Hi Martin
you can use whichever surface you want - they are just different
biological questions. The files we write out by default relate to the
white surface, but you can certainly generate the equivalent for the pial
using mris_curvature.
In general the curvature is 1/r, where r is the radius of an inscribed
circle that is tangent to the surface in that direction. So bigger
curvature implies smaller r (and sharper folds).
As for you question (2), I think that is a biological one and depends on
the effect you are looking for (unless I am misunderstanding)
cheers
Bruce
On
Thu, 20 Dec 2018, Martin Juneja wrote:
External Email - Use Caution
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 <fis...@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 Caution
>>
>> Thanks 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.
>> <dgr...@mgh.harvard.edu <mailto:dgr...@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 Caution
>> >
>> > Hi,
>> >
>> > 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
>> command
>> > to concat cortical volume files from all subjects, then how can
>> I use
>> > this command for "folding index" output file?
>> >
>> > Any help will be really appreciated.
>> >
>> >
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