On Wed, Mar 29, 2017 at 3:47 PM, Nicola Toschi <[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/ >> >> … 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]> 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/softw >>> are/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]> >>> 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]> >>>> Date: Wednesday, March 29, 2017 at 9:09 AM >>>> To: Matt Glasser <[email protected]>, "[email protected]" >>>> <[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 >>>>> 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 on behalf of >>>>> Nicola Toschi" >>>>> <hcp-users-bounces@ >>>>> humanconnectome.org on behalf of >>>>> [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 >>>>>> >>>>>> >>>>>> ______________________________ >>>>>> _________________ >>>>>> HCP-Users mailing list >>>>>> >>>>>> [email protected]http://lists.humanconnectome. >>>>>> org/mailman/listinfo/hcp-users >>>>>> >>>>> ______________________________ >>>>> __ >>>>> The materials in this message are private and may contain Protected >>>>> Healthcare Information or other information of a sensitive nature. 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