Hi Douglas,

Thank you so much for your detailed response and for explaining the rationale behind it—it was very helpful.

I have another question related to setting up the FSGD file for my analysis. Specifically, I’m examining the effects of hormone levels on the brain in a single group (i.e., not comparing different classes). My model includes both continuous and categorical covariates.

From what I’ve gathered, most examples on the FreeSurfer website focus on group comparisons, which isn't the focus of my analysis. Therefore, I’ve set up an FSGD file with only one class (see the attached screenshot for reference). In this file:

Variables like soft_A, soft_B, soft_C, child_raceMixed, and child_raceWhite are categorical covariates.
Since these are covariates and not grouping factors, I haven’t created separate classes for them.

I believe this approach is appropriate, but I wanted to confirm whether that understanding is correct.

Additionally, regarding the contrast file:

My contrast vector is 0 1 0 0 0 0 0 0 0 0 0, where the number of elements matches the number of variables in my model (including the intercept).
As I’m specifically interested in the effect of E2_mean, I’ve set its position to 1 and all others (including covariates and intercept) to 0.

Would you mind confirming whether this is the correct way to set up the FSGD and contrast files for this type of single-group analysis?

Thank you again for your guidance.

Best regards,
Muskan

On Thu, Jun 12, 2025 at 6:09 AM Douglas N. Greve <dgreve@mgh.harvard.edu> wrote:

Yes, it is recommended. The exact form it should take is in dispute, namely whether you should correct by the eTIV or the eTIV.^(2/3). The second is motivated by the idea of a sphere whose surface area grows with the 2/3 power of the volume. But cortex is not a sphere -- it is folded, so the area can grow faster than 2/3 power. I usually just scale by eTIV. When you run mris_preproc, you can add the --etiv option and it will do the scaling for you.

On 6/11/2025 4:05 AM, Muskan Khetan wrote:
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Dear Team members,

I have a question regarding vertex-wise analysis using FreeSurfer. In my current analysis, I’m looking at the effects of an independent variable on cortical thickness and surface area across the brain.

I understand that for ROI-based surface area analyses, it is typically recommended to control for total brain volume (TBV) or intracranial volume (ICV), as surface area is correlated with these global brain size measures. However, I’m unsure whether this recommendation applies to vertex-wise analyses as well or whether any underlying preprocessing steps in FreeSurfer account for brain size differences automatically.

Could you please advise on whether TBV or ICV should be included as covariates in vertex-wise surface area models? And more broadly, under what circumstances is it advisable to control for global brain size in vertex-wise analysis?

Any insights would be greatly appreciated.

Best,

Muskan
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