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Hello,
I am trying to do GLM analysis for cortical thickness with a behavioral score. I wanted to explore square fit in the GLM. Basically wanted to see weather the behaviour is related in a quadratic function instead of linear one.
Couldn't find any option for this is GLM fit.
I was thinking of feeding a squareroot values of the behavioural parameter in the GLM as an alternative. But not sure weather that will work. Please any one can help me with this.
Thanks, Abhinav
It sounds like you will just need to create your own design matrix and then feed it into mri_glmfit with --X (and not include --fsgd)
On 2/12/19 12:03 AM, Abhinav Yadav wrote:
External Email - Use Caution
Hello,
I am trying to do GLM analysis for cortical thickness with a behavioral score. I wanted to explore square fit in the GLM. Basically wanted to see weather the behaviour is related in a quadratic function instead of linear one.
Couldn't find any option for this is GLM fit.
I was thinking of feeding a squareroot values of the behavioural parameter in the GLM as an alternative. But not sure weather that will work. Please any one can help me with this.
Thanks, Abhinav
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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Hi Douglas,
Thanks for your reply. I am not sure how to do this with design matrix. I am trying to get higher order fit. Basically I want to see that whether the cortical thickness varies in a quadratic fashion with the behaviour score.
Are you are suggesting that I should put square root values of the behavioural scores in the design matrix? I am not sure if that is the right approach.
Looking forward to hearing back from you.
Abhinav
On Wed, 20 Feb 2019, 04:12 Greve, Douglas N.,Ph.D., DGREVE@mgh.harvard.edu wrote:
It sounds like you will just need to create your own design matrix and then feed it into mri_glmfit with --X (and not include --fsgd)
On 2/12/19 12:03 AM, Abhinav Yadav wrote:
External Email - Use CautionHello,
I am trying to do GLM analysis for cortical thickness with a behavioral score. I wanted to explore square fit in the GLM. Basically wanted to see weather the behaviour is related in a quadratic function instead of linear one.
Couldn't find any option for this is GLM fit.
I was thinking of feeding a squareroot values of the behavioural parameter in the GLM as an alternative. But not sure weather that will work. Please any one can help me with this.
Thanks, Abhinav
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
If you wanted to do a simple analysis looking at the effect of age in a linear fashion, you'd have a deisgn matrix something like 1 agesubject1 1 agesubject2 ...
ie, first column all ones, second column the ages of each subject. If you think the change is quadratic, then you'd use
1 agesubject1^2 1 agesubject2^2 ...
ie, first column all ones, second column the square of the ages of each subject.
Then use a contrast [0 1] to test whether the 2nd column regressor is diff than 0. (btw, you should demean your ages before squaring)
On 2/28/19 12:12 PM, Abhinav Yadav wrote:
External Email - Use Caution
Hi Douglas,
Thanks for your reply. I am not sure how to do this with design matrix. I am trying to get higher order fit. Basically I want to see that whether the cortical thickness varies in a quadratic fashion with the behaviour score.
Are you are suggesting that I should put square root values of the behavioural scores in the design matrix? I am not sure if that is the right approach.
Looking forward to hearing back from you.
Abhinav
On Wed, 20 Feb 2019, 04:12 Greve, Douglas N.,Ph.D., <DGREVE@mgh.harvard.edu mailto:DGREVE@mgh.harvard.edu> wrote:
It sounds like you will just need to create your own design matrix and then feed it into mri_glmfit with --X (and not include --fsgd) On 2/12/19 12:03 AM, Abhinav Yadav wrote: > > External Email - Use Caution > > Hello, > > I am trying to do GLM analysis for cortical thickness with a > behavioral score. I wanted to explore square fit in the GLM. Basically > wanted to see weather the behaviour is related in a quadratic function > instead of linear one. > > Couldn't find any option for this is GLM fit. > > I was thinking of feeding a squareroot values of the behavioural > parameter in the GLM as an alternative. But not sure weather that will > work. Please any one can help me with this. > > Thanks, > Abhinav > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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Hi Douglas,
Thanks. But I want to see the square root of the age because I am assuming that the brain feature (cortical thickness) is varying in a square fashion.
Do I have to put square root values? If yes then, how can I put negative square roots. Since we have to put demeaned values.
Best, Abhinav
On Fri, 1 Mar 2019, 01:44 Greve, Douglas N.,Ph.D., DGREVE@mgh.harvard.edu wrote:
If you wanted to do a simple analysis looking at the effect of age in a linear fashion, you'd have a deisgn matrix something like 1 agesubject1 1 agesubject2 ...
ie, first column all ones, second column the ages of each subject. If you think the change is quadratic, then you'd use
1 agesubject1^2 1 agesubject2^2 ...
ie, first column all ones, second column the square of the ages of each subject.
Then use a contrast [0 1] to test whether the 2nd column regressor is diff than 0. (btw, you should demean your ages before squaring)
On 2/28/19 12:12 PM, Abhinav Yadav wrote:
External Email - Use CautionHi Douglas,
Thanks for your reply. I am not sure how to do this with designmatrix. I am trying to get higher order fit. Basically I want to see that whether the cortical thickness varies in a quadratic fashion with the behaviour score.
Are you are suggesting that I should put square root values of thebehavioural scores in the design matrix? I am not sure if that is the right approach.
Looking forward to hearing back from you.
Abhinav
On Wed, 20 Feb 2019, 04:12 Greve, Douglas N.,Ph.D., <DGREVE@mgh.harvard.edu mailto:DGREVE@mgh.harvard.edu> wrote:
It sounds like you will just need to create your own design matrix and then feed it into mri_glmfit with --X (and not include --fsgd) On 2/12/19 12:03 AM, Abhinav Yadav wrote: > > External Email - Use Caution > > Hello, > > I am trying to do GLM analysis for cortical thickness with a > behavioral score. I wanted to explore square fit in the GLM. Basically > wanted to see weather the behaviour is related in a quadratic function > instead of linear one. > > Couldn't find any option for this is GLM fit. > > I was thinking of feeding a squareroot values of the behavioural > parameter in the GLM as an alternative. But not sure weather that will > work. Please any one can help me with this. > > Thanks, > Abhinav > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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just deman after you do the square root
On 3/1/19 3:49 AM, Abhinav Yadav wrote:
External Email - Use Caution
Hi Douglas,
Thanks. But I want to see the square root of the age because I am assuming that the brain feature (cortical thickness) is varying in a square fashion.
Do I have to put square root values? If yes then, how can I put negative square roots. Since we have to put demeaned values.
Best, Abhinav
On Fri, 1 Mar 2019, 01:44 Greve, Douglas N.,Ph.D., <DGREVE@mgh.harvard.edumailto:DGREVE@mgh.harvard.edu> wrote: If you wanted to do a simple analysis looking at the effect of age in a linear fashion, you'd have a deisgn matrix something like 1 agesubject1 1 agesubject2 ...
ie, first column all ones, second column the ages of each subject. If you think the change is quadratic, then you'd use
1 agesubject1^2 1 agesubject2^2 ...
ie, first column all ones, second column the square of the ages of each subject.
Then use a contrast [0 1] to test whether the 2nd column regressor is diff than 0. (btw, you should demean your ages before squaring)
On 2/28/19 12:12 PM, Abhinav Yadav wrote:
External Email - Use CautionHi Douglas,
Thanks for your reply. I am not sure how to do this with designmatrix. I am trying to get higher order fit. Basically I want to see that whether the cortical thickness varies in a quadratic fashion with the behaviour score.
Are you are suggesting that I should put square root values of thebehavioural scores in the design matrix? I am not sure if that is the right approach.
Looking forward to hearing back from you.
Abhinav
On Wed, 20 Feb 2019, 04:12 Greve, Douglas N.,Ph.D., <DGREVE@mgh.harvard.edumailto:DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edumailto:DGREVE@mgh.harvard.edu>> wrote:
It sounds like you will just need to create your own design matrix and then feed it into mri_glmfit with --X (and not include --fsgd) On 2/12/19 12:03 AM, Abhinav Yadav wrote: > > External Email - Use Caution > > Hello, > > I am trying to do GLM analysis for cortical thickness with a > behavioral score. I wanted to explore square fit in the GLM. Basically > wanted to see weather the behaviour is related in a quadratic function > instead of linear one. > > Couldn't find any option for this is GLM fit. > > I was thinking of feeding a squareroot values of the behavioural > parameter in the GLM as an alternative. But not sure weather that will > work. Please any one can help me with this. > > Thanks, > Abhinav > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu>> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edumailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edumailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edumailto:Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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In my case the behavioural values are already positive and negative. What about that.
On Fri, 1 Mar 2019, 23:58 Greve, Douglas N.,Ph.D., DGREVE@mgh.harvard.edu wrote:
just deman after you do the square root
On 3/1/19 3:49 AM, Abhinav Yadav wrote:
External Email - Use CautionHi Douglas,
Thanks. But I want to see the square root of the age because I amassuming that the brain feature (cortical thickness) is varying in a square fashion.
Do I have to put square root values? If yes then, how can I put negative square roots. Since we have to put demeaned values.
Best, Abhinav
On Fri, 1 Mar 2019, 01:44 Greve, Douglas N.,Ph.D., DGREVE@mgh.harvard.edu wrote:
If you wanted to do a simple analysis looking at the effect of age in a linear fashion, you'd have a deisgn matrix something like 1 agesubject1 1 agesubject2 ...
ie, first column all ones, second column the ages of each subject. If you think the change is quadratic, then you'd use
1 agesubject1^2 1 agesubject2^2 ...
ie, first column all ones, second column the square of the ages of each subject.
Then use a contrast [0 1] to test whether the 2nd column regressor is diff than 0. (btw, you should demean your ages before squaring)
On 2/28/19 12:12 PM, Abhinav Yadav wrote:
External Email - Use CautionHi Douglas,
Thanks for your reply. I am not sure how to do this with designmatrix. I am trying to get higher order fit. Basically I want to see that whether the cortical thickness varies in a quadratic fashion with the behaviour score.
Are you are suggesting that I should put square root values of thebehavioural scores in the design matrix? I am not sure if that is the right approach.
Looking forward to hearing back from you.
Abhinav
On Wed, 20 Feb 2019, 04:12 Greve, Douglas N.,Ph.D., <DGREVE@mgh.harvard.edu mailto:DGREVE@mgh.harvard.edu> wrote:
It sounds like you will just need to create your own design matrix and then feed it into mri_glmfit with --X (and not include --fsgd) On 2/12/19 12:03 AM, Abhinav Yadav wrote: > > External Email - Use Caution > > Hello, > > I am trying to do GLM analysis for cortical thickness with a > behavioral score. I wanted to explore square fit in the GLM. Basically > wanted to see weather the behaviour is related in a quadratic function > instead of linear one. > > Couldn't find any option for this is GLM fit. > > I was thinking of feeding a squareroot values of the behavioural > parameter in the GLM as an alternative. But not sure weather that will > work. Please any one can help me with this. > > Thanks, > Abhinav > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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if they are negative, it seems like a square root model is not appropriate
On 3/1/19 11:03 AM, Abhinav Yadav wrote:
External Email - Use Caution
In my case the behavioural values are already positive and negative. What about that.
On Fri, 1 Mar 2019, 23:58 Greve, Douglas N.,Ph.D., <DGREVE@mgh.harvard.edumailto:DGREVE@mgh.harvard.edu> wrote: just deman after you do the square root
On 3/1/19 3:49 AM, Abhinav Yadav wrote:
External Email - Use Caution
Hi Douglas,
Thanks. But I want to see the square root of the age because I am assuming that the brain feature (cortical thickness) is varying in a square fashion.
Do I have to put square root values? If yes then, how can I put negative square roots. Since we have to put demeaned values.
Best, Abhinav
On Fri, 1 Mar 2019, 01:44 Greve, Douglas N.,Ph.D., <DGREVE@mgh.harvard.edumailto:DGREVE@mgh.harvard.edu> wrote: If you wanted to do a simple analysis looking at the effect of age in a linear fashion, you'd have a deisgn matrix something like 1 agesubject1 1 agesubject2 ...
ie, first column all ones, second column the ages of each subject. If you think the change is quadratic, then you'd use
1 agesubject1^2 1 agesubject2^2 ...
ie, first column all ones, second column the square of the ages of each subject.
Then use a contrast [0 1] to test whether the 2nd column regressor is diff than 0. (btw, you should demean your ages before squaring)
On 2/28/19 12:12 PM, Abhinav Yadav wrote:
External Email - Use CautionHi Douglas,
Thanks for your reply. I am not sure how to do this with designmatrix. I am trying to get higher order fit. Basically I want to see that whether the cortical thickness varies in a quadratic fashion with the behaviour score.
Are you are suggesting that I should put square root values of thebehavioural scores in the design matrix? I am not sure if that is the right approach.
Looking forward to hearing back from you.
Abhinav
On Wed, 20 Feb 2019, 04:12 Greve, Douglas N.,Ph.D., <DGREVE@mgh.harvard.edumailto:DGREVE@mgh.harvard.edu <mailto:DGREVE@mgh.harvard.edumailto:DGREVE@mgh.harvard.edu>> wrote:
It sounds like you will just need to create your own design matrix and then feed it into mri_glmfit with --X (and not include --fsgd) On 2/12/19 12:03 AM, Abhinav Yadav wrote: > > External Email - Use Caution > > Hello, > > I am trying to do GLM analysis for cortical thickness with a > behavioral score. I wanted to explore square fit in the GLM. Basically > wanted to see weather the behaviour is related in a quadratic function > instead of linear one. > > Couldn't find any option for this is GLM fit. > > I was thinking of feeding a squareroot values of the behavioural > parameter in the GLM as an alternative. But not sure weather that will > work. Please any one can help me with this. > > Thanks, > Abhinav > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu>> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu<mailto:Freesurfer@nmr.mgh.harvard.edu>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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Abhinav Yadav -
Greve, Douglas N.,Ph.D.