Dear FSL people (esp. Christian Beckmann, I'd say),
With regards to the recently developed dual_regression approach to
back-reconstruction of individual maps and timecourses corresponding to
group ICA maps, the examination of the fsl shell script made me think about
some issues that I would very much like to have a more expert opinion about.
Here they are:
1) For a group-ICA dual-regression backrecon, the input to the first phase
of the regression (spatial GLM) are the group IC Z-maps, where the statistic
z-value is computed according to the PICA framework (basically a shifted and
rescaled version of the raw ICA spatial modes). Now, the script demeans the
maps (and the EPI data) but does not standardize them (divide by st.dev.).
It would make sense to me to standardize the regressors and indeed in the
subsequent GLM phase (temporal regression) the reconstructed timecourses
that constitute the design matrix *are* standardized. Is there a reason why
this is omitted in the spatial GLM? Also, it seems to me that both the
standardization of the regressor maps and the GLM fitting would be best
performed within a brain-only mask, to avoid brain-nonbrain differences in
intensity soaking up the bulk of the variance. Correct?
2) If one is interested in examining the spectra of the reconstructed time
course, a common approach is to standardize the individual time courses (to
have zero mean and unit st.dev.), so that the computed spectra will all have
unit variance, before computing group means and between-group statistics on
these PSD. However, I have some concerns that in the specific case of the
dual-regression method we may be throwing away useful information by doing
so. Since the reconstructed time courses from the spatial GLM represent
individual "beta values", if we want to do a group-level analysis on the
spectra, wouldn't we want to use the spectra from the timecourses *as they
are* (that is, not-standardized)? Because if we standardize the time courses
(i.e., equalize total energy across components and subjects), we lose the
information about the relative importance (both within and between subjects)
of those estimated individual time courses in explaining the group ICA maps.
Does this make sense?
3) Before using reconstructed individual spatial maps and time courses as
input to a group analyses, it would be better to scale them according to the
intensity of original EPI images to remove this confounding between-subject
variance. The simplest approach (a la SPM) would be to compute the grand
mean of the original EPI data for each subject (mean over time and voxels)
and transform the individual reconstructed spatial maps and time courses as
percent change with respect to the subject's EPI grand mean. Alternatively,
one could scale the timecourses with respect to the grand mean and the
spatial maps with respect to the EPI mean image (voxel-wise scaling),
although the first approach may be better for consistency. Is this correct?
4) If point 2) is correct, then I would think it would be better to take the
*mean* of the individual (not normalized) spectra as a group representative
instead of its rank-1 approximation (as was done e.g. in Kiviniemi2009),
which if I understand it correctly discards information about total energy.
Pardon the lengthy post and thanks in advance for any comments,
best regards,
giuseppe
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