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, 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 63110Email: 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
>>>>>>
>>>>>>
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