From speaking with others it sounds like most people using Melodic to remove
unwanted noise components from motion artefacts, whereas the second
function of Melodic in revealing unexpected activation is much less commonly
Since I am conducting an exploratory study that is less heavily hypothesis-
driven, I'm assuming that in this case, it may be useful to look at Melodic for
task-related activation patterns.
1) I would like to first verify my Melodic setup:
I have 10 subjects, same stimulus timing across all subjects. However 5
subjects see the sequence of images in one order, and the other 5 see the
images in the reverse sequence. Within each group of 5, 2 subjects see the
image 1 under condition A (1-A), image 2 under condition B (2-B), image 3
under condition B (3-B), etc. The 3 other subjects are given the opposite
conditions: 1-B, 2-A, 3-A, etc. Should I run 4 Multi-session Tensor-ICAs?
To reveal any task-related activations, do I just go to Post-stats tab of the
Melodic Gui and load an individual subject's design.mat file for the time-series
model field and a design.con file for the time-series contrast field? Should I load
anything for the session/subjects model/contrasts fields?
2) Can you explain how Melodic reveals task-related activation that a Feat
analysis would not have?
Thank you very much for your help!