Hi Daniel
There are some useful previous emails you might like to check out, for
example the thread that starts with:
http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind05&L=SPM&D=0&I=-3&P=175640
and
http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=ind03&L=SPM&P=R220972&I=-3
Regarding modelling 'bad' scans, the general point is that as usual a model
should reflect what you think is going on - and so if you model several
outliers their effect on activity in any given voxel may be independent and
qualitatively different. If that's the case (as it is perhaps safest to
assume) you need one design matrix column for each 'bad' scan with a 1 for
that scan and possibly its neighbours.
HTH, Alexa
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On
> Behalf Of cyril pernet
> Sent: 08 October 2006 11:09
> To: [log in to unmask]
> Subject: Re: [SPM] Motion during task
>
> Hi Daniel
>
> >Hi all,
> > I have a question about accounting for motion in the scanner. We scan
> >many children with ADHD, and they tend to move around, and occasionally
> >there is a period of about 10 seconds of fidgeting in the middle of a
> run.
> >In the past, we have removed these volumes and adjusted the timing files,
> >but I was wondering - would it work to create a block regressor to cover
> the
> >period of motion, and assign it a zero in the model (and remove the
> events
> >during that period from the timing file), so that SPM "ignores" the data
> >from that period? Or would keeping the motion volumes in the model still
> >affect the results somehow? It would be convenient to use this
> technique,
> >because I was also thinking of using the same technique for the
> infrequent
> >times when a child falls asleep in the scanner. Does this technique
> sound
> >dodgy? Thanks!
> >Daniel
> >
> >
> That sounds more reasonable to me not to remove the bad volumes and
> model the full time series.
> You can "nullify" the bad scans by adding an extra regressor to your
> design matrix with zeros for all your scans but 1 for the bad ones.
> I did use that to nullify a dummy volume, i.e. there was a missing
> volume in a series that I substituted by the mean of adjacent volumes
> and next nullify this way (thanks to Tom Nichols for the trick)
>
> I guess this is the same idea for you.
> Hope this helps
>
> Best
> Cyril
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