So I created a weighted regression analysis to look at the effect of memory
load in a particular brain region. Basically, I weighted the paradigms by a
behavioral measure that reflected the number of items actually remembered
(as set size was increased). As far as Doug told me there are basically 2
ways to weight your paradigm files.
Version 1:
Have 2 conditions, baseline (condition 0) and all the set sizes (condition
1). Condition 1 would then be weighted by the behavioral measure.
Version 2:
Have 3 conditions, baseline (condition 0), and then I represented each
presentation as two different conditions, one with a weight that is always 1
(condition 1), the other weighted according to the behavioral measure
(condition 2).
The difference, as far as I understand it, in version 1, it is assumed that
the response amplitude is ) when the weight is 0 (ie. that when you are
attending to 0 items, brain activity = 0). Whereas, version 2, tests the
slope of the HRF amplitude vs weight without the assumption above.
However, I'm a bit confused as to the results I got. When I looked at the
data from both versions, version 1 provided a much higher amount of
activation and more areas activated than version 2. However, I believe
version 2 better fits with the multiple regression analysis that is done in
Brain Voyager.
Can anyone give me a better explanation of what the difference between these
analysis models is?
Katie