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The Van Essen Lab and Dr. Avi Snyder (Washington University's Neuroimaging Lab ) STRONGLY recommend registering your anatomical volumes to a stereotaxic space (e.g., wustl.edu's 711-2B; avg152T1; AFNI's +tlrc using @auto_tlrc with the avg152T1 or icbm452 targets). For practical reasons, it's best to do so BEFORE segmentation in Caret.
This is probably a little more important for anatomical studies using sulcal depth, but most of these reasons generalize to all studies. It generally boils down to minimizing noise. While AC-PC alignment has some advantages over starting with native input, the residual noise due to scale differences is undesirable and unnecessary.
Here are the main reasons we recommend spatial normalization using 12-parameter affine transform -- not just AC-PC alignment:
Most of these reasons become important at the stage where you are analyzing your surface-registered data (e.g., sulcal depth or fMRI). It is possible to apply the affine transform to the surfaces post-registration, but for practical reasons, this is a nightmare for wustl.edu users. It's less painful for AFNI users, but you need to know what you're doing. Before you decide not to normalize before segmenting, make sure a linear transformation will really confound your results. Make sure you can articulate those reasons to Donna Hanlon before asking her for help registering your surfaces post-hoc. For the questions we study, this linear transformation improves -- not confounds -- our results.
The following benefits of normalizing before segmenting are really benefits of AC-PC alignment (i.e., they're less affected by scale differences across subjects):
For a UCDavis autism study, we initially segmented AC-PC aligned input, rather than affine-registered input. We later registered the volumes to avg152T1 and recalculated the sulcal depth measures, to remove noise due to scale. The differences in the t-maps were disappointing, but noticeable. The normalized maps have wider range (e.g, for LFA9vCON19 t-maps before norm -7.378 to 5.149; after norm: -9.2 to 5.7) and generally stronger signal:
|AC-PC only||Full Affine|
The differences in the MDS plots, however, were more substantial:
We trust the post-normalization results more than the pre-normalization results, so we used those results for our analyses.
For most of our studies, local deformations in our normalized input confound rather than improve our findings. We let surface-based registration do the lion's share of intersubject alignment, but take advantage of the noise reduction that an affine volume registration provides.