Dear Dr. Gaser,
I want to correct for non-isotropic smoothness for my VBM analysis. But I met an error when I used the hidden option to transform the spmT maps ("expected voxels per cluster" was selected as the cluster extent threshold).
SPM8: spm_vbm8 (v404) 13:55:52 - 26/09/2011
========================================================================
------------------------------------------------------------------------
Running job #1
------------------------------------------------------------------------
Running 'Threshold and transform spmT-maps'
Failed 'Threshold and transform spmT-maps'
Error using ==> spm_P
Too many output arguments.
In file "C:\Program Files\SPM\spm8\spm8\toolbox\vbm8\cg_spmT2x.m" (v415), function "cg_spmT2x" at line 284.
The following modules did not run:
Failed: Threshold and transform spmT-maps
How may I get around with this error? Thanks for your help!
Best wishes,
Meikei
________________________________________
From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Christian Gaser [[log in to unmask]]
Sent: Monday, September 26, 2011 4:23 AM
To: [log in to unmask]
Subject: Re: [SPM] Non-Stationary Cluster Extent Correction for SPM in VBM analysis
Dear João,
On Thu, 22 Sep 2011 15:26:25 -0400, Jonathan Peelle <[log in to unmask]> wrote:
>Dear Jo�o,
>
>> I'm running a VBM analysis with SPM8 and VBM8 toolbox.
>>
>> I have a couple of questions:
>>
>> - Is it better to report the voxel-level or cluster-level p-values?
>
>It's not that one is "better"�they are telling you about different
>levels of inference. For the voxel-level correction, it tells you
>that every voxel you see is unlikely to have occurred by chance (and
>thus, you could make some inference about that voxel). If cluster
>level, it tells you that a particular cluster is bigger than you would
>expect by chance. This entitles you to make some inference about the
>cluster as a whole, but not subparts (which can get tricky if a large
>cluster covers multiple regions of the brain). There are several
>other emails in the archives that go into more detail.
>
>
>> - If I use the "Results" button in SPM8, does it use the non-stationary
>> cluster extent correction to calculate valid cluster p-values? Is the method
>> of Satoru Hayasaka et al. 2004 implemented in SPM8 or should I use an
>> external toolbox, like the NS extension by Hayasaka et al
>> (http://fmri.wfubmc.edu/cms/NS-General) ?
>
>The "results" button in SPM does not correct for non-stationarity
>which is present in VBM images. You'll need to use the ns toolbox.
>Unfortunately, as far as I know the ns toolbox is not available for
>SPM8 (nor will it be), which means using SPM5 for looking at your
>results. As far as I'm aware this should not present a problem, as
>the statistical machinery has not changed appreciably (but I would be
>interested to hear if I'm wrong about that!).
>
There is an option for the newest SPM8 releases (>=4010), which enables non-stationarity
correction on the cluster level. Please set in spm_defaults.m:
defaults.stats.rft.nonstat = 1;
However, this option will result in a much longer calculation if you print the results table.
This is maybe also the reason that this option is not set as default.
>I don't know whether the VBM8 toolbox includes anything for
>non-stationarity correction�it would be worth checking.
I have removed this option from the interface because of the interferences with the SPM
interface. There is still a hidden option if you choose the option
"Transform and threshold spmT-maps".
Regards,
Christian
____________________________________________________________________________
Christian Gaser, Ph.D.
Departments of Psychiatry and Neurology
Friedrich-Schiller-University of Jena
Jahnstrasse 3, D-07743 Jena, Germany
Tel: ++49-3641-934752 Fax: ++49-3641-934755
e-mail: [log in to unmask]
http://dbm.neuro.uni-jena.de
>
>
>
>> - If I choose "none" for the "p value adjustment for control" it will aply
>> the following threshold to each voxel, am I correct? But even if I choose
>> "none" correction I still get the FWE-corrected cluster-level and I get
>> FWE-corrected cluster-level p-values... why is this?
>
>You are correct�if you select "none", it will apply that threshold at
>the voxel level. You can think of this as defining features
>(clusters) in your data. You have not corrected for multiple
>comparisons at the voxel level (because you selected "none"), but you
>can still correct for multiple comparisons at the cluster level.
>Having defined your clusters using some threshold, SPM will therefore
>give you the corrected p values for each cluster. These are perfectly
>valid, provided that (a) you understand the level of inference you're
>working at (see above) and (b) in the case of VBM, you have accounted
>for non-stationarity in the data.
>
>This previous message may also be of some help:
>
>https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;8b567b91.1107
>
>
>Hope this helps!
>
>Best regards,
>Jonathan
>
>
>--
>Dr. Jonathan Peelle
>Department of Neurology
>University of Pennsylvania
>3 West Gates
>3400 Spruce Street
>Philadelphia, PA 19104
>USA
>http://jonathanpeelle.net/
