Thank you so much Doug!

It worked! I was just wondering which value I should report as the correlation coefficient of my analysis.

This is the output I got:

# ColHeaders  Index SegId NVoxels Volume_mm3 StructName Mean StdDev Min Max Range 
  1  -4     20373    20373.0  Seg-004    -0.1596     0.0468    -0.3134    -0.1017     0.2117

Should I report the mean (-0.1596) or the min (-0.3134)?

Thanks again

Try something like

mri_segstats --i pcc.mgh --seg cluster.ocn.mgh --excludeid 0 --sum 
cluster.pcc.dat


2017-04-08 1:35 GMT+01:00 tom parker <tomparker540@gmail.com>:
Hi Doug,

Thank you for your answer!

It would be great if you could send me some instructions.

Thanks for the help!

Douglas N Greve Thu, 06 Apr 2017 13:32:07 -0700

I guess you could average it over space (eg, over a cluster). Let me 
know if you want instructions. Otherwise, I'm not sure what to say.


2017-04-04 11:49 GMT+01:00 tom parker <tomparker540@gmail.com>:
Hi Doug,

Thanks! I realize now I didn't explain myself properly.

I need one single R value for the correlation analysis I made, similarly to when I correlate two variables with Pearson's R in a statistical package.

I have a referee asking me to put it on a table with the freesurfer results. Is there a way to get a single average R value from the pcc.mgh file?

Thank you so much!

Douglas Greve Mon, 03 Apr 2017 18:46:46 -0700

Each vertex is a correlation value.


2017-04-04 1:09 GMT+01:00 tom parker <tomparker540@gmail.com>:
Thanks Doug!

I have version 6 and got a pcc.mgh file.

How can I get a correlation R value from this file?

Thanks again

Douglas N Greve Mon, 03 Apr 2017 09:41:41 -0700

if you are using version 6, then it should have produced a file called 
pcc.mgh. This the partial pearson correlation coef.


2017-04-03 15:40 GMT+01:00 tom parker <tomparker540@gmail.com>:
Dear Freesurfers,

I made some correlations between cortical thickness and age in qdec.

Is it possible to get the correlation coefficient value of this analysis?

I just need to put it in a table with the significant results.

Thank you so much!