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help > RE: edges counted twice in gPPI
Jan 23, 2022 02:01 AM | Rany Abend
RE: edges counted twice in gPPI
Thanks so much, Alfonso.
we'll give it a try.
We really appreciate it.
Rany
Originally posted by Alfonso Nieto-Castanon:
we'll give it a try.
We really appreciate it.
Rany
Originally posted by Alfonso Nieto-Castanon:
Hi Rany,
Yes, one relatively simple way would be the following:
1) use the 'export data' button on the 2nd-level ROI-to-ROI results window to export the original (asymmetric) ROI-to-ROI matrices for all subjects into a single datafile
2) then take the data in those matrices and average it with its transpose, e.g. using something like:
[data,names,coords,samples] = conn_mtx_read('/myfolder/data.nii'); % read from file
data = (data + permute(data,[2,1,3])) / 2; % average upper and lower triangular parts
conn_mtx_write('/myfolder/symdata.nii', data, names, coords, samples); % write to file
and 3) then enter your new (symmetric) ROI-to-ROI matrices into a new 2nd-level analysis, e.g. using something like:
conn_module( 'glm' , ...
'data', '/myfolder/symdata.nii', ...
'design_matrix', X, ...
'contrast_between', C, ...
'contrast_within', M, ...
'folder', '/myfolder/symanalyses/' )
entering in the X/C/M matrices the 2nd-level analysis associated design matrix / between-subjects contrast / within-subjects contrast, respectively (see https://web.conn-toolbox.org/resources/g... for details about the GLM CONN module)
Hope this helps
Alfonso
Originally posted by Rany Abend:
Yes, one relatively simple way would be the following:
1) use the 'export data' button on the 2nd-level ROI-to-ROI results window to export the original (asymmetric) ROI-to-ROI matrices for all subjects into a single datafile
2) then take the data in those matrices and average it with its transpose, e.g. using something like:
[data,names,coords,samples] = conn_mtx_read('/myfolder/data.nii'); % read from file
data = (data + permute(data,[2,1,3])) / 2; % average upper and lower triangular parts
conn_mtx_write('/myfolder/symdata.nii', data, names, coords, samples); % write to file
and 3) then enter your new (symmetric) ROI-to-ROI matrices into a new 2nd-level analysis, e.g. using something like:
conn_module( 'glm' , ...
'data', '/myfolder/symdata.nii', ...
'design_matrix', X, ...
'contrast_between', C, ...
'contrast_within', M, ...
'folder', '/myfolder/symanalyses/' )
entering in the X/C/M matrices the 2nd-level analysis associated design matrix / between-subjects contrast / within-subjects contrast, respectively (see https://web.conn-toolbox.org/resources/g... for details about the GLM CONN module)
Hope this helps
Alfonso
Originally posted by Rany Abend:
Thanks, Alfonso.
This is helpful.
In case we wanted to average the top and bottom triangles, rather than enter both triangles into network analyses, could you help us figure out which data elements to average?
Thanks again,
Rany
Originally posted by Alfonso Nieto-Castanon:
In case we wanted to average the top and bottom triangles, rather than enter both triangles into network analyses, could you help us figure out which data elements to average?
Thanks again,
Rany
Originally posted by Alfonso Nieto-Castanon:
Hi
Laura,
Regarding (1), yes, NBS statistics are derived from the results of randomization/permutation simulations so they remain valid irrespective of how the networks are defined (e.g. as sets of connected nodes from directed or indirected graphs). I am not sure what you mean by "how do the statistics work if both directed edges are counted", the network-intensity measure from a network is computed by summing the number of suprathreshold edges within the network. For example, if a network of two areas A&B is identified and A->B is significant and B->A is not then the network intensity is 1, if both are significant then the network intensity is 2. The same logic applies to any other network computed during the permutation/randomization runs, so the final network-intensity statistics are derived by comparing the intensity of this network to the distribution of network intensities expected if the null hypothesis was true.
And regarding (2), the interpretation of directionality differences in gPPI analyses is debated, unless you want to make a specific case about the directionality of specific connections within any of your significant networks I would generally recommend simply describing each network in terms of the nodes/ROIs within each network and the connections between them, rather than focusing on the specific directionality of these edges/connections (but it is perfectly fine either way)
Hope this helps
Alfonso
Originally posted by Laura Jett:
Regarding (1), yes, NBS statistics are derived from the results of randomization/permutation simulations so they remain valid irrespective of how the networks are defined (e.g. as sets of connected nodes from directed or indirected graphs). I am not sure what you mean by "how do the statistics work if both directed edges are counted", the network-intensity measure from a network is computed by summing the number of suprathreshold edges within the network. For example, if a network of two areas A&B is identified and A->B is significant and B->A is not then the network intensity is 1, if both are significant then the network intensity is 2. The same logic applies to any other network computed during the permutation/randomization runs, so the final network-intensity statistics are derived by comparing the intensity of this network to the distribution of network intensities expected if the null hypothesis was true.
And regarding (2), the interpretation of directionality differences in gPPI analyses is debated, unless you want to make a specific case about the directionality of specific connections within any of your significant networks I would generally recommend simply describing each network in terms of the nodes/ROIs within each network and the connections between them, rather than focusing on the specific directionality of these edges/connections (but it is perfectly fine either way)
Hope this helps
Alfonso
Originally posted by Laura Jett:
Thank you for your reply! We understand why we
end up with an asymmetric matrix; however, we are still unsure on a
few points:
Kind regards,
Laura
- Does the matrix need to be symmetric for network-based statistics to be valid? If network-based statistics are valid with an asymmetric matrix, then how do the statistics work if both "directed edges" are counted?
- What is the interpretation? For example, if A → B is part of a significant cluster, can we infer the directionality of this edge?
Kind regards,
Laura
Threaded View
| Title | Author | Date |
|---|---|---|
| Laura Jett | Nov 9, 2021 | |
| Rany Abend | Jan 10, 2022 | |
| Rany Abend | Jan 18, 2022 | |
| Alfonso Nieto-Castanon | Dec 3, 2021 | |
| Laura Jett | Jan 5, 2022 | |
| Alfonso Nieto-Castanon | Jan 22, 2022 | |
| Rany Abend | Jan 22, 2022 | |
| Alfonso Nieto-Castanon | Jan 22, 2022 | |
| Rany Abend | Jan 23, 2022 | |
| Rany Abend | Dec 1, 2021 | |