Hello Andreas,
thanks once more.
I think I better briefly describe what we did:
We performed a multi-session temporal concatenation mode MELODICA (v4.0) on
the original time-series on 93 epochs of each 5 minute resting scans.
This epochs come from 4 different sleep stages. The result in principle
delivers typical resting networks (29, about 9 meaningful RSNs).
We simply are stuck with the problem of how/if to correctly test the
subject modes (as contained in the box plots) of the four sleep stages
against each other.
It seems that if negative mode values indicate strong anticorrelation of
that specific individual, the modes are not distributed parametrically from
low strength to high strength, but are mirrored round zero; so maybe, the
absolutes of these values should be taken?
So may be question could be re-formulated: Does time concatenated PICA mode
of analysis that is recommended for resting state analysis deliver
meaningful subject mode values?
Sorry for the longish correspondance here...
& thanks in advance,
Philipp
At 15:12 21.11.2008 +0100, Andreas Bartsch wrote:
>Hi Philipp,
>
>I think I got you wrong (or you got me wrong): I was not talking about any
F-testing, just about the multi-session/subject tensor-ICA on FMRI time
series. Here, if you have one subject with an S-mode of 1 and another with
-1 you may in fact assume that their time-courses OR
activation/deactivation maps behave the opposite way (i.e. are
anticorrelated, as you say). However, for resting state data you cannot
assume that the FMRI time-series are temporally consistent across subjects
whereas the powertransformed time-series may be consistent. Thus, either
you run melodic in the full tensor-ICA mode on powertransformed data or in
the multi-session temporal concatenation mode on the original time-series.
The latter is recommended on the web, at least if you primarily want to
identify the networks.
>Does that help? If not, I guess Christian will jump in...
>Cheers-
>Andreas
>
>
>________________________________
>
>Von: FSL - FMRIB's Software Library im Auftrag von Philipp G. Saemann
>Gesendet: Fr 21.11.2008 10:42
>An: [log in to unmask]
>Betreff: [FSL] Meaning of S-modes in resting network group melodica
>
>
>
>Hello Andreas,
>
>thanks a lot for your answer on the F-test issue.
>
>If I understand it correctly, very low negative values would indicate
>strong "similarity" between the individual time course
>and the first eigenvariate time course, but in an anticorrelated manner. This
>would somewhat imply that the negative S-modes need to be
>flipped for testing against zero AND for comparison between subjects to come
>back to a parametric scale from "low strength" to "high strength" for resting
>analyses.
>
>(Take for e. g. the default mode network that is robustly detected in
>practically any group)
>
>My question now is: is the first eigenvariate (in time concatenated group
>analysis) constructed from somewhat time locked signal courses or from the
>unchanged mix of raw courses as they are in the group data?
>
>If not, then it seems at first sight that generation of the subject modes and
>their parametric value are somewhat not meaningful for resting data at all.
>
>So, e. g. correlation between network "strength" (from the mode values as
>they come from FSL) and a parametric behavioural measure seems obsolete to
>us. However, as we intended to do so, we are stuck here...
>
>Does anybody have an explanation of what the S-mode (across subjects of
>ONE component) as coming from FSL in the time-concatenated group mode
>really indicate? In published (users') work it says something like
"strength of
>BOLD fluctutations".
>
>Again thank you very much in advance for any hints on this,
>
>Philipp
>
>Max Planck Institute of Psychiatry
>NMR Research Group
>Munich
>
>
Max Planck Institute of Psychiatry
NMR Research Group
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