Hi Freesurfer experts,
I am trying to look at differences in cortical thickness between the following three groups : (1) MCI, (2) AD, and (3) control group using mri_glmfit (DODS), and want to control for age (continuous) and gender (categorical).
I have read some other posts on the topic of demeaning age (i.e. subtracting each participant's age from the entire sample's mean age) but I am not sure if I will need it/am on the right track so would like to seek some guidance from you.
1) In order to decide whether I should demean age when including age as a covariate in the fsgd file (i.e. using demeaned age instead of participants' 'real' age) and control for gender, I should include gender as a categorical factor and check if there are any interactions between groups (MCI/AD/Control) x gender x age by running the following:
Research question:
Are the three groups different in terms of cortical thickness, when controlling for age and gender
Groups:
FemaleMCI
MaleMCI
FemaleAD
MaleAD
FemaleControl
MaleControl
Contrast:
0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 0 0 for group (i.e. MCI versus AD) x gender x age interaction
0 0 0 0 0 0 0.5 -0.5 0 0 -0.5 0.5 for group (i.e. MCI versus control) x gender x age interaction, and
0 0 0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 for group (i.e. AD versus control) x gender x age interaction
If none of these is significant, then re-run the analyses using DODS/fsgd file with contrasts of interest e.g.:
0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 for differences between MCI and AD. However, if there's there're any significant interactions, I will have to demean age? Is this correct? Or do i have to check for interactions in the first place at all given my research question? Also, say if there's a gender effect, how should I control for it?
2) On a separate question, if I want to correlate disease groups (MCI/AD) with behavioural data such as depression - i.e. to see if cortical thickness in MCI/AD is correlated with depression, do i just have to create fsgd files with only one group (MCI OR AD) in each file and correlate with depression by running contrast [0 1]? Sorry about this silly question, just wanted to double check as I am new to freesurfer!
Thanks for your help in advance!
Regards,
Jen
On 12/21/2016 09:30 PM, Jennifer Szeto wrote:
Hi Freesurfer experts,
I am trying to look at differences in cortical thickness between the following three groups : (1) MCI, (2) AD, and (3) control group using mri_glmfit (DODS), and want to control for age (continuous) and gender (categorical).
I have read some other posts on the topic of demeaning age (i.e. subtracting each participant's age from the entire sample's mean age) but I am not sure if I will need it/am on the right track so would like to seek some guidance from you.
- In order to decide whether I should demean age when including age
as a covariate in the fsgd file (i.e. using demeaned age instead of participants' 'real' age) and control for gender, I should include gender as a categorical factor and check if there are any interactions between _groups (MCI/AD/Control) x gender x age_ by running the following:
Research question:
Are the three groups different in terms of cortical thickness, when controlling for age and gender
Groups:
FemaleMCI
MaleMCI
FemaleAD
MaleAD
FemaleControl
MaleControl
Contrast:
0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 0 0 for group (i.e. MCI versus AD) x gender x age interaction
0 0 0 0 0 0 0.5 -0.5 0 0 -0.5 0.5 for group (i.e. MCI versus control) x gender x age interaction, and
0 0 0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 for group (i.e. AD versus control) x gender x age interaction
If none of these is significant, then re-run the analyses using DODS/fsgd file with contrasts of interest e.g.:
0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 for differences between MCI and AD. However, if there's there're any significant interactions, I will have to demean age? Is this correct? Or do i have to check for interactions in the first place at all given my research question? Also, say if there's a gender effect, how should I control for it?
If there is an interaction, then there is a problem that demeaning won't fix. The problem is that the results change depending upon the mean of the covariate. Demeaning just changes the mean to 0. You can still do the test, but the interpretation depends on the mean you select. Eg, you can change the sign of the effect by selecting a differnet mean. If other researches chose a different mean, then there will be differences. You should be ok with gender because it is a categorical variable.
- On a separate question, if I want to correlate disease groups
(MCI/AD) with behavioural data such as depression - i.e. to see if cortical thickness in MCI/AD is correlated with depression, do i just have to create fsgd files with only one group (MCI OR AD) in each file and correlate with depression by running contrast [0 1]? Sorry about this silly question, just wanted to double check as I am new to freesurfer!
Yes, that will work
Thanks for your help in advance!
Regards,
Jen
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Hi Doug,
Sorry just want to double check - so in order to control for age and gender by including age and gender as nuisance covariates, do I just simply have to include gender as a categorical variable and age as participants' 'real' age in my fsgd file when looking at cortical thickness differences in the MCI/AD/control group?
Thank you very much! Really appreciate it!
Jen
On 23 Dec. 2016, at 6:41 am, Douglas N Greve greve@nmr.mgh.harvard.edu wrote:
On 12/21/2016 09:30 PM, Jennifer Szeto wrote:
Hi Freesurfer experts,
I am trying to look at differences in cortical thickness between the following three groups : (1) MCI, (2) AD, and (3) control group using mri_glmfit (DODS), and want to control for age (continuous) and gender (categorical).
I have read some other posts on the topic of demeaning age (i.e. subtracting each participant's age from the entire sample's mean age) but I am not sure if I will need it/am on the right track so would like to seek some guidance from you.
- In order to decide whether I should demean age when including age
as a covariate in the fsgd file (i.e. using demeaned age instead of participants' 'real' age) and control for gender, I should include gender as a categorical factor and check if there are any interactions between _groups (MCI/AD/Control) x gender x age_ by running the following:
Research question:
Are the three groups different in terms of cortical thickness, when controlling for age and gender
Groups:
FemaleMCI
MaleMCI
FemaleAD
MaleAD
FemaleControl
MaleControl
Contrast:
0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 0 0 for group (i.e. MCI versus AD) x gender x age interaction
0 0 0 0 0 0 0.5 -0.5 0 0 -0.5 0.5 for group (i.e. MCI versus control) x gender x age interaction, and
0 0 0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 for group (i.e. AD versus control) x gender x age interaction
If none of these is significant, then re-run the analyses using DODS/fsgd file with contrasts of interest e.g.:
0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 for differences between MCI and AD. However, if there's there're any significant interactions, I will have to demean age? Is this correct? Or do i have to check for interactions in the first place at all given my research question? Also, say if there's a gender effect, how should I control for it?
If there is an interaction, then there is a problem that demeaning won't fix. The problem is that the results change depending upon the mean of the covariate. Demeaning just changes the mean to 0. You can still do the test, but the interpretation depends on the mean you select. Eg, you can change the sign of the effect by selecting a differnet mean. If other researches chose a different mean, then there will be differences. You should be ok with gender because it is a categorical variable.
- On a separate question, if I want to correlate disease groups
(MCI/AD) with behavioural data such as depression - i.e. to see if cortical thickness in MCI/AD is correlated with depression, do i just have to create fsgd files with only one group (MCI OR AD) in each file and correlate with depression by running contrast [0 1]? Sorry about this silly question, just wanted to double check as I am new to freesurfer!
Yes, that will work
Thanks for your help in advance!
Regards,
Jen
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Yes, but you need to make sure that there are no interactions between your categorical variable of interest and age by running the DODS model. If there are no significant interactions, then test for a difference in group by rerunning with DOSS (will need a new contrast matrix).
On 12/22/2016 04:02 PM, Jennifer Szeto wrote:
Hi Doug,
Sorry just want to double check - so in order to control for age and gender by including age and gender as nuisance covariates, do I just simply have to include gender as a categorical variable and age as participants' 'real' age in my fsgd file when looking at cortical thickness differences in the MCI/AD/control group?
Thank you very much! Really appreciate it!
Jen
On 23 Dec. 2016, at 6:41 am, Douglas N Greve greve@nmr.mgh.harvard.edu wrote:
On 12/21/2016 09:30 PM, Jennifer Szeto wrote:
Hi Freesurfer experts,
I am trying to look at differences in cortical thickness between the following three groups : (1) MCI, (2) AD, and (3) control group using mri_glmfit (DODS), and want to control for age (continuous) and gender (categorical).
I have read some other posts on the topic of demeaning age (i.e. subtracting each participant's age from the entire sample's mean age) but I am not sure if I will need it/am on the right track so would like to seek some guidance from you.
- In order to decide whether I should demean age when including age
as a covariate in the fsgd file (i.e. using demeaned age instead of participants' 'real' age) and control for gender, I should include gender as a categorical factor and check if there are any interactions between _groups (MCI/AD/Control) x gender x age_ by running the following:
Research question:
Are the three groups different in terms of cortical thickness, when controlling for age and gender
Groups:
FemaleMCI
MaleMCI
FemaleAD
MaleAD
FemaleControl
MaleControl
Contrast:
0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 0 0 for group (i.e. MCI versus AD) x gender x age interaction
0 0 0 0 0 0 0.5 -0.5 0 0 -0.5 0.5 for group (i.e. MCI versus control) x gender x age interaction, and
0 0 0 0 0 0 0 0 0.5 -0.5 -0.5 0.5 for group (i.e. AD versus control) x gender x age interaction
If none of these is significant, then re-run the analyses using DODS/fsgd file with contrasts of interest e.g.:
0.5 0.5 -0.5 -0.5 0 0 0 0 0 0 0 0 for differences between MCI and AD. However, if there's there're any significant interactions, I will have to demean age? Is this correct? Or do i have to check for interactions in the first place at all given my research question? Also, say if there's a gender effect, how should I control for it?
If there is an interaction, then there is a problem that demeaning won't fix. The problem is that the results change depending upon the mean of the covariate. Demeaning just changes the mean to 0. You can still do the test, but the interpretation depends on the mean you select. Eg, you can change the sign of the effect by selecting a differnet mean. If other researches chose a different mean, then there will be differences. You should be ok with gender because it is a categorical variable.
- On a separate question, if I want to correlate disease groups
(MCI/AD) with behavioural data such as depression - i.e. to see if cortical thickness in MCI/AD is correlated with depression, do i just have to create fsgd files with only one group (MCI OR AD) in each file and correlate with depression by running contrast [0 1]? Sorry about this silly question, just wanted to double check as I am new to freesurfer!
Yes, that will work
Thanks for your help in advance!
Regards,
Jen
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
freesurfer@nmr.mgh.harvard.edu
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Douglas N Greve -
Jennifer Szeto