Clustsim error in 3dttest++ for small volume correction

AFNI Message Board
1

AFNI version info (afni -ver): 24.1.11

Hi all, I'm using the -Clustsim option in 3dttest++ to try and do a small volume correction but encountered errors as below:

++ saving main effect t-stat MIN/MAX values in ./GLM2_CT_gamble_RPE_SVC.0029.minmax.1D
++ output short-ized file ./GLM2_CT_gamble_RPE_SVC.0029.sdat with 163 values for each of 315 volumes
** ERROR:   Output error? file size = 0   num volumes = 0 ???
 + ======= end of .sdat output run of 3dttest++ =======

I found a similar topic: Clustsim Failure in 3dttest++. I guess both because the ClustSim option is only for global cluster-level thresholding

So are there any known ways to do small volmue correction with Clustsim in 3dttest or other methods to try?

2

Small volume correction can be seen as a cheap workaround to address the challenges of stringent multiple testing adjustments. For further discussion, here's a blog post that provides a general overview of multiple testing adjustments.

Gang Chen

3

Thank you for your reply! Your post mentions two ways to address the challenges of stringent multiple testing adjustments, which is helpful to me. But does this also mean that there is no correction method for SVC in AFNI, considering its limitations?

4

As mentioned in the blog post, there are more effective methods for addressing the multiple testing issue. In my view, the use of "small volume correction" is neither necessary nor methodologically sound in most cases:

The adjustment process is highly sensitive to the size and scope of the data domain, such as whether the analysis is conducted on the whole brain, gray matter, or a specific region. This flexibility introduces the risk of "adjustment hacking," where researchers might post hoc select a smaller region to apply adjustments, using techniques like small volume correction. This approach can artificially inflate the statistical evidence of results, leading to biased interpretations and diminishing the robustness of the findings.

Gang Chen

5

I see, your insights are very inspiring to me, thanks again for your reply!