Tutorial: Connectivity Mapping
This tutorial uses Camino to generate a graph representing the connections between the different cortical regions of the brain. The connectivity mapping involves the following steps:
1. Fiber Tracking
The seed file for the tracking has to cover the whole brain region. This can be done using for e.g. MRICro (Select 'ROI -> Apply intensity filter to volume'. Specify the minimum and the maximum thresholds such that the filter covers the whole brain and 'Save as Analyze').
Run track and create a raw binary track file.
Using PICo tractography algorithm:
Detailed explanation of various tracking algorithms is explained in the Tractography tutorial.
2. Cortical Segmentation
Segmentation of the cortical regions of the brain can be performed using freesurfer. The segmentation is done on the T1-weighted image (brain_T1.nii). After installing freesurfer, use the following code to start the segmentation process, which could take 20-40 hrs to complete.
If the FOV of the T1-weighted image is more than 256, then use -cw256 parameter in the above code. During the segmentation process, verify the registration of the T1-image with the atlas image in the freesurfer. (i.e) After the -talairach operation, execute
and check if the MOVABLE and the TARGET are registered properly.
After the execution of -skullstrip, check if the skull has been removed properly from the T1 image using the following code.
The cortical segmentation image will be stored as wmparc.mgz in brain1/mri. This can be converted into the Nifti or Analyze format using
The segmented image can be viewed using
3. Registration of Segmentation and Diffusion Weighted Image
If the resolution of the T1-weighted image, over which the segmentation is performed, is different from that of the diffusion-weighted image, then the images have to be registered. This can be done using FSL's FLIRT tool. The brain region can be extracted from the b0 image using, BET.
The output of the BET tool is used for the registration with the T1 image. The skull striped version of the T1 image is generated by freesurfer as brain1/mri/brain.mgz. The higher resolution T1 image, is taken as the reference image and the skull stripped b0 image as the input image. Use the 'Advanced' options to set the 'Cost function' to be 'Mutual Information'.
Obtain the inverse transformation matrix using 'Misc->Invert FLIRT Transform' in the FSL GUI.
Apply the inverse matrix over the segmentation image with the 'Interpolation Method' as 'Nearest Neighbour'.
4. Construction of Cortical Connectivity
The graph representing the connections between the regions in the segmented image can be obtained using
Figure 1. The graph visualization output by conmap.
This creates 'Connectivity.png', shown in figure 1, with the graph representing the ROIs and the connections between them. The thickness of the connections is proportional to the number of fibers connecting those two regions. The vertices of the graph indicate the ROI and their diameter is proportional to the number of tracts reaching that vertex or ROI. Another file, 'ConnectionMatrix.txt', containing a matrix of the number of tracts connecting different regions, is created at the same location.
If the mapping between few segments in the brain is required, the indices of those regions can be given separately. The labels and the indices of the different segments in wmparc.mgz can be found at brain1/stats/wmparc.stats. Create a file, say indices.txt with comma-separated indices of the required segments.
where 1001,1002.. are the indices of the segments for which graph has to be drawn. The labels of the different vertices can be specified, by creating a file say indices-labels.txt in the following format.
These files can be specified as optional parameters.
where threshold indicates the minimum number of fiber connections that has to be drawn in the graph.
. Patric Hagmann et al. (2007) Mapping Human Whole-Brain Structural Networks with Diffusion MRI PLoS ONE 2(7): e597. doi:10.1371/journal.pone.0000597