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**edges counted twice in gPPI**Showing 1-10 of 10 posts

Nov 9, 2021 01:11 AM | Laura Jett

edges counted twice in gPPI

Hello,

We are running an ROI-ROI, gPPI analysis and have noticed that each edge/connection is being counted twice, and we hoped to get some advice on this. When doing the gPPI analysis, both terms' edges (i.e. both node A time course * task predicts node B time course and B * task predicts A time course) go into the group-level analysis; so, for example, when looking at 27 ROIs, the result should be 351 connectors, but instead we get double that (so 702). This really limits the ability to run this kind of analysis at a network level.

Is there any way to resolve this issue, or perhaps integrate a number of potential solutions (eg, add options to include the mean of the edges as one edge, or the max)? Given that integrating task-based analyses and network-level analyses could really be an important way forward in fMRI, we think that offering ways to address this limitation could be really useful for many groups.

Thank you for your help!

Laura

We are running an ROI-ROI, gPPI analysis and have noticed that each edge/connection is being counted twice, and we hoped to get some advice on this. When doing the gPPI analysis, both terms' edges (i.e. both node A time course * task predicts node B time course and B * task predicts A time course) go into the group-level analysis; so, for example, when looking at 27 ROIs, the result should be 351 connectors, but instead we get double that (so 702). This really limits the ability to run this kind of analysis at a network level.

Is there any way to resolve this issue, or perhaps integrate a number of potential solutions (eg, add options to include the mean of the edges as one edge, or the max)? Given that integrating task-based analyses and network-level analyses could really be an important way forward in fMRI, we think that offering ways to address this limitation could be really useful for many groups.

Thank you for your help!

Laura

Dec 1, 2021 02:12 AM | Rany Abend

RE: edges counted twice in gPPI

Any chance this could be
acknowledged?...

We have a couple of studies that depend on this kind of analysis.

Thank you for your help,

Rany

We have a couple of studies that depend on this kind of analysis.

Thank you for your help,

Rany

*Originally posted by Laura Jett:*Hello,

We are running an ROI-ROI, gPPI analysis and have noticed that each edge/connection is being counted twice, and we hoped to get some advice on this. When doing the gPPI analysis, both terms' edges (i.e. both node A time course * task predicts node B time course and B * task predicts A time course) go into the group-level analysis; so, for example, when looking at 27 ROIs, the result should be 351 connectors, but instead we get double that (so 702). This really limits the ability to run this kind of analysis at a network level.

Is there any way to resolve this issue, or perhaps integrate a number of potential solutions (eg, add options to include the mean of the edges as one edge, or the max)? Given that integrating task-based analyses and network-level analyses could really be an important way forward in fMRI, we think that offering ways to address this limitation could be really useful for many groups.

Thank you for your help!

Laura

We are running an ROI-ROI, gPPI analysis and have noticed that each edge/connection is being counted twice, and we hoped to get some advice on this. When doing the gPPI analysis, both terms' edges (i.e. both node A time course * task predicts node B time course and B * task predicts A time course) go into the group-level analysis; so, for example, when looking at 27 ROIs, the result should be 351 connectors, but instead we get double that (so 702). This really limits the ability to run this kind of analysis at a network level.

Is there any way to resolve this issue, or perhaps integrate a number of potential solutions (eg, add options to include the mean of the edges as one edge, or the max)? Given that integrating task-based analyses and network-level analyses could really be an important way forward in fMRI, we think that offering ways to address this limitation could be really useful for many groups.

Thank you for your help!

Laura

Dec 3, 2021 12:12 AM | Alfonso Nieto-Castanon -

*Boston University*RE: edges counted twice in gPPI

Hi Laura,

I am not sure if I am understanding correctly your question, but if you are referring to the '

Best

Alfonso

I am not sure if I am understanding correctly your question, but if you are referring to the '

*alternative settings for network-based inferences: Network Based Statistics*' option in the ROI-to-ROI results explorer window, you are right that the current implementation of NBS uses an undirected graph when grouping connections into networks (and if, as in your case, the original adjacency matrix A is asymmetric, CONN will consider the matrix A|A' as the new symmetric adjacency matrix when determining groups of connections conforming a 'network') but after that the computation of both network-size and network-mass use the original (possibly asymmetric) graph and connectivity values (so that each "direction" of an edge is counted once if appropriate). I am not sure I understand why you find this limits the ability to run analyses at the network level, if you could please clarify.Best

Alfonso

*Originally posted by Laura Jett:*Hello,

We are running an ROI-ROI, gPPI analysis and have noticed that each edge/connection is being counted twice, and we hoped to get some advice on this. When doing the gPPI analysis, both terms' edges (i.e. both node A time course * task predicts node B time course and B * task predicts A time course) go into the group-level analysis; so, for example, when looking at 27 ROIs, the result should be 351 connectors, but instead we get double that (so 702). This really limits the ability to run this kind of analysis at a network level.

Is there any way to resolve this issue, or perhaps integrate a number of potential solutions (eg, add options to include the mean of the edges as one edge, or the max)? Given that integrating task-based analyses and network-level analyses could really be an important way forward in fMRI, we think that offering ways to address this limitation could be really useful for many groups.

Thank you for your help!

Laura

We are running an ROI-ROI, gPPI analysis and have noticed that each edge/connection is being counted twice, and we hoped to get some advice on this. When doing the gPPI analysis, both terms' edges (i.e. both node A time course * task predicts node B time course and B * task predicts A time course) go into the group-level analysis; so, for example, when looking at 27 ROIs, the result should be 351 connectors, but instead we get double that (so 702). This really limits the ability to run this kind of analysis at a network level.

Is there any way to resolve this issue, or perhaps integrate a number of potential solutions (eg, add options to include the mean of the edges as one edge, or the max)? Given that integrating task-based analyses and network-level analyses could really be an important way forward in fMRI, we think that offering ways to address this limitation could be really useful for many groups.

Thank you for your help!

Laura

Jan 5, 2022 10:01 PM | Laura Jett

RE: edges counted twice in gPPI

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

Jan 10, 2022 03:01 PM | Rany Abend

RE: edges counted twice in gPPI

Hi,

I randomly came across a paper in which the upper and lower triangles were averaged in a PPI ROIxROI matrix (https://doi.org/10.1016/j.jad.2021.05.08...).

It might be that this is the most practical solution if one wants to run network analyses for a PPI design (with the other option of entering both sets of directed edges seeming potentially invalid).

Thanks,

Rany

I randomly came across a paper in which the upper and lower triangles were averaged in a PPI ROIxROI matrix (https://doi.org/10.1016/j.jad.2021.05.08...).

It might be that this is the most practical solution if one wants to run network analyses for a PPI design (with the other option of entering both sets of directed edges seeming potentially invalid).

**Alfonso**: what needs to be done in order to end up with a matrix of averaged triangles (resulting in one triangle and NAs?) that would then allow for network analyses?Thanks,

Rany

Jan 18, 2022 01:01 PM | Rany Abend

RE: edges counted twice in gPPI

Just bringing this up again, in case
anyone has any experience with running network analyses with a gPPI
approach.

Thanks,

Rany

Thanks,

Rany

*Originally posted by Rany Abend:*Hi,

I randomly came across a paper in which the upper and lower triangles were averaged in a PPI ROIxROI matrix (https://doi.org/10.1016/j.jad.2021.05.08...).

It might be that this is the most practical solution if one wants to run network analyses for a PPI design (with the other option of entering both sets of directed edges seeming potentially invalid).

Thanks,

Rany

I randomly came across a paper in which the upper and lower triangles were averaged in a PPI ROIxROI matrix (https://doi.org/10.1016/j.jad.2021.05.08...).

It might be that this is the most practical solution if one wants to run network analyses for a PPI design (with the other option of entering both sets of directed edges seeming potentially invalid).

**Alfonso**: what needs to be done in order to end up with a matrix of averaged triangles (resulting in one triangle and NAs?) that would then allow for network analyses?Thanks,

Rany

Jan 22, 2022 05:01 PM | Alfonso Nieto-Castanon -

*Boston University*RE: edges counted twice in gPPI

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

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

Jan 22, 2022 05:01 PM | Rany Abend

RE: edges counted twice in gPPI

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

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

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

Jan 22, 2022 11:01 PM | Alfonso Nieto-Castanon -

*Boston University*RE: edges counted twice in gPPI

Hi Rany,

Yes, one relatively simple way would be the following:

1) use the '

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

Yes, one relatively simple way would be the following:

1) use the '

**' 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***export data*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

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

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

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:*Kind regards,

Laura

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

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 '

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

Yes, one relatively simple way would be the following:

1) use the '

**' 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***export data*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

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

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

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:*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:*

Kind regards,

Laura