Hi Steve,
Yes I have checked all of these things. The raw tstat output is rather random as well. I even re-ran the MD analysis just to be sure, but got the same results. Any suggestions?
Thanks,
Laura
---------------------------------
Laura Danielian
Biomedical Engineer
National Institutes of Health
Building 10 CRC Room 7-5753
Bethesda, MD 20892
301-496-2168
________________________________
From: Stephen Smith <[log in to unmask]>
Reply-To: FSL - FMRIB's Software Library <[log in to unmask]>
Date: Tue, 20 Oct 2009 02:40:40 -0400
To: <[log in to unmask]>
Conversation: [FSL] tbss_non_FA on MD
Subject: Re: [FSL] tbss_non_FA on MD
Hi - hard to tell - did you check things like - did you definitely enter all the subjects in the same order for MD as for FA? have you looked at the raw tstat image output by randomise - does that make some sense? Did you look at the full movie loop of all_MD and all_MD_skeletonised?
Cheers, Steve.
On 19 Oct 2009, at 17:27, Laura Danielian wrote:
Hi,
I have run tbss_non_FA on MD data and my results suggest that I may have
done something wrong. My FA data is consistent with previous research and
makes good sense, however the results for MD are blank, as in no significant
regions after running randomise. Previous studies have shown MD results
similar to FA.
I have reviewed the all_MD and all_MD_skeletonised outputs and both look
fine. I ran the steps straight from the "Using non-FA Images in TBSS"
section on the TBSS website. I did not correct for scale as I do not think
that this is part of the new version. I used the following randomise command:
randomise -i all_MD_skeletonised -o tbss_MD -m mean_FA_skeleton_mask -d
design.mat -t design.con -n 5000 --T2 -V
where design.con has 2 contrasts to compare between each of our disease
groups and normal volunteers (there are 3 "waves" in design.mat).
When I review even the basic uncorrected p outputs (tbss_MD_tfce_p_tstat1)
the regions of significance seem random and do not line up in the CST as
expected. I do get one small region of significance when running this
process on Lambda1 data, but the location does not make much sense clinically.
Any ideas where I might be going wrong or missing something? Am I missing a
scaling or thresholding step?
Thanks so much for your help!
-Laura
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
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