I would advice setting up the FSGD files with 8 classes based on your 3
discrete, 2-level variables, eg, MaleControlWhite then use two
continuous variables Age and eTIV.
You should definitely normalize the eTIV (and probably age too)
On 11/10/2021 7:07 PM, Zeng, Qi wrote:
External Email - Use Caution
Hi Douglas,
Just to be clear, should I recode discrete variables as numeric as
sample1) or change discrete variables into strings 2) or even change
the comparison groups into strings too 3)?
1)
Variables AgeGenderRaceeTIV
InputSub001_baseControl85111350519.71
InputSub002_baseControl81.3 011558515.86
InputSub003_baseControl81.3 001558515.86
or 2)
Variables AgeGenderRaceeTIV
InputSub001_baseControl85"Female""White"1350519.71
InputSub002_baseControl81.3 "Male""White"1558515.86
InputSub003_baseControl72.7 "Male""White"1187649.47
or 3)
Variables AgeGenderRaceeTIV
InputSub001_base"Control"85"Female""White"1350519.71
InputSub002_base"Control"81.3 "Male""White"1558515.86
InputSub003_base"Control"72.7 "Male""White"1187649.47
Best,
Zeng
On Wed, Nov 10, 2021 at 6:38 PM Douglas N. Greve
<dgr...@mgh.harvard.edu> wrote:
Did you read the error message? It says:
Variable 2 has character string Female
Variables should be continuous numbers
You have listed Gender and Race as strings, they have to be
numbers (eg, 1, 2)
You probably want to have these be discrete variables instead of
continuous
On 11/10/2021 6:20 PM, Zeng, Qi wrote:
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Hi Douglas,
Here is the command line:
mri_glmfit \
--y rh.thickness-rate.MyStudy.10.mgh \
--fsgd FSGD/MyStudy.fsgd dods\
--C Contrasts/Case-Control_age_gender_race_etiv.mtx\
--surf fsaverage rh \
--cortex \
--glmdir
rh.thickness-rate.MyStudy.10.mgh_age_gender_race_etiv.glmdir \
--eres-save
Also error message in terminal:
gdfRead(): reading FSGD/MyStudy.fsgd
WARNING: variable 1 is "Gender" which is often a discrete factor
The proper way to handle discrete factors is to create classes.
See *MailScanner has detected a possible fraud attempt from
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https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples
<https://secure-web.cisco.com/1NHVxBIRnha6EjEdEho81-VE9d-vjJ6ivzt10z28vhX9c94pCwosQpkLG4Hfu3nLYp7KMDne5wAk9mK45YIQJEZBe6UlteF2wwLohmZJzp070UjHvv6fqugRAkgBk0uNO3Fsgo5BzblW95J5iO5sXWwZLK-LGAZJJTBaU7mLTkO5L70JRSscCW3pdc-G5KchZMbVyeY65gGH4w5MMIYBSVHvX03spMjZ85XkHbkgYYmWXTFnzqUGhqGgHygP5sLeR7OWPKIzEb7X3pHi7POf0UyEHJAZpQLU9bgT2BnOcClt0kW4mll1ODQDC2Y0U7-0f/https%3A%2F%2Fsurfer.nmr.mgh.harvard.edu%2Ffswiki%2FFsgdExamples>
ERROR: gdfReadV1: Format Error: Input line 1, subjid = sub001_base
Variable 2 has character string Female
Variables should be continuous numbers
FSGDF Format Error: file = FSGD/MyStudy.fsgd, tag=Input
Also attached FSGD file below.
Thank you so much for the help!
Best,
Zeng
On Wed, Nov 10, 2021 at 5:41 PM Douglas N. Greve
<dgr...@mgh.harvard.edu> wrote:
please send your command line and full terminal output as
well as the fsgd file
On 11/10/2021 5:12 PM, Zeng, Qi wrote:
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Hi Freesurfer experts,
Here, I want to design a simple group comparison (two
levels: Case vs. Control) and also count for 4
covariates (two continuous and two categoricals). However,
the GLM model cannot recognize my discrete variables. What
do you suggest to change?
>> FSDG file:
GroupDescriptorFile 1
Title MyStudy
Class Control
Class Case
Variables agegenderrace eTIV
Inputsub001_baseControl85FemaleWhite1350519
Contrast:
>> Design matrix for Case-Control and regress out the effect
of age, gender, race, and eTIV.
-1 1 0 0 0 0 0 0 0 0
Best,
Zeng
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