Hi there,
After conducting a DODS analysis (One group, four factors, one covariate), we were able to identify a region (LH pars opercularis) whose mean thickness differed when two of the four factors interacted. In order to further investigate the interaction, we obtained each participant's mean thickness by drawing an ROI (using QDEC, which also applied the mri_label2label function to map the new ROI to all the participants).
However, when we extract the thickness data in this way, and then run a glm on the ROI data (with the same four factors), the interaction is no longer significant, and instead we observe a main effects of only one of the factors.
Does anyone have an idea why there is such a disconnect between the freesurfer output and the subsequent analysis (using glm in R)?
THANKS!!
clint
Are you sure your R design matrix and contrasts are consistent with FS? Also, something could have gone wrong in the transfer to each subject. I would recommend extracting the data directly out of the input file (y.mgh) using mri_segstats with --slabel, especially if the label is small.
On 03/28/2014 12:15 PM, Clint Johns wrote:
Hi there,
After conducting a DODS analysis (One group, four factors, one covariate), we were able to identify a region (LH pars opercularis) whose mean thickness differed when two of the four factors interacted. In order to further investigate the interaction, we obtained each participant's mean thickness by drawing an ROI (using QDEC, which also applied the mri_label2label function to map the new ROI to all the participants).
However, when we extract the thickness data in this way, and then run a glm on the ROI data (with the same four factors), the interaction is no longer significant, and instead we observe a main effects of only one of the factors.
Does anyone have an idea why there is such a disconnect between the freesurfer output and the subsequent analysis (using glm in R)?
THANKS!!
clint
We are reasonably certain that the R design matrix matches but I will certainly double check and report back. I also wondered about the transfer of the label to each subject, and wanted to use mri_segstats... but since we didn't use qdec, we don't seem to have a y.mgh input file, and I'm not sure what we should use in its place. (The label is on the small side, to be sure.) If you could tell us what to use instead, I'd be grateful. (We do have a y.fsgd file, but that didn't do the job for obvious reasons. We looked at the examples on the mri_segstats page on the wiki, but the examples did not specify the kind of input file to use in this case.)
We really appreciate your advice!
THANKS.
clint
On Fri, Mar 28, 2014 at 1:37 PM, Douglas N Greve greve@nmr.mgh.harvard.edu wrote:
Are you sure your R design matrix and contrasts are consistent with FS? Also, something could have gone wrong in the transfer to each subject. I would recommend extracting the data directly out of the input file (y.mgh) using mri_segstats with --slabel, especially if the label is small.
On 03/28/2014 12:15 PM, Clint Johns wrote:
Hi there,
After conducting a DODS analysis (One group, four factors, one covariate), we were able to identify a region (LH pars opercularis) whose mean thickness differed when two of the four factors interacted. In order to further investigate the interaction, we obtained each participant's mean thickness by drawing an ROI (using QDEC, which also applied the mri_label2label function to map the new ROI to all the participants).
However, when we extract the thickness data in this way, and then run a glm on the ROI data (with the same four factors), the interaction is no longer significant, and instead we observe a main effects of only one of the factors.
Does anyone have an idea why there is such a disconnect between the freesurfer output and the subsequent analysis (using glm in R)?
THANKS!!
clint
-- 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.
If you used mri_glmfit, just use whatever you passed as --y doug
On 03/28/2014 02:06 PM, Clint Johns wrote:
We are reasonably certain that the R design matrix matches but I will certainly double check and report back. I also wondered about the transfer of the label to each subject, and wanted to use mri_segstats... but since we didn't use qdec, we don't seem to have a y.mgh input file, and I'm not sure what we should use in its place. (The label is on the small side, to be sure.) If you could tell us what to use instead, I'd be grateful. (We do have a y.fsgd file, but that didn't do the job for obvious reasons. We looked at the examples on the mri_segstats page on the wiki, but the examples did not specify the kind of input file to use in this case.)
We really appreciate your advice!
THANKS.
clint
On Fri, Mar 28, 2014 at 1:37 PM, Douglas N Greve greve@nmr.mgh.harvard.edu wrote:
Are you sure your R design matrix and contrasts are consistent with FS? Also, something could have gone wrong in the transfer to each subject. I would recommend extracting the data directly out of the input file (y.mgh) using mri_segstats with --slabel, especially if the label is small.
On 03/28/2014 12:15 PM, Clint Johns wrote:
Hi there,
After conducting a DODS analysis (One group, four factors, one covariate), we were able to identify a region (LH pars opercularis) whose mean thickness differed when two of the four factors interacted. In order to further investigate the interaction, we obtained each participant's mean thickness by drawing an ROI (using QDEC, which also applied the mri_label2label function to map the new ROI to all the participants).
However, when we extract the thickness data in this way, and then run a glm on the ROI data (with the same four factors), the interaction is no longer significant, and instead we observe a main effects of only one of the factors.
Does anyone have an idea why there is such a disconnect between the freesurfer output and the subsequent analysis (using glm in R)?
THANKS!!
clint
-- 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@nmr.mgh.harvard.edu
-
Clint Johns -
Douglas N Greve