Dear Peter, dear SPM-experts,
This may serve as an example to demonstrate how I have defined and
extracted my VOIs.
First, I have created masks for my ROIs with the SPM Anatomy Toolbox
(version 18) in MNI-space.
In my example, this is for posterior parietal cortex of the left
hemisphere (PPC_L), consisting of Brodman area 5+7.
As mentioned before, I have calculated one SPM per subject containing
sessions 1-6, so for each subject...
- SPM => Results
- t-contrast for "attention"
- masking (inclusive) => image: ROI_PPC_L (as created with SPM Anatomy
Toolbox)
- p value adjustment: none
- threshold: p = 0.05
- extended threshold voxels: 0
- goto: global maximum (which is inside my predefined mask)
- eigenvariate
- adjust for: effects of interest (EOI)
- session: 1-6
- sphere radius: 6 mm
In this way I have extracted my VOIs for all subjects and sessions for
PPC_L. For PPC of the right hemisphere and the other regions (V1 and V4)
I have used different masks and/or contrasts, resulting in 36 VOIs per
subject (6 regions x 6 sessions). By definition these VOIs were based on
activations in my SPM analysis, subjects without activation in any of
these ROIs were not further analysed.
Based on these VOIs I have set up my DCMs. The basic setup of each DCM
is similar to the one used for the „attention to motion“ paper,
including 6 regions with reciprocal intrahemispheric connections between
V1-V4 and V4-PPC and reciprocal interhemispheric connections between
V4-V4 and PPC-PPC. Driving input is allocated to both V1 regions. On
this basis I have set up multiple DCMs with alternating modulatory input
of factor "attention" to either regions or connections to subsequently
perform a BMS analysis.
Peter, I hope I have answered your questions. If you have more
questions, please let me know. If the extraction of my VOIs is correct,
where else could I dig for my mistake?
Thank you for your time!
Best,
Eric
> Hi Eric,
> Sorry you're still having problems with your models. Simplifying the models to 3 regions is a good start. Could you tell us more about how you choose your ROIs? Are they based on activations in your SPM analysis, in the same experimental conditions as you're modelling with the DCM?
>
> Best,
> Peter
>
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Eric Holst
> Sent: 01 December 2014 13:55
> To: [log in to unmask]
> Subject: [SPM] DCM: Problem with percentage of explained variance
>
> Dear Peter, dear SPM-experts,
>
> some time ago I posted a question about very low percentage of explained variance (0%) when checking my DCMs with spm_dcm_fmri_check.
>
> (see https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1411&L=spm&F=&S=&X=4C60EE6C21C6F1C1B5&Y=eric.holst%40web.de&P=334445)
>
> As this could be due to complexity of my DCMs I have reduced my basic model from 6 to 3 regions, but explained variance is still at 0%.
>
> I have read about increasing the area of the VOIs (I am using a 6 mm sphere), but most of my VOIs already consist of 30+ voxels.
> [Btw: I smoothed my data in preprocessing.]
>
> Now I am wondering about the right way to proceed. What should I do now to improve my models?
>
> Any help is appreciated and thanks in advance,
>
> Eric Holst
>
> Department of Neurology
> Charité, Berlin, Germany
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