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Hi Matt

Thanks for the input, quite interesting stuff. You hear a lot of argument
about doing this or that all the time, and you are right there is no
consensus yet.

I have not used ICA-FIX so I cannot comment about that, but I will check it
out now that you mention it does not need other type of motion correction
afterwards.

I guess the take home message is that in any case, you need to do more than
Realingment and in Monty's case, ICA-FIX that may have been enough.

Thanks again

Eduardo


On Fri, Mar 13, 2015 at 11:03 AM, Matt Glasser <[log in to unmask]> wrote:

> 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)
>