The pyClusterROI_test.py script included in the distribution provides detailed instructions and examples for using the library to parcellate fMRI data. This script can be used in combination with a test dataset to validate your installation. In addition to instructions for using the library functions, there are several other details that bear consideration.
Functional parcellation can be performed using either resting-state or task-based fMRI data. The data should be preprocessed to minimize the effects of confounding variation such as head motion and physiological noise. Although the different preprocessing steps applied to the data will have an impact on the clustering result, their impact has yet to be systematically evaluated. The only mandatory preprocessing step is that the data from different subjects must be spatially normalized to a template space (e.g. MNI152 or Talaraich and Tournoux) so that brain regions are aligned between them. The amount of spatial smoothing applied to the data will also impact the clustering results, on one hand spatial smoothing improves the correspondance between brain regions across individuals and improves signal-to-noise ratio. On the other hand, too much spatial smoothing will bias the clustering results. As with other preprocessing steps, the impact of spatial smoothing on clustering results hasn’t been evaluated, and the amount of spatial smoothing applied should be considered carefully.
Gray matter mask
A gray-matter mask is useful for reducing the computational complexity of clustering, as well as, confining the clustering results to gray matter. Creating the masks is tricky, and the one that I used for the paper required quite alot of trial and error. The process that I used was to segment each individuals anatomical image and to average the GM probability maps across subjects. After this I applied a threshold to the averaged probability maps in order to create a binary GM mask. I chose a threshold by visual inspection so that the mask covered all of the brain regions that I am interested in. You will notice that parts of the putamen and globus pallidus may be excluded using this procedure. You will have to try several different parameters for segmentation and thresholding and possibly erosion/dilation in order to find a happy medium. Another approach could be to use a brain atlas such as the Harvard Oxford atlas to derive your grey matter mask. Of course, probably the best way to go would be to hand segment a set of images …
Constructing connectivity matrices
Once the data has been preprocessed and a GM mask has been constructed, the next step of functional parcellation is to construct individual level similarity matrices from the data. This matrix is an Nvoxel x Nvoxel matrix in which each entry corresponds to the similarity between voxels. Two different methods are available for measuring similarity between voxels. Tcorr corresponds to the Pearson’s correlation between voxel time-series. Scorr is the spatial correlation between whole-brain functional connectivity maps, each of which are created by correlating the voxel time-series with every other voxel time-series in the brain. The spatial constraint is imposed on the clustering result by only calculating similarity between neigboring voxels. A third method for generating connectivity matrices is included, the “ones connectivity”, sets the similarity between voxels to ‘1’ if they are neighbors, and ‘0’ otherwise. In other words, only spatial information, and no functional information, is used in the clustering.
The similarity metric chosen depends on the desired properties of the clustering solution. Tcorr is preferred when the desire is to maximize the temporal homogeniety of the clusters. Scorr should be used when the desire is to maximaize the similarity between whole-brain functional connectivity maps generated at each voxel. The spatial-only clustering is extremely fast and only requires the gray-matter mask for construction.
Instructions for generating individual-level connectivity matrices using each of these methods are illustrated below (copined from pyClusterROI_test.py):
Tcorr - temporal correlation
from make_local_connectivity_tcorr import * # the name of the maskfile that we will be using maskname="gm_maskfile.nii.gz" # make a list of all of the input fMRI files that we # will be using infiles = [ 'subject1.nii.gz', 'subject2.nii.gz', 'subject3.nii.gz' ] # construct the connectivity matrices using tcorr # and a r>0.5 threshold for idx, in_file in enumerate(infiles): # construct an output filename for this file outname='rm_tcorr_conn_'+str(idx)+'.npy' print 'tcorr connectivity',in_file # call the funtion to make connectivity make_local_connectivity_tcorr( in_file, maskname, outname, 0.5 )
Scorr - spatial correlation
from make_local_connectivity_scorr import * # the name of the maskfile that we will be using maskname="gm_maskfile.nii.gz" # make a list of all of the input fMRI files that we # will be using infiles = [ 'subject1.nii.gz', 'subject2.nii.gz', 'subject3.nii.gz' ] # construct the connectivity matrices using scorr # and a r>0.5 threshold # This can take a _really_ long time for idx, in_file in enumerate(infiles): # construct an output filename for this file outname='rm_scorr_conn_'+str(idx)+'.npy' print 'scorr connectivity',in_file # call the funtion to make connectivity make_local_connectivity_scorr( in_file, maskname, outname, 0.5 )
Ones - spatial information only
from make_local_connectivity_ones import * # the name of the maskfile that we will be using maskname="gm_maskfile.nii.gz" # the easiest is random clustering which doesn't # require any functional data, just the mask print 'ones connectivity' make_local_connectivity_ones( maskname, 'rm_ones_connectivity.npy')
Group-mean and two-level clustering
Clustering can begin once the individual level similarity matrices have been constructed. pyClusterROI includes two different methods for combining information across individuals to accomplish group-level clustering. In the group-mean approach, the individual similarity matrices are averaged and the resulting matrix is clustered using the normalized-cut algorithm. The two-level approach begins with the normalized-cut clustering of each individuals similarity matrix. Results are combined across individuals to for a “coincidence matrix”, that is clustered to obtain the group-level clustering results. The “ones” clustering is accomplished by a single normalized-cut clustering of the ones connectivity matrix. Below are examples for each of these clustering methods (copied from pyClusterROI_test.py).
from group_mean_binfile_parcellation import * NUM_CLUSTERS = [100,150,200] # group_mean clustering is pretty simple, input the # connectivity files and run. We can perform multiple # clusterings with this function, so once again the # output filename is a prefix tcorr_conn_files=['rm_tcorr_conn_0.npy', 'rm_tcorr_conn_1.npy', 'rm_tcorr_conn_2.npy'] print 'group-mean parcellate tcorr' group_mean_binfile_parcellate( tcorr_conn_files,\ 'rm_group_mean_tcorr_cluster',\ NUM_CLUSTERS,mask_voxels);
from binfile_parcellation import * from group_binfile_parcellation import * NUM_CLUSTERS = [100,150,200] # for tcorr for idx, in_file in enumerate(infiles): # construct filenames infile='rm_tcorr_conn_'+str(idx)+'.npy' outfile='rm_tcorr_indiv_cluster_'+str(idx) print 'tcorr parcellate',in_file binfile_parcellate(infile, outfile, NUM_CLUSTERS) # the 2-level clustering has to be performed once for # each desired clustering level, and requires individual # level clusterings as inputs for k in NUM_CLUSTERS: ind_clust_files= for i in range(0,len(infiles)): ind_clust_files.append('rm_tcorr_indiv_cluster_'+\ str(i)+'_'+str(k)+'.npy') print '2-level parcellate tcorr',k group_binfile_parcellate(ind_clust_files,\ 'rm_group_tcorr_cluster_'+str(k)+'.npy',\ k,mask_voxels)
from binfile_parcellation import * NUM_CLUSTERS = [100,150,200] # For random custering, this is all we need to do, there is # no need for group level clustering, remember that the # output filename is a prefix binfile_parcellate('rm_ones_connectivity.npy',\ 'rm_ones_cluster',NUM_CLUSTERS)
Finalizing the cluster atlas
The outputs of the clustering must be transformed in order to make them more easy to use. The output .npy files are ‘index files’ and must be remapped to voxel space. Additionally, the regions are not consecutively numbered. Utility functions are provided to handle these transformations.
from make_image_from_bin_renum import * # write out for group mean clustering for k in NUM_CLUSTERS: binfile='rm_group_mean_tcorr_cluster_'+str(k)+'.npy' imgfile='rm_group_mean_tcorr_cluster_'+str(k)+'.nii.gz' make_image_from_bin_renum(imgfile,binfile,maskname)
Several individuals have asked for labels for the clusters. A python script is now provided that calculates the center of mass for each cluster and maps this coordinate to brain region labels. This mapping is performed for several different brain atlases, including Harvard-Oxford, Juelich, Talaraich and Tournoux, and the MNI atlases. This script is called from the command line using the following syntax:
python parcel_naming.py tcorr05_2level_all.nii.gz \ tcorr05_2level '50,100,150,200'