>> I was wondering if you would mind giving me some additional guidance on visualizing the tracts in SUMA.?
The output of probabilistic tractography is 'volume based'-- that is, it's sets of voxels. You can load individual bricks of this kind of data into SUMA using the '-vol' option. There are some variations around how precisely to do this, depending on what you want to look at what you want to output. The PAIRMAP file will have a map of all pairwise connections in brick [0]; it will have a map of all pairwise connections from roi_1 to any other ROI in brick [1]; etc. Additionally, if you used '-dump_rois AFNI' when called 3dTrackID, you have a set of binary masks, each one showing an individual connection (i.e., one for the connection between roi_1 and roi_2, between roi_1 and roi_3, etc.); if you wanted these to be thresholdable afterward, then you would also include the '-posteriori' switch, and then each of these files are *maps*, containing the number of voxels through a region.
Therefore, you could load these in various ways:
# binary mask of all AND-logic connections:
$ suma -vol PAIRMAP_NAME[0]
# mask of all AND-logic conenctions through ROI 2:
$ suma -vol PAIRMAP_NAME[2]
# specific connection between ROI 1 and 2, if you used '-dump_rois AFNI':
$ suma -vol DUMP_DIR/NET_000_ROI_001_002+orig.
Volumes in SUMA can be viewed as planar slices, or as volume surfaces. Default is planar slices. To view the PROB dataset as volumes, open the SUMA controller (View -> 'Object controller', or CTRL+s); in the controller window for the volume, on the left side, nearish to the bottom is a 'Volume rendering controls' section-- click the little square by the 'v' to turn on the 'Volume rendering'.
>> Would I be looking at o.PR_000.niml.dset or the o.PR_000_PAIRMAP.niml.lt?
The o.PR_000_PAIRMAP.niml.lt file just contains label naming information for the results. It is supplementary and need not be viewed.
The o.PR_000.niml.dset file just contains the (structural) connectivity matrix information, and can be loaded into SUMA for viewing the matrix properties of the connections (FA, MD, etc.). It can be loaded into SUMA using the '-gdset' option, so that taking one of the above examples:
$ suma -vol PAIRMAP_NAME[0] -gdset o.PR_000.niml.dset
Now you could toggle between viewing the tract map in space, and the properties, by pressing the '.' key. (NB: this may require zooming in a lot when you initially open SUMA, sometimes it does.)
>> Also I assume that an anatomical image (spgr) for the subject would serve as my underlay? Although this is in original anatomical space and the tracts will be in diffusion space.
You could use the FA map immediately, sometimes I do; it is a volume-type data set as well, and so could be added by just using:
$ suma -vol PAIRMAP_NAME[0] -gdset o.PR_000.niml.dset -vol FA_FILE
To use the anatomical, if it's in a different space, then you should warp it to the DWI space using, for example, 3dAllineate (or 3dQwarp if you want more precise alignment-- my guess is that 3dAllineate might be good enough for this?). Then you can load it as a volume, in the same way.
>> Also, do you typically do additionally thresholding after tracking to get rid of any additional extraneous tracts?
You can do this with '-posteriori' for the , if you wish.
>> Also I see in the o.PR_000.grid, there is an FA value for ROI1 and ROI2. However, I am interested in the FA value for the path that connects ROI1 and ROI2. How does one obtain that?
There are just column labels, but the same would apply along the rows as well; it's like looking at a correlation matrix. The element in the i-th row and j-th column shows the property value for the WM connecting roi_i and roi_j.
Thus, while part of the file looks like:
roi_001 roi_002 roi_003
1.0000 0.1234 0.5678
0.1234 1.0000 0.2468
0.5678 0.2468 1.0000
You could think of it as also having hte labels along the rows, as well, a la:
roi_001 roi_002 roi_003
roi_001 1.0000 0.1234 0.5678
roi_002 0.1234 1.0000 0.2468
roi_003 0.5678 0.2468 1.0000
which might make it more apparent that the value '0.1234' is the property connecting roi_001 and roi_002.
If you want to view any matrix easily, there's either the 'suma .... -gdset FILE.niml.dset ...' route, or from the commandline, a program called fat_mat_sel.py:
$ fat_mat_sel.py -m FILE_NAME.grid -P 'FA' -H
would both show the FA matrix in the FILE_NAME grid file ('-H' is used to pause and hold it on the screen), and when you hit a button again on the terminal, it will save it as a JPG file.
--pt