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

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