[FieldTrip] Neighborhood of voxels in the volume source space

Tue Mar 23 16:21:51 CET 2021

Hi Jan-Mathijs,

Thank you very much for your suggestion.

I'd like to conduct the analysis in the volume space because I'm also
interested in hippocampal activity. I guess I could use The Brainnetome
Atlas as provided in fieldtrip template atlases (
https://www.fieldtriptoolbox.org/template/atlas/) for finer-grained parcels.

Thank you again for your valuable input!

Best,
Lin

On Tue, 23 Mar 2021 at 03:49, Schoffelen, J.M. (Jan Mathijs) <
jan.schoffelen at donders.ru.nl> wrote:

> Hi Lin,
>
> I agree that the AAL atlas without further subdivisions is too coarse. If
> you need a finer subdivision, you need to create one manually. I am usually
> quite happy with the granularity provided by a cortical surface based
> source model, and an refined anatomical atlas based on Conte69, as used in
> DOI:10.1523/JNEUROSCI.2271-19.2020
> <https://doi.org/10.1523/JNEUROSCI.2271-19.2020>, DOI: 10.7554/eLife.53715
> <https://doi.org/10.7554/eLife.53715> and DOI: 10.1073/pnas.1703155114
> <https://doi.org/10.1073/pnas.1703155114>.
>
> Best wishes,
> Jan-Mathijs
>
>
> On 22 Mar 2021, at 20:51, Lin Wang <wanglinsisi at gmail.com> wrote:
>
> Hi Jan-Mathijs,
>
> Thank you for your super clear explanation. I see what you mean now.
>
> I also see why it's better to run the analysis within a bigger
> anatomically defined brain region. Is there a way to divide a
> specific anatomical region into two searchlight regions based on the MNI
> coordinate? For example, I'd like to use the AAL atlas to define the left
> inferior temporal lobe, and then divide this region into two ROIs, with the
> anterior and posterior parts being separated by y=-31.
>
> Is there a way to do so? Or can I only use the existing template atlas to
> define the searchlight regions?
>
> Best,
> Lin
>
> On Mon, 22 Mar 2021 at 12:59, Schoffelen, J.M. (Jan Mathijs) <
> jan.schoffelen at donders.ru.nl> wrote:
>
>> Hi Lin,
>>
>> Not exactly. Apologies that I was not clear enough. What I meant with
>>  'the neighbourhood is implicit in their order’ is the following:
>>
>> if you take the spatial dimension of your data - including the outsiders
>> - and you would reshape it into the 3D volume, by doing something like
>> reshape(datavector, source.dim); then for a given dipole indexed with the
>> triplet of volumetric indices (ix,iy,iz) has its neighbours at
>> (ix+1,iy,iz), (ix, iy+1, iz), (ix, iy, iz+1) etc.
>>
>> Now, if you want to manually define for each of the dipole positions
>> their (6 18 or 26? neighbours, depending on whether you include only the
>> center faces, the edges or the corners of the 3x3x3 cube) you probably
>> would want to loop through the three dimensions and identify for each of
>> the dipoles which are the indices of their direct neighbours. To this end,
>> you could start with creating a ‘dummy’ variable that contains the dipole
>> indices in a volume, i.e. reshape(1:5780, [17 20 17]), and then loop
>> through all positions in the volume to identify the neighbours
>>
>>
>> neighbours = cell([17 20 17]);
>> for i = 2:16
>> for j = 2:19
>> for k =2:16
>>
>> tmp = dummy(i+[-1:1], j+[-1:1],k+[-1:1]); % this needs to be adjusted
>> according to which part of the cube you desire in your searchlight
>> neighbours{i,j,k} = tmp(:);
>>
>> end
>> end
>> end
>>
>> On a side note, I would think that doing a spatial searchlight on source
>> reconstructed MEG data is a little bit of an overkill, unless you have
>> reason to suspect that you have some local very high spatial resolution. As
>> an alternative, you could consider chunking your data into parcels -e.g.
>> based on a(n) (refined) anatomical atlas- and use those parcels as your
>> spatial searchlight.
>>
>> Best wishes,
>> Jan-Mathijs
>>
>>
>>
>> On 22 Mar 2021, at 17:19, Lin Wang <wanglinsisi at gmail.com> wrote:
>>
>> Hi Jan-Mathijs,
>>
>> Thank you very much for your response.
>>
>> I defined the dipole positions with a regular 3D grid, with a dimension
>> of 17*20*17 (following
>> https://www.fieldtriptoolbox.org/tutorial/sourcemodel/). This gives me
>> 5780 grid points in total, with 2982 grid points inside of the brain.
>>
>> You mentioned that 'the neighbourhood is implicit in their order', so can
>> I do the following for the searchlight type of analysis:
>> (1) select only the activation of the 2982 grid points
>> (2) conduct analysis to every 10 grid points that are next to each other
>> (3) loop through all the 2982 grid points
>>
>> Thank you again for your input!
>> Lin
>>
>>
>> On Fri, 19 Mar 2021 at 03:57, Schoffelen, J.M. (Jan Mathijs) <
>> jan.schoffelen at donders.ru.nl> wrote:
>>
>>> Hi Lin,
>>>
>>> The answer to your question depends on the topology of the source space.
>>> If the dipole positions are defined on a regular 3D grid, the
>>> neighbourhood is implicit in their order.
>>> If the dipole positions are defined on a cortical mesh (that includes a
>>> triangulation), the neighbourhood can be determined from the triangulation.
>>>
>>> Best wishes,
>>> Jan-Mathijs
>>>
>>>
>>> On 18 Mar 2021, at 01:59, Lin Wang <wanglinsisi at gmail.com> wrote:
>>>
>>> Hi field excerpts,
>>>
>>> I'd like to implement a searchlight type of multivariate analysis in the
>>> MEG source localized data, but I don’t know where to find the information
>>> about neighboring voxels for each voxel.
>>>
>>> I guess this is also relevant to the definition of the neighborhood when
>>> conducting cluster-based permutation tests in the source space.
>>>
>>> Could anyone help me?
>>>
>>> Thank you very much!
>>> Lin
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