Dear David,
On Tue, 20 Feb 2018 01:05:10 +0100, Szabolcs David <[log in to unmask]> wrote:
>Dear Dr. Gaser and co.
>
>I would like to analyse T1s of (mostly) single subjects: monitoring cancer
>patients long term morphological changes after treatment.
>For the first try, I processed two images of a subject, but got stuck after
>the 'Segment longitudinal data' step.
>
>My plan is to show the thickness/gyrification/etc. difference between the
>consecutive scans, for that first resample and smooth the
>l(r)h.thickness.data1 and data2 to FS mesh with the 'Resample and Smooth
>surface data' tool. Next: Surface calculator: simply 's1-s2' of the two
>previously merged and smoothed surfaces. With the 'Display surface' option
>I can visualize this resulting difference surface map, but how is it
>possible to use the 'Display surface results' tool for that? The latter is
>much fancier as I see and also more tools are available (surface atlases,
>flatmap, flatbrain, etc.)
The surface based methods will be not that sensitive to track changes over time compared to DBM or VBM methods. However, you can try to analyze cortical thickness. Folding measures such as gyrification are not meaningful because the folding pattern is quite constant across life in adults.
Your steps were quite right: resampling and smoothing (try 8mm or lower), use surface calculator to calculate the difference and the "Display Surface Result" should also work if you don't move the images outside of your analysis folder where the SPM.mat is saved. The function "Display Surface Results" tries to find the underlying surface using the SPM.mat file (if no other data were found) and is also trying to get some side information by using "lh" or "rh" for the hemispheres or "mesh" for the merged hemispheres. Thus, take care that you use an output name for the surface calculator like "lh.diff_thickness.subject.gii" or "mesh.diff_thickness.subject.gii" The 1st name part is for side information, the 2nd for the measure and the 3rd for the subject name.
>
>Also pretty much the same, but in DBM style ( showing differences between
>two scans using the swj_*.niis, smoothed with an 8mm kernel). In the OHBM
>educational talks, there is an example ( for example here, slide 4,
>https://www.pathlms.com/ohbm/courses/252/sections/1834/slide_presentations/15621
>) How can one make such a represenation? Is it something like a percentage
>difference between the two swj_*.niis?
In CAT12 DBM is only supported for cross-sectional data and the volume changes are always related to the template and not to the baseline scan. Either use the VBM data and estimate the difference or try the longitudinal toolbox in SPM12. Please note, that there are substantial differences in these methods w.r.t. the expected changes (also see the CAT12 manual for more information). The SPM12 Longitudinal Toolbox has its strengths for larger long-time changes while CAT12 is more sensitive for short-time changes. If you apply the SPM12 Longitudinal Toolbox to longitudinal data with very short time differences of a few days or weeks the methods is simply not expecting larger changes due to the short time interval.
>
>On the longer term I would like to do something similar of what is
>presented in the Frontiers 2009 article (this guy: https://www.frontiersin.
>org/articles/10.3389/neuro.11.025.2009/full ; expecially this figure:
>https://www.frontiersin.org/files/Articles/668/fninf-03-025/
>image_n/fninf-03-025-g004.gif ) and I do belive that the OHBM educational
>talks were also presenting the same-ish material, but could not find any
>original researach on that particular data, same link as before slide 5 -
>What is applied test here? A one sample t-test?
If you have more time points you can use a polynomial model for increase/decrease with time. In that example I have used a polynomial regression with time (linear + squared effects) and analyzed the data using F-test for any effects due to linear or squared increase or decrease.
Best,
Christian
>
>Thank you in advance and apologies for this sea of qs.
>
>Kind regards,
>Szabolcs
>
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