ft_read_cifti.m does extra stuff that may be confusing, we don't do spatial
operations in matlab.

I would recommend putting the volume-space mask into surface space, rather
than trying to put the surface-based data back into volume space.  There is
less information to be lost in a binary mask than in timeseries data.

As we have implied, defining a cortical ROI in MNI volume space is
inherently defining it on a rather blurry map, as far as brain function is
concerned, because MNI registration is (so far) based only on the shape of
the brain, which is both quite variable across subjects over large expanses
of cortex, and because function does not always line up with these brain
shape features.

However, it is possible to map such an ROI onto the surface, taking into
account this blurriness, but it won't be binary anymore, and it will reveal
what parts of cortex it is grabbing that you probably didn't want it to.
To do so, you can take a large number (at a guess, 30+, more is better but
takes longer) of randomly-chosen HCP subjects, and use wb_command
-volume-to-surface-mapping with -ribbon-constrained and each subject's
MNINonLinear pial and white surfaces.  This will give you how your group
ROI intersects each subject's cortical ribbon.  Then, you can average this
across subjects to see on average, what parts of subjects' cortex are being
captured by your ROI.  If you need it binary again, you will have to
threshold it, and you might want to also use wb_command
-metric-remove-islands (as your MNI space ROI will almost certainly cross
to the opposite sulcal bank fairly strongly).

Tim


On Thu, Jul 19, 2018 at 6:25 AM, Aslan Satary Dizaji <[email protected]>
wrote:

> Thank you very much for both responses. Also, thank you for the insightful
> paper.
>
> Here, I am going to describe a processing pipeline and I was wondering if
> you could tell me if there is something wrong with this pipeline:
>
> 1) We read "rfMRI_REST{1,2}_{LR,RL}_Atlas_MSMAll_hp2000_clean.dtseries.nii"
> with "ft_read_cifti.m" and "${subject}.{L,R}.
> midthickness_MSMAll.32k_fs_LR.surf.gii" with "gifti.m" of each subject.
>
> 2) We save four fields of the generated structure by "ft_read_cifti.m":
>     2-1) "dtseries" which contains the time-series of subcortical voxels
> and cortical grayordinates.
>     2-2) "pos" which, I assume, contains the MNI coordinates of
> subcortical voxels. [?]
>     2-3) "brainstructure" which labels each structure of brain with
> different numbers.
>     2-4) "transform" which, I assume, has the matrix for transformation of
> coordinates from MNI-mm space to MNI-matrix space (91,109,91). [?]
>
> 3) We save only the "vertices" field of the generated structure by
> "gifti.m" which, I assume, contains the coordinates of cortical
> grayordinates for left or right hemisphere in the MNI-mm space. [?]
>
> 4) Let's assume further that we want to apply a particular 3D mask in the
> MNI-matrix space (91,109,91).
>
> 5) By using "brainstructure" and "vertices", we can complete the "pos"
> with the correct coordinates of cortical grayordinates.
>
> 6) By using "transform", we can transform the coordinates of "pos" from
> the MNI-mm space to the MNI-matrix space (91,109,91).
>
> 7) Now we have a complete set of time-series from "dtseries" with their
> corresponding coordinates in the MNI-matrix space (91,109,91).
>
> 8) At this stage, we can apply our mask to this data.
>
> Thank you in advance for your time and consideration.
>
> Best regards,
>
> Aslan
>
> On Thu, Jul 19, 2018 at 1:15 AM Timothy Coalson <[email protected]> wrote:
>
>> As an additional note, if you do use the non-MSMAll data, you should not
>> use an MSMAll surface with it.  The data and surface file should go through
>> the same registration and resampling.  The files without MSMAll in their
>> names are registered/resampled using MSMSulc.
>>
>> Tim
>>
>>
>> On Wed, Jul 18, 2018 at 8:16 AM, Glasser, Matthew <[email protected]>
>> wrote:
>>
>>> 1.  rfMRI_REST1_LR_Atlas_MSMAll_hp2000_clean.dtseries.nii
>>> 2-3. 100307.L.midthickness_MSMAll.32k_fs_LR.surf.gii
>>>
>>> Note that the surface coordinates or only valid for single individuals.
>>> There is no “standard space” (like MNI space) that properly lines up the
>>> cortical areas, instead surface registration brings the data onto a
>>> standard 32k mesh where vertex 1000 has the same neuroanatomical location
>>> across subjects.  You probably don’t actually need the surface coordinates
>>> for your analysis unless you are doing something unusual.
>>>
>>> Peace,
>>>
>>> Matt.
>>>
>>> From: Aslan Satary Dizaji <[email protected]>
>>> Date: Wednesday, July 18, 2018 at 12:57 AM
>>> To: Matt Glasser <[email protected]>
>>> Cc: "[email protected]" <[email protected]>,
>>> Mohammad Reza Khodaei <[email protected]>
>>> Subject: Re: [HCP-Users] The coordinates of surface time-series of
>>> Resting State fMRI FIX-Denoised (Compact)
>>>
>>> Dear Matt,
>>>
>>> Thank you for your email.
>>>
>>> In "Resting State fMRI FIX-Denoised (Compact)" of 100 unrelated subjects,
>>> inside folder "${StudyFolder}/${Subject}/MNINonLinear", there is only
>>> one folder, "Results", which inside it there are four folders
>>> "rfMRI_REST1_LR", "rfMRI_REST1_RL", "rfMRI_REST2_LR", "rfMRI_REST2_RL". And
>>> finally inside each one of these folders, there are two "*.dtseries.nii"
>>> files and two "*.dtscalar.nii" files. So basically, in "Resting State
>>> fMRI FIX-Denoised (Compact)" of 100 unrelated subjects, there is not any
>>> folder with the name of "fsaverage_LR32k". However, I checked the other
>>> data-sets of 100 unrelated subjects, and I found that "Structural
>>> Preprocessed" of 100 unrelated subjects has the  "fsaverage_LR32k"
>>> folder with the files that you mentioned. So my question is that, do we
>>> need to download this data-set too so to be able to get the coordinates of
>>> surface time-series?
>>>
>>> Also, I have three other questions:
>>>
>>> 1) Which one of these "*.dtseries.nii" files do you recommend that we
>>> use:
>>>
>>> rfMRI_REST1_LR_Atlas_hp2000_clean.dtseries.nii
>>> rfMRI_REST1_LR_Atlas_MSMAll_hp2000_clean.dtseries.nii
>>>
>>> 2) For example, for subject 100307 and for left hemisphere, which one of
>>> these three "*.surf.gii" files do you recommend that we use:
>>>
>>> 100307.L.inflated.32k_fs_LR.surf.gii
>>> 100307.L.midthickness.32k_fs_LR.surf.gii
>>> 100307.L.very_inflated.32k_fs_LR.surf.gii
>>>
>>> 3) I assume that with "rfMRI_REST1_LR_Atlas_hp2000_clean.dtseries.nii",
>>> we should use one of the three above "*.surf.gii" files and with "
>>> rfMRI_REST1_LR_Atlas_MSMAll_hp2000_clean.dtseries.nii", we should use
>>> one of the three below "*.surf.gii" files:
>>>
>>> 100307.L.inflated_MSMAll.32k_fs_LR.surf.gii
>>> 100307.L.midthickness_MSMAll.32k_fs_LR.surf.gii
>>> 100307.L.very_inflated_MSMAll.32k_fs_LR.surf.gii
>>>
>>> Am I right?
>>>
>>> Many many thanks in advance for your time and consideration.
>>>
>>> Best regards,
>>>
>>> Aslan
>>>
>>>
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>>

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