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Hi Shrikanth,
Thank you for using Braph. Braph uses an FDR correction, which can indicate which edges remain significant after correcting for multiple comparisons. Additionally, one could visualize the subject matrices and check which edges have the strongest weight.
However, as there are many different methods published in the literature regarding selection of most important edges, we implemented Braph 2.0 in a way that would allow you to easy implement any method you prefer. We would be happy to guide you through the process of implementing any method that you think can be best utilized in your research.
Best wishes,
MiteDear Keivan,
Thank you for contacting us and for using Braph for your analysis.
The betweenness centrality in Braph has to option to provide both normalized and unnormalized values when used from the command line. However, if you are using the GUIs provided by Braph, then the default value is the normalized version.
The measure is normalized by the number of nodes. In particular,
BC_normalized = BC_unnormalized / ((N-1)*(N-2)),
where N is the number of nodes in the network.Hope this helps. Please let me know if can do anything else to help.
Best wishes,
MiteHi,
Thank you for considering BRAPH for your research and I am sorry to hear you are experiencing problems with your analysis. We would be very happy to assist you with this issue and we can do so in 2 ways:
1) You could upload your data directly in BRAPH and generate the correlation matrices within the software. If you would like to calculate these matrices using some methods that are not currently available in BRAPH, please let us know and we will do our best to help you and add these specific methods to our pipelines. The advantage of this method would be that BRAPH will store all parameters you used to calculate the matrices, which would aid in the reproducibility of your findings.
2) BRAPH 2.0 comes with several pipelines that are termed ‘Connectivity’ (followed by the type of analysis). These pipelines will allow you to import your correlation matrices into BRAPH and perform all the steps of the network analysis.
Please let us know if there is anything additional that we can do to help. Our website has some detailed tutorials that could guide you through the analysis, however, please do not hesitate to ask us if you need more information.
Best wishes,
MiteHi Aaron,
The run time on the MRIComparisonWU would depend on the size of your network, number of measures and which measures you will calculate, but it will also be highly dependent on the number of permutations that you choose to perform.
This issue arises due to the methods used to calculate the null distributions for the MRI covariance networks. Namely, the permutations are performed at a subject data level. For each permutation, two new groups are formed and the measures are recalculated for each group.
Therefore, I would suggest calculating your data measure by measure starting from a small number of permutations and then gradually increase the number of permutations. This should give you a robust estimate of how much it will take to calculate all measures. As a rule of thumb, the measures related to global and local efficiency as well as the small-worldness measure take longer computational time.
Please let me know if there is anything I can do to help additionally.
Best wishes,
MiteHi Shrikanth,
Thank you for sharing the information regarding your analysis. I have met with my colleagues and we thing that this analysis can be done in Braph.
In order to know how to proceed with the implementation we would need several technical details regarding your analysis. I was wondering whether you would be available for a short meeting so that we can discuss them and decide on a plan on how to proceed with the implementation? Please contact me on my e-mail so that we can agree on a meeting time.
Looking forward to your response and the meeting.
Best,
MiteHi Shrikanth,
Of course, we can try to help you with the implementation of this pipeline.
To transfer the data from SPM software to Braph2, you would first need to export the data from SPM in .txt, .xls or .mat format. Then, if you have one .txt file per subject (that holds the connectivity matrix that is associated with the subject), you can group all the subjects from same group within a folder and import that folder in the cohort part of Braph2.
In order for us to be able to provide you with detailed information about how to proceed, we would need more information about the nature of the analysis that you want to perform. For example, given the A matrix from DCM for each subject, do you want to analyze the topology of this matrix? Alternatively, do you want to use also B and C matrices in some way?
I am looking forward to your response and will get back to you with more detailed explanation of how to proceed with the analysis.
Best,
MiteHi Shrikanth,
Thank you for showing an interest in Braph 2.0 and hope that it will prove to be a useful tool for your research.
The answer to your question is yes, graph analysis can be used to analyze the DCM matrices and provide explanation of the topological properties of the nodes. You can do it with Braph 2. While the pipelines for such analyses are not implemented to the core release of the software, it comes with all the tools needed to perform this analysis (directed measures are implemented).
Together with the release of the software, We will post detailed explanations of how to implement additional pipelines in the software (it will be only several lines of code written in Matlab according to predetermined rules). On other hand, we can also assist you in implementing this pipeline.
If you have any additional questions in meantime, please do not hesitate to ask and we will try to help as much as possible.
Best,
MiteHi,
Since you are doing binary analysis, there is a good possibility that you encounter this issue due to the fact that some nodes are disconnected from the network at those densities. Currently in Braph the characteristic path length is calculated as a mean over the nodal path lengths of all nodes. This averaging assumes a connected networks, and will return NaN for the networks that have disconnected nodes.
One way is to calculate characteristic path length within connected subgraphs (also available in Braph). Alternatively, you could employ a different averaging procedure that would take into account the disconnected nodes; taking a harmonic mean of the nodal measures is a possible option.
If you would like to change the code in Braph, for binary undirected graphs, the characteristic path length is calculated on the lines 463/464 in GraphBU.m file. You could use any definition of the path length on line 464 and the code would work accordingly. Otherwise, please let me know if you need help and I can help you with any modification of the code you might need.
Best wishes,
MiteHi Daniela,
Braph’s interface might change slightly depending whether you would need to do weighted or binary analysis. For example, in the binary analysis of global measures you have plot view and table view that show the measures as a function of density/threshold. On the other hand, in the weighted analysis the results are presented in a table only, which is due to the fact we have only a single number characterizing the network topology.
Could you please share a bit more information about the interface at which you encounter this problem and the type of analysis you would like to do. If you could send me some snapshots that would be great, so that I can try and help you fix the issue.
Best wishes,
MiteHi Cho,
Thank you for using Braph.
The issue with small worldness (and characteristic path length) is that they cannot be calculated (especially for small densities) due to the disconnections of some nodes in the graph. This is due to their current implementation, in which first the nodal path lengths for each node are calculated and then they are averaged out in order to calculate the characteristic path length. Therefore, if any disconnected nodes exist in the graph, the nodal path length will return NaN and the characteristic path length cannot be calculated.
As an alternative, you might want to calculate them only within connected subgraphs, where there are no disconnected nodes. If you calculate the small worldness from the command line, you can specify a second argument (“wsg”) as a function input, which ,if set to true, will calculate the small worldness within the connected component of the network. You can find the codes for this on line 1427 in the file Graph.m (for calculating the small worldness) and on line 460 in GraphBU.m (within the method “measure”) for the differences of calculating the path length only within the connected components. Currently, the default value of this argument is set to false. If you would like to change this so that the small worldness is always calculated within the connected components you can do so by changing line 1439 of Graph.m file.
Another alternative method is to implement the harmonic mean as I explained above.
I realize that the calculation of the small-worldness takes a lot of time. This is due to the fact that in order to calculate the small-worldness the network needs to be randomized many times, and currently, the randomization algorithm implemented in Braph is very slow. We hope that we can implement faster alternative methods in Braph soon.
Hope this helps you. Please do not hesitate to ask if you would need any more help.
Best,
Mite
