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Anderson,
Thanks. Unfortunately, this did not work. I got the following error. Have attached the design and contrast matrix files, in case you’d like to look at it,
but I’m pretty sure these are configured correctly.
“ERROR:: design matrix of different size to number of masks specified”
The command used to run setup_masks as “setup_masks ICVF_Q1vsQ4_124subj_2017Aug09.mat ICVF_Q1vsQ4_124subj_2017Aug09.mat ICVF_Q1vsQ4_124subj_2017Aug09_masked
mask1 mask2 … mask124”.
Can it not handle this large a dataset? Thanks for any advice you can provide.
Mark
____________________
Mark Wagshul, PhD
Associate Professor
Gruss Magnetic Resonance Research Center
Albert Einstein College of Medicine
Bronx, NY 10461
Ph: 718-430-4011
FAX: 718-430-3399
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From: FSL - FMRIB's Software Library [mailto:[log in to unmask]]
On Behalf Of Anderson M. Winkler
Sent: Wednesday, August 9, 2017 9:22 AM
To: [log in to unmask]
Subject: Re: [FSL] handling of zeros in randomise
Hi Mark,
That's exactly it. For subject with no lesions, use an all zeroes mask (i.e., a blank volume).
All the best,
Anderson
On 7 August 2017 at 10:30, Mark Wagshul <[log in to unmask]> wrote:
Anderson,
Sorry, I just noticed this response from you. I did see that comment, but didn’t have time to look
carefully into this option. I now understand what you mean and will try this out. You’re right, I think this is exactly what we need to run.
Just to clarify how setup_masks works – I have to specify a mask for each volume, irrespective of
whether or not I want to mask that volume (since it requires the masks to be in the same order as the images in the input image). Not quite sure what the EV’s will look like, since the EV’s are now images (masks), but I guess it generates these automatically,
so I will find out once it runs.
Thanks for the help!
Mark
____________________
Mark Wagshul, PhD
Associate Professor
Gruss Magnetic Resonance Research Center
Albert Einstein College of Medicine
Bronx, NY 10461
Ph: 718-430-4011
FAX:
718-430-3399
Email:
[log in to unmask]
This email message and any accompanying attachments may contain privileged information intended only
for the named recipient(s). If you are not the intended recipient(s), you are hereby notified that the dissemination, distribution, and or copying of this message is strictly prohibited. If you receive this message in error, or are not the named recipient(s),
please notify the sender at the email address above, delete this email from your computer, and destroy any copies in any form immediately.
From: FSL - FMRIB's Software Library
[mailto:[log in to unmask]]
On Behalf Of Anderson M. Winkler
Sent: Sunday, July 30, 2017 4:10 PM
To: [log in to unmask]
Subject: Re: [FSL] handling of zeros in randomise
Hi Mark,
Have you considered setup_masks that I commented in the earlier email? It should do what you need...
All the best,
Anderson
On 27 July 2017 at 18:12, Mark Wagshul <[log in to unmask]> wrote:
Thanks, Anderson (sorry for the delayed response!).
So, if I understand you correctly, it is NOT possible to ignore the zeros with randomize and the
best approach would be to leave the non-physical values in. This will work for FA, since most of these problematic voxels are areas where there are imperfections in a few of the directions (or where the imperfection is in the b=0 image, such that its value
is lower than some of the b~=0 images). In this case, the FA will be slightly above 1, but as you suggest the actual value is still likely high.
This will unfortunately not work for NODDI. Many of the errors in these maps give values which are
way off, e.g. ICVF << 0, and are likely due to imperfections in the data which lead to erroneous fitting of the NODDI model. In these cases, I think it’s unwise to leave the data as is since these will look like extreme parameter values for this subject,
and the real solution would be to zero out those voxels for only that subject. But, it seems that this feature is not available for randomize, so that the only way to prevent erroneous results in these voxels is to zero them out for all subjects. Would this
be your recommendation as well?
Thanks for any further advice you can provide.
Best,
Mark
____________________
Mark Wagshul, PhD
Associate Professor
Gruss Magnetic Resonance Research Center
Albert Einstein College of Medicine
Bronx, NY 10461
Ph: 718-430-4011
FAX:
718-430-3399
Email:
[log in to unmask]
This email message and any accompanying attachments may contain privileged information intended only
for the named recipient(s). If you are not the intended recipient(s), you are hereby notified that the dissemination, distribution, and or copying of this message is strictly prohibited. If you receive this message in error, or are not the named recipient(s),
please notify the sender at the email address above, delete this email from your computer, and destroy any copies in any form immediately.
From: FSL - FMRIB's Software Library
[mailto:[log in to unmask]]
On Behalf Of Anderson M. Winkler
Sent: Tuesday, July 18, 2017 11:27 AM
To: [log in to unmask]
Subject: Re: [FSL] handling of zeros in randomise
Hi Mark,
Please see below:
On 17 July 2017 at 14:46, Mark Wagshul <[log in to unmask]> wrote:
Dear FSL experts,
How does randomise handle zeros in the parameter maps? I am analyzing DTI and NODDI data and there are instances where the parameter maps contain values which are non-physical (e.g. FA > 1). I would like to zero out these data points for this subject alone,
rather than masking out the voxel for all subjects. Is this possible, or will randomise still calculate the statistics based on this subject with a value of zero?
I think you can leave these values as is, i.e., even if non-physical, they are still the best estimate of the diffusion parameters given the data, and randomise can benefit from
them if you leave them there.
Zeroeing out or removing will be worse as the value randomise will see is much farther from the reality than the non-physical value. Say the true (unseen) FA value is 0.95, and
the estimate found was 1.06 (non-physical). This 1.06 is closer to the truth than 0. Removing the voxel from the analysis (see the script setup_masks) may be just as bad, as the 1.06 provides some information (i.e., that FA is high for this subject), whereas
knocking the voxel out will toss that information away.
Obviously, if it were possible to exclude individual subjects with zeroed-out data this will change the number of degrees of freedom across the image, but for a large enough sample (> 50 subjects per group), and assuming the zeros are occurring in random locations due to imperfections in the data, this should be a pretty small effect (alternatively, randomise may be able to handle the varying DOF's across the image).
The script setup_masks is useful for this purpose. If the DOF becomes a problem, it's possible to make small syntax changes and run using PALM with the option "-zstat", that will
remove the dependency on the DOF.
Hope this helps!
All the best,
Anderson
Any advice you can give me on this would be much appreciated.
Thanks,
Mark Wagshul