Hi FSL users
I have run a total of three fMRI studies for my PhD and I am in the process
of re-analysing to write them up.
A lot of my work requires extraction of effect sizes in different ROIs and
then running stats on these values - generally I have run functional
localisers to create functional ROIs and then queried subsequent memory
effects across a variety of conditions against an active baseline condition
within these.
For example
objects later remebered - active baseline
objects later forgotten - active baseline
I orginally extracted the PEs (betas) using Featquery, however I have
recently come across some literature that suggests that you shouldn't use PE
for your stats, instead you should use % signal change (that said I have
seen plenty of recent published papers in decent journals that have used FSL
PEs).
First question is whether I can stick with PEs for my stats?
Secondly I have also extracted % signal change for my data and have already
seen there is not a simple one to one mapping to PE. For two of my data
sets the numerical patterns are pretty much the same but for another it only
appears to have an effect on one of the contrasts I am interested in looking
at. Surely if the baseline used to covert these %s is the same across
conditions (within participants) why would these conversions have a greater
effect on one contrast? Obviously if it ok for me to use PE then this is so
much of an issue
Thirdly, I have checked the FSL Feat manual and it says that you cannot use
the 'covert PE into % signal change' option in Featquery for event related
designs (which all of my experiments are) as this assumes the height of the
waveform is 1, which is only appropriate for blocked designs. Is there a
simple way to calculate % signal change using my PEs - say a formula and
somewhere I can extract a waveform height value?
I am desperately confused and there is quite a lot riding on this so any
help at all will be greatly appreciated. Let me know if you need me to
clarify anything.
Thanks in advance
Hilary
|