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Thank you so much every one for helping me in this regard. I have some follow up questions:
I am interested in associating mean curvature (H) with some behavioral measure. After extracting subject-wise mean curvature estimates, I found a significant positive association between mean curvature and those behavioral measures. My questions are:
(1). Why did Dr. Greve suggested to use white surface for estimating mean curvature, and not pial? What's the exact differences in both - is the only difference is that one is used for mean curvature from white matter (white surface) and the other from gray matter? I am not sure why one is recommended over the other? (2). If I want to use my own spherical ROIs for mean curvature estimation, how much radius is enough to draw spheres around peak coordinates? (3). Using white surface as recommended as Dr. Greve, I get (-) values for all the subjects, please see attached plot. Here mean curvature values are plotted along Y-axis. So here more negative values (.e.g ~ -0.07) represent sharper folds or less negative values (e.g. ~ -0.04) represent sharper folds? If I take absolute values of mean curvature, this association shown in attached plot changes from positive to negative. Can you please help me in interpreting the differences in more negative and less negative values here?
Any additional information about this measure will also be very useful, as I am using this parameter for the first time.
Thanks a lot.
On Wed, Dec 19, 2018 at 4:24 PM Bruce Fischl fischl@nmr.mgh.harvard.edu wrote:
a 2D manifold has two principal curvatures, usually called k1 and k2, which are the curvature in the direction of maximum curvature and minimum curvature (they are also the eigenvalues and eigenvectors of the Hessian of the surface if it is expressed as a function over the tangent bundle). The Gaussian (K) and mean (H) curvatures are then:
K = k1*k2 H = (k1+k2)/2
cheers Bruce
On Wed, 19 Dec 2018, Greve, Douglas N.,Ph.D. wrote:
I'll leave that up to Bruce and Rudolph
On 12/19/2018 05:37 PM, Martin Juneja wrote:
External Email - Use CautionThanks Dr. Greve. That works, but both white.K and white.H are giving me very different output values.
For example, for first few subjects I get: -0.00237 -0.00450 -0.00204 0.00113 -0.00228 -0.00958 -0.00314 0.00180 -0.00452 if I use white.K -0.05809 -0.05799 -0.06457 -0.07254 -0.07208 -0.05023 -0.06044 -0.09338 -0.09178 if I use white.H
Can you please tell me the difference between white.K and white.H conceptually and mathematically?
On Wed, Dec 19, 2018 at 2:27 PM Greve, Douglas N.,Ph.D. <DGREVE@mgh.harvard.edu mailto:DGREVE@mgh.harvard.edu> wrote:
something like --meas white.K or white.H On 12/19/2018 04:23 PM, Martin Juneja wrote:External Email - Use CautionHi,
Just like volume, I have "Folding Index" measures saved in lh/rh.aparc.stats files for each subject.
If I am using mris_preproc *--meas volume* --out CV/lh.CV.mgh
commandto concat cortical volume files from all subjects, then how can
I usethis command for "folding index" output file?
Any help will be really appreciated.
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