Dear Bas & Tess,
While you can include more voxels by lowering the threshold, I don't
think this is what you want in this case - you would end up including
various amounts of extracerebral tissue (resulting in different numbers
of multiple comparisons between the two pulse sequences).
I would recommend a combination of a low threshold (to include the
voxels you're interested in) with an explicit mask to 1. exclude
extracerebral voxels and 2. (& possibly more importantly) enforce the
same number of voxels per analysis for the two sequences.
We have used this method for example in
Hammers A et al. JCBFM 2008
Hammers A et al. Brain 2003 (with an illustration of such a mask on p.
1310).
Hope this helps,
All the best,
Alexander
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
On Behalf Of Bas Neggers
Sent: 07 March 2008 22:34
To: [log in to unmask]
Subject: Re: [SPM] Including dropout regions in analyses
Hi Tess,
this behavior can be influenced by the parameter
defaults.mask.thresh = 0.8;
in spm_defaults.m
Values below this fraction of average session intensity (average over
time and voxels, I believe) will be replaced with NaNs when doing
statistical analyses.
So you could try lowering this value. Take care however when you have
too much outside-the-head voxels in your statistical estimation when
using really low values, the autoregressive modeling (AR(1)) can fail to
converge in that case (see earlier posts on this issue by myself and
others).
Good luck,
Bas
Tess Nelson schreef:
> Hello,
>
> We are currently conducting an fMRI study examining whether a novel
> pulse sequence will enhance signal in the OFC. In order to
demonstrate
> the effectiveness of this method, we'd like to perform a statistical
> comparison of the activation in the new vs. conventional sequences.
The
> main problem we're
> encountering is that during modeling, SPM eliminates regions where
> there is no signal, which prevents us from comparing regions that have
> signal in the novel method but not in the conventional method (the
> dropout regions). Does anyone know how we can get around this and
> include these regions in our analysis? We are using SPM5 with Matlab
> 7.1.
>
> Thanks for your help,
>
> Tess Nelson
>
> Research Assistant
> Kennedy Krieger Institute
> [log in to unmask]
>
>
>
> Disclaimer:
> The materials in this e-mail are private and may contain Protected
Health Information. Please note that e-mail is not necessarily
confidential or secure. Your use of e-mail constitutes your
acknowledgment of these confidentiality and security limitations. If you
are not the intended recipient, be advised that any unauthorized use,
disclosure, copying, distribution, or the taking of any action in
reliance on the contents of this information is strictly prohibited. If
you have received this e-mail in error, please immediately notify the
sender via telephone or return e-mail.
>
--
--------------------------------------------------
Dr. S.F.W. Neggers
Division of Brain Research
Rudolf Magnus Institute for Neuroscience
Utrecht University Medical Center
Visiting : Heidelberglaan 100, 3584 CX Utrecht
Room B.01.1.03
Mail : Huispost B.01.206, P.O. Box 85500
3508 GA Utrecht, the Netherlands
Tel : +31 (0)30 2509609
Fax : +31 (0)30 2505443
E-mail : [log in to unmask]
Web : http://www.fmri.nl/people/bas.html
--------------------------------------------------
|
