix to the ROI (for each subject separately)
flirt -in test_ROI.nii.gz -ref run02.feat/reg/example_func.nii.gz -applyxfm -init
run02.feat/reg/Std2Example_func.mat -out run02.feat/reg/reg_test_ROI
Extracting data from ROIs
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This example uses the atlas based ROI after transforming to subject space
Extract data from ROIs using fslstats using the following options:
–k (mask)
–m (output the mean)
For example:
fslstats run02.feat/stats/zstat1.nii.gz –k run02.feat/reg/reg_test_ROI –m
gives you the mean zstat in that ROI for each subject
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Your inputs can be zstat images, magnitude of parameter estimate (cope
images) variance of parameter estimate (varcope images)
For DTI data, you can extract metrics such as mean FA on a subject by
subject basis from a WM ROI
Extracting data from ROIs
- Extracting mean time series from ROI
- Time series data comes from (preprocessed) filtered_func_data.nii.gz in a first
level .feat directory
- use function fslmeants
fslmeants -i filtered_func_data -o meants.txt -m my_mask