Dear all,
I am attempting to do a group level analysis using Melodic and have a few
questions regarding the appropriate (if any) procedures. I have read
through the listed posts but still have some lingering thoughts.
1. I have discovered that if I perform normalization of my subjects' data
(they are partial-brain volumes) to MNI space before using melodic, I get
far more, and less interpretable, components (on the order of 500
components for a 1000 volume dataset) than if I use melodic before any kind
of normalization (in which case, I get 100-200 components on the 1000 volume
dataset). I have also found that smoothing in addition to normalization
before using Melodic only exacerbates this problem. The normalization
process appears to be functioning properly based on visual inspection of the
normalized volumes. Why should spatial normalization, or smoothing for that
matter, cause such a dramatic boost in the dimensionality of the output of
Melodic? Does this mean that I should employ normalization only on the
output of Melodic if I want to bring this information to a group level analysis?
2. I am employing an external metric for selecting components of interest
within an individual subject's Melodic output. The selected components may
be anyone and any amount of the components in a given individual. I then
concatenate all the selected components (from the spatial maps in
melodic_IC) from each subject (the number of selected spatial maps varies
from subject to subject) into a 4D dataset and use Melodic once again in
order to identify components which represent spatial consistency of the
selected components across subjects. Is this method an appropriate use of
Melodic and if so, how can I interpret the statistics that Melodic outputs?
Thanks so much! I appreciate any help or advice you can give.
Sincerely,
Tim
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