This may or may not be helpful, but, I was and still am fascinated by grayordinates.
Alpay, I think you are asking for a mapping from grayordinates to a standard volume representation of the functional areas, such as MNI or Talairach coordinates. I think Matt might be resistant because the modeling of the pancake of the neocortex as a grid of voxels may not be conceptually clean. It seems that if there were an ideal folding of the neocortex into a volume, then you would have your mapping. So, I have two questions: Is there an ideal folding of the neocortex into a volume? Is there a geometric path for the grayordinates? Does anyone have a graph? If I ever come up with one, does anyone else want to see it? I’m an armchair neuroscientist and I got sent to Resting State 2014 as sort of a reward for debugging a deep mystery with our aging volume rendering hardware on a newer server. I’ve been in 3D graphics forever and medical imaging for 17 years. I am a coordinate system geek. I tried to see if there was a any sort of geometric path for the grayordinates. http://www.jch.com/jch/notes/RestingState2014/grayordinates.html but then I had to get back to my day job. YON - Jan C. Hardenbergh <> jch.com <> Pixelsmith Deadlines may impose an artificial thought process, but, without them there would be no thought process at all. > On Mar 31, 2015, at 2:11 PM, Alpay Özcan <[email protected]> wrote: > > Matt, > thank you very much for your explanations. > > Taking a pragmatic view, I just loaded the time series data from the > grayordinates file > rfMRI_REST1_LR_Atlas_hp2000_clean.dtseries.nii > > picked up one of the time series and subtracted it from > all of the time series in the file > rfMRI_REST1_LR_hp2000_clean.nii.gz > > There was no match. > > This answers my question about the location > of the grayordinates in the full MNI space time series data. > > Basically, the time series in grayordinates from the file > rfMRI_REST1_LR_Atlas_hp2000_clean.dtseries.nii > > are not a subset of MNI space 91x109x91 1200 time point time series data > from the file > rfMRI_REST1_LR_hp2000_clean.nii.gz > > Therefore, grayordinate time series data are obtained after registration > transformations targeting the surface points, I am guessing, as you > explained in your e-mails. > > Please allow me to follow up by asking if there is a segmentation or > a binary mask on the 91x109x91 data set (rfMRI_REST1_LR_hp2000_clean.nii.gz) > > for example using the 1st time point brain volume > that gives the segmentation for the cortex? > > Thanks in advance and thank you for your patience, > alpay > > > On 28-Mar-15 11:19, Glasser, Matthew wrote: >> The surface grayordinates simply don’t exist in a 3D cartesian space, they >> exist on a 2D surface mesh made up of triangular tiles and their neighbors >> are determined by the topology of that mesh. Most vertices have six >> neighbors, but a very few have 5 (also those along the medial wall may >> have fewer valid neighbors for a different reason). You find the >> neighbors of surface grayordinates by looking at the topology section of a >> GIFTI surface file. The first data array of a GIFTI surface file are the >> 3D coordinates of each vertex, the second data array are the topological >> relationships between the vertices that form each triangular tile. >> Individual subject standard mesh surfaces may have different 3D >> coordinates across subjects, but always have the same topology and hence >> are said to be on the same mesh. It is this standard mesh that is the >> equivalent of a standard voxel grid in volume space. We provide the >> command wb_command -surface-geodesic-rois that you can use to find the >> nearest surface vertices within some number of mm of a given vertex. >> There is the command wb_command -surface-geodesic-distance which you can >> use to compute the distances between a vertex and the rest of the surface >> (you can limit this to a max distance to speed it up). Finally, there is >> the command wb_command -cifti-export-dense-mapping that will tell you >> which grayordinate indices correspond to which surface vertex indices or >> subcortical voxels. Perhaps one or more of these will address your >> request. >> >> If for some reason you need to know about an individual subject’s voxels, >> you can work with the data before it has been resampled to grayordinates >> space and then resample any results to grayordinates space after you’re >> done (however you’ll want to be sure you are only working with voxels >> inside the greymatter ribbon). You’ll also need to be careful you don’t >> accidentally average across subjects in volume space or smooth the data in >> volume space. At some point we’ll make it possible to completely reverse >> the grayordinates projection (e.g. go from group average results in >> grayordiantes space to individual subject native or MNI volume space), but >> this has not yet been a high enough priority for our developers, as most >> people don’t need to run their analysis in that direction. >> >> I’ll be honest that despite your lengthy e-mail, I’m not really clear what >> you are doing and hence why you need what you are requesting. If you >> don’t want to post sensitive details in public, you can e-mail me off the >> list. If you don’t want to tell me specifically what you’re up to, I’m >> more limited in my ability to help. >> >> Peace, >> >> Matt. >> >> On 3/28/15, 8:44 AM, "Alpay Özcan" <[email protected]> wrote: > _______________________________________________ > HCP-Users mailing list > [email protected] > http://lists.humanconnectome.org/mailman/listinfo/hcp-users _______________________________________________ HCP-Users mailing list [email protected] http://lists.humanconnectome.org/mailman/listinfo/hcp-users
