Here is my attempt at a rational discussion of these issues based on
similar ones and ongoing work inside the HCP:

I would say that exactly how best to deal with motion confounds between
two groups remains an open research question and I would be concerned
about any reviewers that make prescriptive statements about these issues
(at the same time, the Power work and that of other groups has shown this
is an absolutely huge problem in neuroimaging, not just in fMRI but also
in diffusion, or even morphemetric structural analyses).  In particular
for fMRI, what techniques are needed when using different quality data
(e.g. ³standard² neuroimaging acquisitions with low spatial and temporal
resolution vs ³HCP-style² neuroimaging acquisitions with high spatial
resolution) are not yet clear.  A lot of this denoising work has been done
on standard neuroimaging data, but how much applies to HCP-style imaging
data has not yet been proven.

Particularly with HCP-syle imaging data, one won¹t be going wrong by
performing ICA+FIX on the data (particularly when one¹s runs are long).
The reason is the performance of FIX at classifying components into signal
and noise is very high in this scenario.  ICA+FIX, when performing well,
is very good at specifically removing spatially localized artifacts of
many sources (and it includes 24-parameter movement regression) without
removing signal of interest.  Thus, things like regressing out white
matter or CSF or variations on these themes aren¹t needed when using
ICA+FIX (if ventricle or white matter signals are strong, they come out as
artifactual components).  ICA+FIX removes many spatially localized
nonlinear effects of motion, including interactions with b0 variations in
the magnetic field.

Notably, ICA+FIX does not specifically clean artifactual parts of the
global signal (the 24-parameter movement regression portion does clean the
global signal, but other nonlinear effects of motion or other global
artifacts‹e.g. CO2 fluctuations‹are not cleaned).  At the same time, we
have seen that the global signal variance after ICA+FIX clean up localizes
to primary sensory brain areas in a strikingly spatially specific way.
What that means is that most brain areas are positively correlated with
primary sensory areas, but may be anti-correlated with each other (e.g.
the default mode and the anti-default mode networks).  This aspect of the
global signal is probably not artifactual.

One strategy for dealing with global artifacts is to use partial
correlation as implemented in FSLNets.  Another strategy would be to
regress out the global signal (though, as noted above, this will also
remove apparently neurobiologically valid whole brain to primary sensory
areas correlations and may cause apparent spatial shifts in the borders
between areas when one of the areas' time courses is particularly
correlated to the global signal).

Other lossy strategies such as band pass filtering, scrubbing, etc. have
not yet been shown to be needed after ICA+FIX on high temporal and spatial
resolution fMRI data (i.e. HCP-style data).  Nor have partial correlation
and global signal regression been compared and contrasted in this kind of
data.  Ideally, what we would want to show is that even after ICA+FIX and
when using either partial correlation or global signal regression, band
pass filtering and scrubbing still substantially reduce between group
motion differences without also reducing differences between an
uncorrelated third variable of interest (e.g. gender, if gender is
uncorrelated with movement).  What we wouldn¹t want to do, is just
continue to use these techniques ³just because people have previously used
them,² because these techniques are lossy (treating signal and noise the
same), we need to check to see if they are still helpful/don¹t make any
differences/are actually harmful in reducing between motion group
differences without also reducing non-movement correlated third variable
differences of interest.

How to properly clean you data has certainly been one of those issues that
generates strong opinions in different investigators, who often then talk
past each other.  There has been at temptation of reviewers in one or the
other of the political ³camps² on the various issues to say ³you must do
this² or ³you must not do this,² when some of the various approaches to
clean up probably do similar things (though some may be more efficient
than others in reducing artifacts while not reducing signals of interest).
 I¹ve also by no means covered all of the other acquisition and analysis
techniques out there that people have developed to try to address this
issue, just ones that I¹m more familiar with.

Peace,

Matt.

P.S.  The above is based on my interpretation of many internal HCP
discussions with investigators in a variety of the ³camps² together my
looking at HCP data in relation to some of these issues.  Because of the
outstanding work yet to be done, the HCP cannot offer a consensus position
on these issues (though hopefully consensus will be achievable after the
above analyses have been done, at least for HCP-style data).

On 3/13/15, 10:57 AM, "Monty Waite" <[log in to unmask]> wrote:

>Is that really the case Eduardo?? Can I see what the collective thoughts
>of others are on this as would mean redoing a whole load of analysis.....
>
>(Ps thanks Michael for earlier comments)