Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
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 networ
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> 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> > 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> on behalf of David Hartman >> <dhartman1...@gmail.com> >> Date: Monday, July 17, 2017 at 6:24 PM >> To: Timothy Coalson <tsc...@mst.edu> >> Cc: "hcp-users@humanconnectome.org" <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> 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> >>> 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. >>>
Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
Please use our CIFTI tool for now (option 2B): https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ We are planning to modify the CIFTI tools that were originally written for field trip to work better with brain imaging data in the future. Peace, Matt. From: David Hartman <dhartman1...@gmail.com<mailto:dhartman1...@gmail.com>> Date: Tuesday, July 18, 2017 at 12:05 PM To: Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Cc: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>>, "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) Thank you for your responses. My data is indeed in a 32k (per hemisphere) mesh. My question pertains to what counts as a medial wall? My impression (but please correct me if I am wrong) is that when using the MATLAB command ciftiopen, there are 64.9k vertices (32k per hemisphere). and when using the MATLAB command ft_read_cifti, there are ~59.5k vertices. The results from the ft_read_cifti produce around 5.5k NANS corresponding to the discrepancy between ciftiopen and ft_read_cifti. My impression is that these ~5.5k NANS are the medial wall vertices. On the other hand, when using the "RSN-networks.32k_fs_LR.dlabel.nii”, there are 8 distinct numbers. From workbench (cifti-label-export-table) I notice that one of these numbers corresponds to FreeSurfer_Defined_Medial_Wall, while the other 7 to 7Networks_X. The NANS and the FreeSurferDefined_Medial_Wall vertices match up mostly. However, there is a gap. There is only a ~5k overlap between the ~5.5k NANs and the vertices corresponding to the group: FreeSurfer_Defined_Medial_Wall. Any ideas why the groups would be slightly different (mismatch of 500 vertices) between the NANS and the FreeSurfer_Defined_Medial_Wall? 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_c
Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
Thank you for your responses. My data is indeed in a 32k (per hemisphere) mesh. My question pertains to what counts as a medial wall? My impression (but please correct me if I am wrong) is that when using the MATLAB command ciftiopen, there are 64.9k vertices (32k per hemisphere). and when using the MATLAB command ft_read_cifti, there are ~59.5k vertices. The results from the ft_read_cifti produce around 5.5k NANS corresponding to the discrepancy between ciftiopen and ft_read_cifti. My impression is that these ~5.5k NANS are the medial wall vertices. On the other hand, when using the "RSN-networks.32k_fs_LR.dlabel.nii”, there are 8 distinct numbers. From workbench (cifti-label-export-table) I notice that one of these numbers corresponds to FreeSurfer_Defined_Medial_Wall, while the other 7 to 7Networks_X. The NANS and the FreeSurferDefined_Medial_Wall vertices match up mostly. However, there is a gap. There is only a ~5k overlap between the ~5.5k NANs and the vertices corresponding to the group: FreeSurfer_Defined_Medial_Wall. Any ideas why the groups would be slightly different (mismatch of 500 vertices) between the NANS and the FreeSurfer_Defined_Medial_Wall? Thank you, David Hartman On Mon, Jul 17, 2017 at 7:55 PM, Timothy Coalson <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> > 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> on behalf of David Hartman >> <dhartman1...@gmail.com> >> Date: Monday, July 17, 2017 at 6:24 PM >> To: Timothy Coalson <tsc...@mst.edu> >> Cc: "hcp-users@humanconnectome.org" <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> 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> >>> 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,
Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
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> 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> on behalf of David Hartman < > dhartman1...@gmail.com> > Date: Monday, July 17, 2017 at 6:24 PM > To: Timothy Coalson <tsc...@mst.edu> > Cc: "hcp-users@humanconnectome.org" <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> 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> >> 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> >>> 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 >>&g
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> 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> > 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.dt >> series.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> 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 <(314)%20747-6173> >>> St. Louis, MO 63110 Email: mha...@wustl.edu >>> >>> From: <hcp-users-boun...@humanconnectome.org> on behalf of David >>> Hartman <dhartman1...@gmail.com> >>> Date: Monday, July 17, 2017 at 12:21 PM >>> To: "hcp-users@humanconnectome.org" <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> >>> wrote: >>> >>>> Hi, >>>> >>>> >>>> >>>> *Background:* >>>> >>>> Regarding the parcellation of the cortex into
Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
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> 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 <(314)%20747-6173> > St. Louis, MO 63110 Email: mha...@wustl.edu > > From: <hcp-users-boun...@humanconnectome.org> on behalf of David Hartman < > dhartman1...@gmail.com> > Date: Monday, July 17, 2017 at 12:21 PM > To: "hcp-users@humanconnectome.org" <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> > 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 > 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. If you have received this email > in error, please immediately notify the sender via telephone or return mail. > ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
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 St. Louis, MO 63110 Email: 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. If you have received this email in error, please immediately notify the sender via telephone or return mail. ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
Also you shouldn’t be using the HCP 7T fMRI data at this time. See the message about this on the list. Peace, Matt. From: <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Date: Friday, July 14, 2017 at 2:57 PM To: David Hartman <dhartman1...@gmail.com<mailto:dhartman1...@gmail.com>> 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) As for the 59k vertices you mentioned, I'm guessing you were looking at a cifti file that contains both hemispheres, and excludes the medial wall - if so, this actually uses surfaces with 32k vertices. Unfortunately, our 1.6mm 7T data was processed with a mesh that happens to use 59k-vertex surfaces for each hemisphere, so there is some potential for confusion here. You may want to read this for an explanation of the cifti format: http://www.humanconnectome.org/software/workbench-command/-cifti-help The file containing the 17-network version on balsa also contains the 7-network version as the first map: https://balsa.wustl.edu/file/show/Q2xn Tim On Fri, Jul 14, 2017 at 2:42 PM, Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> wrote: We have a version of the 17-network Yeo parcellation here: https://balsa.wustl.edu/W8wK 163842 sounds like a freesurfer resolution. If the version you have is on the freesurfer atlas, then you can resample it to ours (or resample our parcellation to freesurfer's atlas) following these instructions: https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ#HCPUsersFAQ-9.HowdoImapdatabetweenFreeSurferandHCP? FAQ 9, "How do I map data between FreeSurfer and HCP?" Note that if you want the left and right portions of each network separated, or want each contiguous piece to be a separate entity, more work is required. We have a script somewhere that does these things... Tim On Fri, Jul 14, 2017 at 1:27 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 ___ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
Most of the released HCP data are on a 32k mesh. You can find the Yeo parcellation on the 32k mesh here: https://balsa.wustl.edu/study/show/WG33 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: Friday, July 14, 2017 at 1:27 PM To: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation) 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 ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
As for the 59k vertices you mentioned, I'm guessing you were looking at a cifti file that contains both hemispheres, and excludes the medial wall - if so, this actually uses surfaces with 32k vertices. Unfortunately, our 1.6mm 7T data was processed with a mesh that happens to use 59k-vertex surfaces for each hemisphere, so there is some potential for confusion here. You may want to read this for an explanation of the cifti format: http://www.humanconnectome.org/software/workbench-command/-cifti-help The file containing the 17-network version on balsa also contains the 7-network version as the first map: https://balsa.wustl.edu/file/show/Q2xn Tim On Fri, Jul 14, 2017 at 2:42 PM, Timothy Coalsonwrote: > We have a version of the 17-network Yeo parcellation here: > > https://balsa.wustl.edu/W8wK > > 163842 sounds like a freesurfer resolution. If the version you have is on > the freesurfer atlas, then you can resample it to ours (or resample our > parcellation to freesurfer's atlas) following these instructions: > > https://wiki.humanconnectome.org/display/PublicData/HCP+ > Users+FAQ#HCPUsersFAQ-9.HowdoImapdatabetweenFreeSurferandHCP? > > FAQ 9, "How do I map data between FreeSurfer and HCP?" > > Note that if you want the left and right portions of each network > separated, or want each contiguous piece to be a separate entity, more work > is required. We have a script somewhere that does these things... > > Tim > > > On Fri, Jul 14, 2017 at 1:27 PM, David Hartman > 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 >> http://lists.humanconnectome.org/mailman/listinfo/hcp-users >> > > ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
Re: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
Hi David, With 59k vertices, it sounds like you are using the versions of the HCP individual data that was preprocessed at 1.6mm resolution for use with the 7T data contained in the "Structural Preprocessed for 7T (1.6mm/59k mesh)" package in ConnectomeDB. The 3T "Structural Preprocessed" package contains both 164k and 32k resolution Conte69-registered standard mesh cortical surfaces in MNI space in the {Subject_ID}/MNINonLinear/ and {Subject_ID}/MNINonLinear/fsaverage_32k directories, respectively. In addition, 32k versions of the 7 and 17 Network versions of the Yeo et al. 2011 parcellation are included in the RSN-networks.32k_fs_LR.dlabel.nii CIFTI file that is included in the 900 Subjects group average dataset available in ConnectomeDB: https://db.humanconnectome.org/data/projects/HCP_1200 Tim Coalson can give you more info on using wb_command to do surface resampling if you want to do that as well. Best, Jenn Jennifer Elam, Ph.D. Scientific Outreach, Human Connectome Project Washington University School of Medicine Department of Neuroscience, Box 8108 660 South Euclid Avenue St. Louis, MO 63110 314-362-9387 e...@wustl.edu<mailto:e...@wustl.edu> www.humanconnectome.org<http://www.humanconnectome.org/> From: hcp-users-boun...@humanconnectome.org <hcp-users-boun...@humanconnectome.org> on behalf of David Hartman <dhartman1...@gmail.com> Sent: Friday, July 14, 2017 1:27:09 PM To: hcp-users@humanconnectome.org Subject: [HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation) 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 http://lists.humanconnectome.org/mailman/listinfo/hcp-users ___ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
[HCP-Users] mapping HCP data into 7 functional networks (using Thomas Yeo parcellation)
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 http://lists.humanconnectome.org/mailman/listinfo/hcp-users