Did you map the networks into the native subject space and then compute
the mean thickness for each ROI? If so, try to compute the subject/ROI
means after all preprocessing (ie, on the argument to --y in
mri_glmfit). You can do this with mri_segstats using --annot fsaverage
hemi parc and specifying --avgwf as the output. Then do your ROI test on
that table
On 9/25/2020 4:43 AM, Martin Juneja wrote:
External Email - Use Caution
Hello FreeSurfer experts,
I have this interesting situation. I understand its important to
include ICV as a covariate while conducting cortical volume (CV) analysis.
So for my clinical sample (CL) vs healthy-controls (HCs):
1. If I do *network-wise* analysis (i.e., after parcellating the whole
brain into 17 networks), I get highly significant differences in CV
(MANCOVA: CL < HCs) for a specific network (say N1) - *if I include
ICV as a covariate*, but not otherwise.
2. If I do *whole-brain vertex-wise* analysis, I get highly
significant differences in CV (CL < HCs) for a specific cluster (which
is overlapping with an area of N1) - *if I do not include ICV as a
covariate*, but not otherwise.
I am not sure how to interpret this i.e., ICV as a covariate for
network-wise analysis plays an opposite role i.e., makes my findings
stronger compared to ICV as a covariate for whole-brain vertex-wise
analysis i.e., makes my findings weaker. Does ICV as a covariate add
some kind of noise during whole-brain vertex-wise analysis?
I would really appreciate any help in understanding this.
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