On Wed, Mar 29, 2017 at 3:47 PM, Nicola Toschi <[email protected]
<mailto:[email protected]>> wrote:
Dear Donna and Tim,
thank you very much for your replies and insights. Yes, flatmaps
is exactly what I was looking for!
For now, I was just taking the xyz positions on the sphere,
converting to polar coordinates, interpolating and resampling the
myelin on a regular grid along the two angular coordinates, hence
resulting in a 2D image.
From you explanation I understand that this would preserve
topological neighborhood relationships between scalar values but
not so much real (geodesic) distances, right?
Mostly correct, though wheverever the "poles" happen to land will be
stretched out immensely, and any 2D smoothing applied naively will not
only result in much less smoothing near these poles, but it will also
be highly directional (anisotropic). Additionally, if the spatial
processing doesn't "wrap around" and process the 2D image as if it
were a tube, then it will have a "seam" vertically on the sphere that
data doesn't interact across. Any other spatial processing that takes
a naive 2D regularly spaced grid approach will have similar biases.
Distances on the sphere surface are merely similar to real cortical
distances, but even using the sphere will be more consistent across
the surface than what you describe. This is why I recommend doing any
smoothing or gradient steps in wb_command (or any other things it can do).
You mentioned a classifier, which often doesn't need spatial
information, or at least not fully regular structure - gifti or cifti
files would work just fine for a spatially-agnostic process, and even
if it needs spatial information, if it can be freeform associations
rather than 2D regular grid, then it is possible to do it without this
resampling.
Tim
Thanks a lot!
Nicola
On 3/29/2017 9:29 PM, Dierker, Donna wrote:
Nicola,
It is possible to generate a grid on a spherical surface like
Alex Cohen did in this paper:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2705206/
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2705206/>
… and then fold those coordinates back up into midthickness
configuration.
The 164k_fs_LR vertex-wise correspondence is as good as can be
expected, given anatomical variability across subjects.
Just having trouble understanding how you are using x,y,z
coords with the myelin scalars.
Donna
On Mar 29, 2017, at 12:22 PM, Timothy Coalson
<[email protected] <mailto:[email protected]>> wrote:
If by 2D images you mean pixel-based (like .png), then the
spatial relationships can't be preserved very well. In
order to make flatmaps with moderate distortion, we have
to add cuts to the cortical surface, which leaves
connected parts of cortex separated by many blank pixels.
Our flat surfaces are intended for visualization, and we
do not use them for processing.
I would suggest doing as much of the spatial processing as
you can with wb_command - we have smoothing, gradient,
dilation, and a few other things - if you want it
identical across subjects, rather than based on the
cortical distances in each subject, you can use a group
average surface along with the corresponding vertex area
metric files - this is because group average surfaces have
much less folding than individual subjects, and as such,
geodesic distances along the cortical surfaces are much
shorter than they should be.
As your earlier emails implied use of matlab, you should
note that you can load the gifti surface files into
matlab, which contain vertex neighbor information, by
using the gifti toolbox
(https://www.artefact.tk/software/matlab/gifti/
<https://www.artefact.tk/software/matlab/gifti/>). The
easiest way to get vertex correspondence to the data in
matlab is currently to use wb_command -cifti-separate to
convert the surface data to gifti metric (.func.gii)
files, and then load them instead.
Tim
On Wed, Mar 29, 2017 at 9:24 AM, Nicola Toschi
<[email protected] <mailto:[email protected]>>
wrote:
Hi,
I'd like to arrive at a set of 2D images (e.g. 'unraveled'
myelin maps obtained by cutting and stretching out the
sphere), with anatomical correspondence across
subjects, which can be used as inputs to external
proprietary algorithms (e.g. classification), so I'm
pretty sure I'll need to step out of the Workbench.
Thanks!
Nicola
On 03/29/2017 04:18 PM, Glasser, Matthew wrote:
What operations beyond smoothing do you need? There is
wb_command -cifti-smoothing.
Peace,
Matt.
From: Nicola Toschi <[email protected]
<mailto:[email protected]>>
Date: Wednesday, March 29, 2017 at 9:09 AM
To: Matt Glasser <[email protected]
<mailto:[email protected]>>,
"[email protected]
<mailto:[email protected]>"
<[email protected]
<mailto:[email protected]>>
Subject: Re: [HCP-Users] Position of vertices in
myelin maps and 164k sphere
Hi Matt,
thank you for your quick answer! That makes sense.
I would like to do some spatial operations on the
sphere that exploit spatial neighborhood information
(e.g. even simple smoothing or filtering on the 2D
surface), and have these operations be consistent
across subjects.
What would be the best way to go about this do you
think? Incorporate the exact vertex locations for each
subject anyway, or ignore them and (somehow) just use
the fact that vertex n has the same neuroanatomical
meaning in every subject?
Thanks again,
Nicola
On 03/29/2017 04:02 PM, Glasser, Matthew wrote:
We don¹t recommend using the ft_ tools with MRI
data. Instead use
ciftiopen/ciftisave/
ciftisavereset:
https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ
<https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ>
item 2B
As for your other question, the vertex indices
have neuroanatomical
correspondence across subjects, but that often
does not equate to having
the same 3D coordinates (as volumetric
registration is not able to align
cortical areas across subjects).
Peace,
Matt.
On 3/29/17, 8:41 AM,
"hcp-users-bounces@
humanconnectome.org <http://humanconnectome.org>
on behalf of
Nicola Toschi"
<hcp-users-bounces@
humanconnectome.org <http://humanconnectome.org>
on behalf of
[email protected]
<mailto:[email protected]>>
wrote:
Dear List,
I am trying to do some postprocessing on
myelin and thickness maps (164k
versions) contained in the MNINonLinear
Directory of the Structural
dataset (e.g. ${subject}.corrThickness_
MSMAll.164k_fs_LR.dscalar.nii and
${subject}.MyelinMap_BC_
MSMAll.164k_fs_LR.dscalar.nii)
.
I thought these data would all be in the same
atlas space across
subjects, hence I was expecting to find the
same vertex coordinates for
all subjects. Instead, when reading the data
into matlab (
'ft_cifti_read' and gifti') every subject
seems to have their own
distinct vertex locations. Also, the spherical
gifti surfaces appear to
be sampled at different coordinates across
subjects.
Can I assume that, nonetheless, anatomical
correspondence is preserved
across subjects? Could I, for example, take
the vertex locations from
${subject}.R.sphere.164k_fs_
LR.surf.gii, associate the scalar values in
${subject}.MyelinMap_BC_
MSMAll.164k_fs_LR.dscalar.nii to each vertex,
interpolate on the sphere and assume that I
can superimpose the results
of this procedure across subjects?
Thanks very much in advance!
Nicola
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