On Wed, Nov 10, 2021 at 6:38 PM Douglas N. Greve <dgreve@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 "secure-web.cisco.com" claiming to be https://surfer.nmr.mgh.harvard.edu/fswiki/FsgdExamples

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 <dgreve@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 age gender race eTIV

Input sub001_base Control 85 Female White 1350519

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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