Hi Rowena,

You are right, our HOW-TO document describing how to spatially normalize individual DTI data to the IIT Human Brain Atlas using DTI-TK describes how to register directly to the atlas and does not cover the case where a population template is first constructed and then registered to the IIT atlas. Before answering your specific questions, let me pause here to comment on the two approaches above. I tend to prefer the approach covered by our HOW-TO document. The reasons are the following. A study-specific template is theoretically most representative of the data under study and may lead to the best spatial normalization accuracy. Based on that, some users first register to a study-specific template and then register the study-specific template to the IIT atlas. However, the accuracy of spatial normalization to a study-specific template is high only when the study-specific template is carefully constructed and of high quality. Poorly constructed study-specific templates (e.g. when small number of subjects are used, or when suboptimal template-building procedures are used) are actually not representative of the individual data under study and lead to low spatial normalization accuracy. On the other hand directly registering to a high quality standardized template like the one in the IIT atlas, is shown to perform very similar to registering to a high-quality study-specific template and then registering that to the IIT atlas, and is also consistent and fast (no need to make a study-specific template). In brief, both approaches are appropriate, but if you decide to make a study-specific template first, you need to make sure that the study-specific template you are constructing is of high quality. If you cannot achieve that for whatever reason, or you don't want to worry about it, or you want to be able to compare results across studies, then register directly to the IIT atlas as shown in our HOW-TO document.
Now, in terms of your specific questions:
1) Yes, you should be able to combine the steps you mentioned with the later steps of our HOW-TO document.
2) Yes, you can perform TBSS after you have normalized to the IIT atlas. Just read the HOW-TO use the IIT atlas in TBSS document. It walks you through the steps you'll need to follow. And once you are done with your TBSS analysis in IIT space following the steps in our HOW-TO document, you can use our white matter atlas resources to help you understand your findings. For example, if you find some significant effects somewhere in white matter, you can use the "regionstat" program to give you the list of the most probable connections going through the region with the significant effects (See HOW-TO-extract-pairs-of-GM-labels-with-connections-through-WM-ROI). It does not require any tractography and is very fast. It works by interrogating our 4D white matter atlas to extract the most probable connections through your white matter ROI.

Let me know if you have more questions.
Regards,
Konstantinos

Hi Rowena,

I think the jhu labels are in 182 space (not 256). As a result, the output of DTI-TK using our 256 atlas will most likely not work with the jhu labels. I should mention here that you may want to take a look into the "HOW-TO-use-the-WM-atlas". The IIT white matter atlas contains four-dimensional (4D) white matter labels. This means that each white matter voxel is assigned a list of gray matter label-pairs with the most probable connections through that voxel. The reason behind the 4D labels is that multiple fiber populations often share the same voxels, and therefore white matter voxels must be given the chance to have multiple labels. If you still prefer the winner-takes-all approach (similar to the jhu approach) then you can simply use the top label in each voxel of our atlas, which means you can simply use the first volume of our 4D atlas and ignore the rest of the volumes (less likely labels). However, I recommend avoiding "winner-takes-all" white matter atlases, for the reason explained above. It is best to embrace the truth i.e. multiple connections can share the same white matter voxels, so each white matter voxel should have multiple labels.

Please let me know if you have more questions.

Regards,
Konstantinos