I came accross two ways of doing ICA with fMRI data. In one method, ICA is
performed directly on the fMRI time series data resulting in components.
The ICA derived components are then correlated with the design matrix to
identify the task-related components. In the other method the fMRI times
series data is cross correlated with the design matrix and the ICA is
performed on the result of the cross correlation to yield eigenimages. I
think both methods could be implemented in FEAT. Are there any suggestions
about which might be a better method? Is there any other method that is
commonly used?
My other question is about how to do an ICA on a group of subjects.
The following is a well established (though not very common) procedure
in the event-related brain potential literature. Has a similar procedure
been used in the fMRI literature? Is it (or: how much of it is) implemented
in FSL? If this procedure is not used then what is usually recommended for
doing ICA on a group of subjects?
Assume that the data set for the entire experiment consists of
multiple subjects, multiple conditions per subject, multiple electrodes,
and multiple time-point observations for each electrode. (The electrode
can be seen as corresponding to MR voxels, and the time points to the MR
time series across TRs.)
Put all of the data into a single matrix, with columns = time
points and rows = everything else, not distinguishing subjects,
conditions, or electrodes. Conduct a PCA or ICA, producing factors and
factor loadings as a function of time.
For the time-point vector in each subject x condition x electrode
cell, apply the factor loading to the vector, producing a factor
score. The factor scores are suitable dependent variables in an ANOVA
(or whatever). The ANOVA could have condition and electrode as factors,
with subjects as the within-cell replications to estimate
error. Rejecting the null hypothesis means that the factor scores vary
systematically as a function of conditions and/or electrodes.
All of the above can also be done by exchanging electrodes and time
points, so that with columns = electrodes and rows = everything
else. The result is that the factor loadings are a function of electrode
location rather than time.
thanks
Appu Mohanty
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