There are folks working on this. The Yeo parcellation was made using differing surface registration, and also it is a “winner take all” clustering parcellation, rather than a gradient-based parcellation. This means that relatively subtle differences in fc could occur on either side of some borders. The parcellation agrees with the resting state gradients found in HCP data however (i.e. parcels do not cross regions of strongly differing functional connectivity).
Peace, Matt. From: David Hartman <dhartman1...@gmail.com<mailto:dhartman1...@gmail.com>> Date: Tuesday, July 18, 2017 at 6:45 PM To: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>> Cc: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation) I noticed that the parcellation of the nodes into 360 ROIs (Glasser 2016) does not "fit" well into networks (ie, Yeo parcellation). There are nodes in a given ROI that belong to multiple networks. Are you aware of any work that has tried to bridge your ROI parcellation with some of the network parcellations or is this something any of the folks at HCP have explored? Thank you, David Hartman On Mon, Jul 17, 2017 at 7:55 PM, Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> wrote: As Matt says, 32k is used to roughly match the acquisition resolution. The 7T data at 1.6mm is on a 59k mesh for the same reasons. If you want to resample them, see the -cifti-resample command (or -metric-resample for a simpler command that does the same surface operation on a different format). Side note though, in order to get the data onto 32k or 59k, we do in fact first map the volume data to the native freesurfer mesh for the subject (~140k if memory serves), in order to use the surfaces that contain the original folding detail (even though 32k surfaces still have very good folding detail). After that, we downsample them on the surface to the more sensible resolution. Tim On Mon, Jul 17, 2017 at 6:44 PM, Glasser, Matthew <glass...@wustl.edu<mailto:glass...@wustl.edu>> wrote: No that would be a massive oversampling of the data. The data are acquired at 2mm isotropic, which is roughly the vertex spacing of the 32k mesh. 164k files would be much larger for no benefit. If you want to upsample particular maps to 164k (or downsample something to 32k) that is trivial to do. Peace, Matt. From: <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of David Hartman <dhartman1...@gmail.com<mailto:dhartman1...@gmail.com>> Date: Monday, July 17, 2017 at 6:24 PM To: Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Cc: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation) Yes it does seem the first of four columns has 8 distinct numbers. As a side note, is there any released HCP resting state data on a 164k mesh or are they all 32k mesh? Thank you, David Hartman On Mon, Jul 17, 2017 at 6:19 PM, Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> wrote: You can get a text file containing the label names and keys for a map by using -cifti-label-export-table. It appears that this will have extraneous labels in it due to how the file was generated, but you can ignore the key values that aren't used. If you count the number of unique values in one column of the cifti file you opened, you will probably get the number you are expecting. It is simply that the values are not 1-7, but an arbitrary set of integers. The -cifti-parcellate command will automatically use the first map in the label file to compute within-label averages for its labels. It is probably not what you want to use to simply examine the label map. I'm not sure why it ended up with 8 rather than 7 parcels, though. As a side effect, you could use "wb_command -cifti-parcel-mapping-to-label" to generate a label file that should be much less cluttered, but we may have better advice on this soon... Tim On Mon, Jul 17, 2017 at 4:51 PM, David Hartman <dhartman1...@gmail.com<mailto:dhartman1...@gmail.com>> wrote: Since calling in MATLAB: “ciftiopen('RSN-networks.32k_fs_LR.dlabel.nii','workbench\bin_windows64\wb_command.exe')” gives me a matrix with 4 columns whose range is beyond the 17 or 7 corresponding to their functional grouping, I have resorted to using wb-command hoping to get right labelling. Question: Which wb-command should I use to read the labels and see for the 32k mesh which networks the nodes belong to (ie. 1 to 7)? I tried using wb_command -cifti-parcellate rfMRI_REST1_LR_Atlas_MSMAll.dtseries.nii RSN-networks.32k_fs_LR.dlabel.nii COLUMN out.ptseries.nii, but out.ptseries.nii returns a matrix 8×1200. But I am hoping for something 32k×1 for the correct labelling, where each row is a number between 1 and 7 or 17 corresponding to the group the node belongs to. Hope it is not too confusing. Thank you, David Hartman On Mon, Jul 17, 2017 at 3:09 PM, Harms, Michael <mha...@wustl.edu<mailto:mha...@wustl.edu>> wrote: Hi, There are actually 4 different maps in that file. If you load it into Workbench, the name associated with each map tells you what each map is. cheers, -MH -- Michael Harms, Ph.D. ----------------------------------------------------------- Conte Center for the Neuroscience of Mental Disorders Washington University School of Medicine Department of Psychiatry, Box 8134 660 South Euclid Ave.Tel: 314-747-6173<tel:(314)%20747-6173> St. Louis, MO 63110Email: mha...@wustl.edu<mailto:mha...@wustl.edu> From: <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of David Hartman <dhartman1...@gmail.com<mailto:dhartman1...@gmail.com>> Date: Monday, July 17, 2017 at 12:21 PM To: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation) Hi, Background: Regarding the labels file, “RSN-networks.32k_fs_LR.dlabel.nii” which I thought should contain the 7 and 17 network parcellation, this file has a matrix of size 64984×4. What do the numbers in the 4 columns represent (ie. 1st column has a max of 44 and 4th column a max of 26). I was expecting a single column that took values from 1 to 17 or 1 to 7 mapping each vertex to its grouping in the functional networks. Question: How should I understand these 4 columns and their connection to functional network parcellation? Thank you, David Hartman On Fri, Jul 14, 2017 at 2:27 PM, David Hartman <dhartman1...@gmail.com<mailto:dhartman1...@gmail.com>> wrote: Hi, Background: Regarding the parcellation of the cortex into functional networks (“The organization of the human cerebral cortex estimated by intrinsic functional connectivity,” Yeo et al.) Yeo breaks up the cortex into 7 networks. However, his cortical data has 163842 vertices, while the HCP data only has 59412 vertices. Question: I am looking to map the HCP data into these 7 networks, but I don’t see a way to get the data into the same format as Yeo’s data (ie. 163842 vertices) to use his mapping. 1. Does anyone know of a way to convert HCP data into the same format as Yeo’s data to use his mapping or a direct way to map the HCP data to 7 networks? Any help would be much appreciated. Thank you, David Hartman _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> 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. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited. 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