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Hello,
I've been analyzing a dataset consisting of two groups (control, clinical), each with two timepoints (tp1, tp2), following FreeSurfer's LME longitudinal analysis pipeline, to assess cortical thickness differences.
Given the relative simplicity of my data, I've only specified a single random effect (intercept). In the FreeSurfer section on spatiotemporal models, it begins with "*Spatiotemporal models are more powerful to detect effects in your data than traditional vertex-wise models when two or more random effects are included in the longitudinal statistical model.*" However, I've currently been using the spatiotemporal model approach (lme_mass_fit_EMinit, lme_mass_RgGrow, lme_mass_fit_Rgw) with the single random effect, but am curious if this is inappropriate and instead need to use the mass-univariate (lme_mass_fit_vw) workflow?
Dan
Hi Dan,
Also for 1 random effect you probably have a benefit using the region approach rather than vertex wise.
Best, Martin
On 25. Jun 2024, at 17:47, Dan Levitas djlevitas208@gmail.com wrote:
Hello,
I've been analyzing a dataset consisting of two groups (control, clinical), each with two timepoints (tp1, tp2), following FreeSurfer's LME longitudinal analysis pipeline, to assess cortical thickness differences.
Given the relative simplicity of my data, I've only specified a single random effect (intercept). In the FreeSurfer section on spatiotemporal models, it begins with "Spatiotemporal models are more powerful to detect effects in your data than traditional vertex-wise models when two or more random effects are included in the longitudinal statistical model." However, I've currently been using the spatiotemporal model approach (lme_mass_fit_EMinit, lme_mass_RgGrow, lme_mass_fit_Rgw) with the single random effect, but am curious if this is inappropriate and instead need to use the mass-univariate (lme_mass_fit_vw) workflow?
Dan _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
External Email - Use Caution
Thanks, Martin!
Dan
On Wed, Jun 26, 2024 at 7:04 AM Reuter, Martin,Ph.D. < MREUTER@mgh.harvard.edu> wrote:
Hi Dan,
Also for 1 random effect you probably have a benefit using the region approach rather than vertex wise.
Best, Martin
On 25. Jun 2024, at 17:47, Dan Levitas djlevitas208@gmail.com wrote:
Hello,
I've been analyzing a dataset consisting of two groups (control, clinical), each with two timepoints (tp1, tp2), following FreeSurfer's LME longitudinal analysis pipeline, to assess cortical thickness differences.
Given the relative simplicity of my data, I've only specified a single random effect (intercept). In the FreeSurfer section on spatiotemporal models, it begins with "*Spatiotemporal models are more powerful to detect effects in your data than traditional vertex-wise models when two or more random effects are included in the longitudinal statistical model.*" However, I've currently been using the spatiotemporal model approach (lme_mass_fit_EMinit, lme_mass_RgGrow, lme_mass_fit_Rgw) with the single random effect, but am curious if this is inappropriate and instead need to use the mass-univariate (lme_mass_fit_vw) workflow?
Dan _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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Dan Levitas -
Reuter, Martin,Ph.D.