Hi,
If you are analyzing multiple subjects together then this isn't
necessarily an unreasonable number. The number of components that
MELODIC estimates depends on many factors, including effective
resolution, smoothing, quality of subject alignments, amounts of
artefacts, subject homogeneity, SNR, etc etc. I would actually be a
little surprised if it estimated much lower than this in a
multisubject analysis.
However, if this number is larger than is useful for your purposes
(e.g. if you want to get group results that gives you 5-10 RSNs) then
there is no reason why you shouldn't hand-set the dimensionality to
anything you like - e.g. 20 is what we used in the 'low-dimensional'
analyses in our BrainMap vs RSNs paper that's just come out on
earlyview.
Cheers, Steve.
On 24 Jul 2009, at 17:37, M Mather wrote:
> We are analyzing some data using MELODIC (temporal concatenation
> option)
> that we did not collect ourselves and are baffled by the number of
> MELODIC
> components that result (over 100). This is much higher than we've
> seen in
> our own data and are wondering what the causes might be, or if it is
> within
> the bounds of what we should expect. There are 492 TRs per scan,
> with almost
> 40 scans included in the original analysis (each participant
> contributed one
> or two scans). Many of the MELODIC components show very high values
> for one
> person or scan and very low values for the rest of the participants.
> We have
> tried a variety of things and everything just ends up leading to
> even more
> components:
>
> 1. We went back and ran MELODIC for each individual separately and
> denoised
> their data by removing components that appeared to be motion or other
> artifacts, then reran the overall set. This did not appear to
> improve matters.
>
> 2. Because it looked like one person in particular was driving many
> of the
> components that showed a large effect size for one person and not
> the rest,
> we reran the analysis without that person. That led to a result with
> even
> more components, many driven by another person. We excluded that
> person, and
> got a result with even more components.
>
> 3. We added 10 mm to the spatial smoothing. That was also no help.
>
> Does this suggest a problematic dataset or is there something we
> might be
> doing incorrectly?
>
> Many thanks,
> Mara
>
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
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