AFNI Analysis: Choosing stim_times_AM1 vs. stim_times_AM2 and Using dmUBLOCK() for Modeling Condition Differences with Duration and Preference Ratings

AFNI Message Board
12

Hi Shuning,

  1. Comparing Conditions without Covariates
    If you’re comparing conditions without including the preference score as a covariate, use the -stim_times option rather than -stim_times_AM1.

  2. Including Preference Score as a Covariate
    When considering the preference score as a covariate, first ask whether the condition causally affects the preference score. If so, use the -stim_times_AM2 option. You could input mean-centered covariate values, as you demonstrated. On the other hand, 3dDeconvolve will automatically mean-center these values for you.

  3. Intercept Effects and Mean-Centering
    The estimated effects for the intercept (mean-centered covariate) from option (2) will not match exactly with those from (1) but should be very similar.

  4. Assessing Effects Across Runs
    If your goal is to evaluate effects across multiple runs rather than within individual runs, handle the covariate values consistently across runs and allow 3dDeconvolve to perform mean-centering. Note that the BOLD response estimate associated with the intercept (mean-centered covariate value of zero) reflects the BOLD response at the mean covariate level, not the baseline of the experiment.

  5. Mediator Role of the Covariate
    If the condition causally impacts the covariate (preference score), then the covariate serves as a mediator. If the main research interest is in the condition effect, it’s not essential to include the mediator as a covariate. However, if you do include it, mean-centering is critical. This distinction is covered in more detail here, which also explains why methods (1) and (2) yield similar results. Importantly, there is no rationale for orthogonalization in this context.

  6. Explanation of TENTzero(0,16,9) Nodes and Intervals
    With TENTzero(0,16,9), there are 9 nodes (or knots) across a 16-second window, resulting in 8 intervals. The first and last nodes are set to zero, so only 7 \beta coefficients are estimated.

  7. Slice Timing Adjustment in fmriprep
    I’m not familiar with the slice-timing adjustment specifics in fmriprep; if it adjusts timing to the midpoint of each TR, consider using -stim_times_subtract in 3dDeconvolve to account for this.

Gang Chen