Hello,
I am new to FSL and had a question about how to set up a parametric modulation design for a habituation experiment. So the experiment consists of 2 runs and each run has 3 blocks. The main block of interest is a passive fear task and we want to look at habituation over the course of 6 blocks. Specifically we are interested in seeing if the habituation is a linear trend or curvilinear (quadratic) trend.
Linear: 3 1.8 0.6 -0.6 -1.8 -3
Curvilinear (Positive U) : 3 -1 -2 -2 -1 3
Questions
1a) Should I model the habituation in the 3rd column of my ev file? I know in the 3 column format I could put my modulation vector in the last column but will I be able to do this for both the linear and curvilinear trend?
OR
1b) Should I simply model the linear and curvilinear trend under the STATS > Contrasts and F-Tests?
2) Also, how does one go about testing a linear versus a non-linear model? Should I run the group with both models and see which is more significant- we have reason to believe that we will see a āUā curve based on previous literature but want to also assess linear. After I rule out a model, should I then rerun the analysis with only one model.
3) We were also going to do an ROI analysis to get a better estimate of what the data curves look like in our two regions of interest. Would it be better to do this first to get an estimate and then just analyze with one model?
4) We are looking at this data in a two groups- what if one group shows linear and the other is curvilinear? How would the between groups stats be conducted in this event?
Thank you for your suggestions,
Nathan
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