Hello,

I am having difficulty deciding the best way to control for covariates in my analysis in my within-subject experiment. I want to identify the effects of Time on brain activity and control for some covariates. I have four conditions of interest (Time1, Time2, Time3, and Time4) and three nuisance covariates (Confidence, Response Time, and Accuracy). The covariates change as a function of Time. That is why they are nuisances.

I have determined that 3dLME would be ideal for this analysis. In this case, the covariates (which vary across my conditions of interest) will be included in order to look for changes in brain activity as a function of Time (4 conditions) independent of the effects of the covariates. This analysis would control for the covariates at the level of the conditions and not at the trial level.

I am also wondering if an amplitude modulated 3dDeconvolve analysis offers a possibility of controlling for the nuisance covariates at the trial level. In this case, I would create 4 stim files, one for each condition of interest. Then I would marry the three covariates to each trial for each condition. In this case, each condition would produce 4 outputs: the unmodulated effect, the effect modulated by Confidence, the effect modulated by Response Time, and the effect modulated by Accuracy.

In this case, does the unmodulated effect I would obtain for each condition of interest represent the effect while controlling for the nuisance covariates? If so, maybe I don't need to turn to LME to analyze these data. Or does the unmodulated effect represent the effect for each condition while ignoring the nuisance covariates (i.e. pretending they had not been included in the model).

If the former is true, I am wondering whether you would recommend 3dLME or an amplitude modulated analysis for these data.

Best,
Christine