Subject: | | Re: normalization issues |
From: | | Colin Hawco <[log in to unmask]> |
Reply-To: | | [log in to unmask][log in to unmask]] On Behalf Of Rabia Mazhar Sent: 09 September 2016 15:41 To: [log in to unmask] Subject: Re: [SPM] DCM Failed Estimation error
Dear Peter Thanks again for your quick reply. 4x4x1 is essentially 4x4. Please correct me if I am wrong. With 4x4 it is still the same error.
I dig deep in to the code, and found that for some unknown reason, kl_weights=spm_kl_eig_normal(w_mean(:),w_cov,prior_cov); is coming out as NaN which is then leading to the same error. Dont know if that helps
Is it possible that you may send me a working DCM.mat so I can get to know in which format the inputs are required ? That will be great help and hopefully will solve my problem
Thanks and best
On Fri, Sep 9, 2016 at 1:42 PM, Zeidman, Peter <[log in to unmask]<mailto:[log in to unmask]>> wrote: Dear Rabia Your b-matrix should be of size [n x n x u], where u is the number of inputs. As can be seen from DCM.U, you have one input (‘null’). At the moment your bmatrix is of size 4x4x4. So you need to change it to be 4x4x1. I have now added code to give clearer error messages, which will be in the next release.
Note that as this is resting state, you can use spm_dcm_estimate(DCM) for stochastic DCM (which operates in the time domain) or spm_dcm_fmri_csd (which operates in the frequency domain). The latter is a new approach (Razi et al. 2016), which is quicker and potentially more accurate.
Best Peter
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]<mailto:[log in to unmask]>] On Behalf Of Rabia Mazhar Sent: 09 September 2016 12:00 To: [log in to unmask]<mailto:[log in to unmask]> Subject: Re: [SPM] DCM Failed Estimation error
Dear Peter Thanks a lot for your suggestion and your quick reply. But the error still continues. Here is my attached new DCM with connectivity matrices as 4 dimensions. I will be grateful if you can suggest a solution
Thanks and best Rabia
On Fri, Sep 9, 2016 at 12:21 PM, Zeidman, Peter <[log in to unmask]<mailto:[log in to unmask]>> wrote: Dear Rabia Your DCM has 4 regions (DCM.n), as well as timeseries for 4 regions (in DCM.xY and DCM.Y), and timing offsets (delays) for 4 regions (in DCM.delays). However, all of your connectivity matrices are of dimension 3 rather than 4 (DCM.a, DCM.b, DCM.c, DCM.d). I suspect that is causing the problem.
Best Peter
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]<mailto:[log in to unmask]>] On Behalf Of Rabia Mazhar Sent: 08 September 2016 13:43 To: [log in to unmask]<mailto:[log in to unmask]> Subject: [SPM] DCM Failed Estimation error
Dear SPM users I m getting the following error while estimating DCM Error in inline expression ==> spm_fx_fmri(x,v,P)*2 Index exceeds matrix dimensions.
I already have the time series extracted from the ROIs, and I m using this to create the DCM structure. I have attached my DCM variable probably I have some mistake in defining it. Can somebody please look in to it and help me what I have made wrong here ?
Thank you
best regards, [log in to unmask] |
Date: | | Thu, 29 Sep 2016 20:11:14 -0400 |
Content-Type: | | text/plain |
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Thanks Helmut. Quite helpful. My admittedly limited previous experience with deformation maps (which is what I understand the y_anat.nii files to be) lead me to expect... something else.
I'll try the manual registration correction. Maybe I can tweak those people a bit. It is a big data set (177 subjects for a task study) so it has been a fair amount of work : )
I generally have full coverage in the functional data, but sometimes the edge of the brain is on the top slice of the fMRI image is the top edge of the brain or scalp. The registration does not seem to deal well with these cases, even though we still have the whole brain.
Colin
-----Original Message-----
From: MRI More [mailto:[log in to unmask]]
Sent: September-29-16 12:04 PM
To: [log in to unmask]; Colin Hawco
Subject: Re: normalization issues
Dear Colin,
The "gradient" pattern is what the y_ usually look like. Maybe you accidentally looked at y_ files from some subjects and u_ files from others?
> Also the offset images are more likely to have a small misregistration with the anatomical T1, but all attempts to get coregister to do a better job have not produced improvements.
You could still correct these images manually. It's subjective, but well, in that case it makes more sense than relying on objectively but poorly registered images.
> The deformation fields (the y_anat.nii file) do not seem correct to me.
Not sure whether I understand this correctly. Is it the anatomical images which are "incomplete"? In case some parts of the brain are cut off and/or the brain extends close to the boundary (thus, parts of the skull and dura missing) there's probably not much to do except if you have another image that covers the whole head, which you could coregister the "incomplete" data onto and then use the "complete" ones for segmentation purpose. Of course, you could still try to work with tissue priors/templates that are cut off at the top to the same extent, but this would be time-consuming and probably quite subjective. However, if the c1, c2 are alright (not incomplete) I would suggest to Dartel register these together (probably combined with an additional transformation into MNI space). As it's just the c1, c2 (well, the rc1, rc2) it wouldn't matter whether the other tisse class images are "incomplete" or not. This should lead to a undistorted normalisation, but as stated, it requires unaffected c1, c2, which might not be the case.
Or is it the functional images which are "incomplete"? In that case the problem should be solved by manually correcting the coregistration onto the anatomy. You might still encounter something like in the attached figure, where parts of the brain are extended into regions where no data was available, but this can be solved by applying an explicit mask to the first-level models.
Hope this helps a little
Helmut
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