One of my papers is under review showing significant differences in
cortical surface area between healthy controls and patients.

In this paper, regarding statistical results, for multiple comparison
correction, I ran Monte Carlo simulations using following command:

mri_glmfit-sim \

  --cache 4 neg \
  --cwp  0.05\

In the paper, I reported that multiple comparisons were corrected at p <
0.05 using Monte Carlo simulations. I also reported cluster sizes (number
of voxels) of all clusters which survived multiple comparison correction.

I checked literature and found that that's how people report cluster
results after running analysis in FreeSurfer.

One of the reviewers asked- Did you consider any constraints on findings
(specifically cluster size) before considering the finding significant?

Could you please help me in understanding this question and how can this be

In this paper:
authors reported that -
"The data were tested against an empirical null distribution of maximum
cluster size across 10,000 iterations using Z Monte Carlo simulations as
implemented in FreeSurfer [31,32] synthesized with a cluster-forming
threshold of P < 0.05 (two-sided), yielding clusters fully corrected for
multiple comparisons across the surfaces. Clusterwise corrected P < 0.05
(two-sided) was regarded significant."

I assume that that's what reviewer is demanding me to report in my paper.

I would really appreciate any help.
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