ROI analyses using FSL March 27, 2013

Overview 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/fsl-5.0.1/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_atlas_templates/ >File > Open > /usr/local/packages/MATLAB/WFU_PickAtlas_3.0.1/wfu_pickatlas/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_V4.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 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 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 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 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