Thanks, Martin. This helps a lot.

Swati

____________________________________________

Swati Rane Levendovszky, PhD

Interim Director, Diagnostic Imaging Sciences Center

Assistant Professor, Integrated Brain Imaging Center

Radiology

University of Washington

Box 357115 | AA035 HSB | Seattle, WA 98195

srleven@uw.edu | Ph: 206 543 6159
Web: https://sites.google.com/site/uwswatirane/
____________________________________________

From: <freesurfer-bounces@nmr.mgh.harvard.edu> on behalf of Martin Reuter <mreuter@nmr.mgh.harvard.edu>
Reply-To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Date: Friday, January 17, 2020 at 3:46 AM
To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Subject: Re: [Freesurfer] mri_glmfit-sim for longitudinal pipelines

Hi Swati,

- yes, you can treat the percent change maps (PC1) as the signal instead of the thickness and do a regular analysis (so either multiple comparisons or sim etc).

- and yes, I would always recommend to use the linear mixed effects model instead as it is specifically designed for longitudinal data. The percent change (2 step approach) is a remainder of the time before we had the LME. Both models are similar only if the longitudinal data has the same number of time points for each subject and if those time points are equally spaced. That is rarely the case and LME can account for that.

Best, Martin

On 17. Jan 2020, at 00:59, Swati Rane <srleven@uw.edu> wrote:
External Email - Use Caution

It is the file computed longitudinal processing that represents the rate with respect to the thickness at the first time point computed by long_mris_slopes.

Swati

____________________________________________

Swati Rane Levendovszky, PhD

Interim Director, Diagnostic Imaging Sciences Center

Assistant Professor, Integrated Brain Imaging Center

Radiology

University of Washington

Box 357115 | AA035 HSB | Seattle, WA 98195

srleven@uw.edu | Ph: 206 543 6159
Web: https://sites.google.com/site/uwswatirane/
____________________________________________

From: <freesurfer-bounces@nmr.mgh.harvard.edu> on behalf of "Greve, Douglas N.,Ph.D." <DGREVE@mgh.harvard.edu>
Reply-To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Date: Thursday, January 16, 2020 at 3:26 PM
To: "freesurfer@nmr.mgh.harvard.edu" <freesurfer@nmr.mgh.harvard.edu>
Subject: Re: [Freesurfer] mri_glmfit-sim for longitudinal pipelines

Sorry, what is "pc1"?

On 1/14/2020 9:44 AM, Swati Rane wrote:
External Email - Use Caution

Hello,

I was wondering if someone could answer my question below

Can I use mri_glmfit-sim after mri_glmfit on the longitudinal pc1 files.

Are there any set of parameters that should be set differently when using pc1 instead of thickness files?

Thanks!

Swati

____________________________________________

Swati Rane Levendovszky, PhD

Interim Director,Diagnostic Imaging Sciences Center

Assistant Professor,Integrated Brain Imaging Center

Radiology

University of Washington

Box 357115 | AA035 HSB | Seattle, WA 98195

srleven@uw.edu | Ph: 206 543 6159
Web: https://sites.google.com/site/uwswatirane/
____________________________________________

From: <freesurfer-bounces@nmr.mgh.harvard.edu> on behalf of Swati Rane <srleven@uw.edu>
Reply-To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Date: Friday, January 10, 2020 at 10:06 AM
To: "freesurfer@nmr.mgh.harvard.edu" <freesurfer@nmr.mgh.harvard.edu>
Subject: [Freesurfer] mri_glmfit-sim for longitudinal pipelines

External Email - Use Caution

Hello FreeSurfer Developers,

I'm attempting to compare longitudinal cortical thinning rates (pc1) between 3 groups using mri_glmfit using FreeSurfer 6.0. The tutorial suggests that you could use the multiple comparisons corrections that come with it but does not comment on using the mri_glmfit-sim option.

I just want to confirm whether there is anything prohibiting me from using mri_glmfit-sim. Are there any set of parameters that should be set differently when using pc-1 instead of thickness files?

Also, would you do a stricter correction and stick to the longitudinal model or prefer the linear mixed effects model and do a FDR? How do you decide between the two?

Thanks!

Swati
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