Dear all,
I'm currently working on a patient-control study with relatively small sample of patients (5 patients versus 13 controls), these patients all share a similar small lesion and I would like to optimize my pre-processing pipeline for the functional scans I've acquired. Normally I would use a 'standard' DARTEL procedure that will allow me to get some optimal normalisation as follows:
1. reallign my functional scans
2. coregister my functional scans to my anatomical scans
3. segment the anatomical scans (using New Segment)
4. create DARTEL template and flow-fields from the anatomical scans
5. use the flow-fields from step 4 to normalise (DARTEL utility) my functional scans to MNI.
6. Smooth the functional scans
Now given the balance of my groups (more than twice as many controls) I'm a bit concerned that the template created in step 4 might be a bit biased towards the control group (and hence the extend of the lesion in the patient group might be diminished). Therefore I was wondering whether the following (somewhat circular approach) could be used to prevent that issue:
1. reallign my functional scans
2. coregister my functional scans to my anatomical scans
2.1 use a balanced subset of the anatomical scans (5 patients 5 controls) to segment and subsequently create a DARTEL template
2.2 normalise the DARTEL (study-specific) template to MNI (for all tissue classes)
3. segment the anatomical scans, but instead of using the standard TPM's from the segment toolbox, use the DARTEL template created in 2.2
4-6 stays the same
The other issue pertains to the smoothing, I've heard there might be an issue with using small smoothing kernels in the DARTEL procedure when the sample size is small. First of all, perhaps someone is keen to elaborate on that issue as it is not immediately clear to me why that might be the case. Second, given that the patients all share a relatively small lesion I would like to use a medium-sized smoothing kernel of 6mm (the original anatomical are acquired at 3mm isotropic). Would that be reasonable?
Best,
Richard
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