Sorry for the late reply. I was travelling.

Thank you for the information. The FDR correction approach sounds good. Does Braph also have any permutation based statistics in the pipeline?

For now, could you please guide me on how to go about with the statistics in this case using FDR approach.

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…[Read more]

What kind of statistics does Braph have for within-subject comparisons?
Where, each subject has a connectome and I am interested in the most significant edges of the connetome within subjects.

Thank you.
Shrikanth

Many thanks! Please don’t hesitate to reach out if you have any questions or need assistance.

Thank you for your feedback in suing BRAPH 2.

Absolutely, it is possible to extract the learned parameters from a trained neural network model.

Let me to guide you through the process using the “example_NN_CON_WU_Classification_AdjacencyMatrix.m” script as an illustration. After line 69, kindly integrate the following code…[Read more]

I have been actively using the pipeline of neural networks. Since acquiring insights into the parameters learned by the model during training would greatly enhance me to explore the process of fine-tuning for my research.

Is it available to extract the relevant parameters from a model after it has undergone training ?

Many…[Read more]

Yes, if you select partial Pearson correlation, the covariates are considered then. So, before measures are calculated.

Best regards,
Anna

Thank you for your response. Yes, I select the Pearson with covariates. My question was rather when exactly during the processing the covariates are applied when I select this option. So, is the process as follows true ?

1. The pearson correlation matrices for each group is created considering the covariates (or using residual).
2. These…

[Read more]

If you are using a structural pipeline, you can adjust for covariates by selecting the partial Pearson correlation or partial Spearman correlation when creating the graph/network. If you do not select one of these options, covariates won’t be used in the analysis.

1. Right-click on the graph measure panel and a list will pop out. You can refer to the following screenshot.
   http://braph.org/wp-content/uploads/2023/07/plot_graph_forum_answer-2.png
2. Click on “Plot Graph” in the…

[Read more]

I was wondering at what step is the covariates applied during the computation. Is it already applied while computing measures? Or only while comparing them? I mean are the network measures computed on the residuals or it is used as ANCOVA for comparison?

Thank you.

Best,
Nabin

Is there a way to visualize the correlation matrices ? In BRAPH 1 it was very easy to see them but somehow could not find it in BRAPH 2. I am using structural BUD comparison for my analysis and would like to visualize the matrices at different densities.

Thank you for your support.

Best,
Nabin

*Note***
In Nabin’s case the saved b2 file was a Comparison of Structural groups.

[file, path, filterindex] = uigetfile(BRAPH2.EXT_ELEMENT, [‘Select the .b2 file.’]);
tmp = load(filename, ‘-mat’, ‘el’);
filename = fullfile(path, file);
tmp = load(filename, ‘-mat’, ‘el’);
comparison =…[Read more]

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…[Read more]

I’m currently using Braph for brain network analysis and have a question about the betweenness centrality measure. In the results produced by Braph, is the betweenness centrality measure normalized?

If the betweenness centrality is indeed normalized, could you please provide some details about the normalization procedure or direct me to…[Read more]

The reference manual for braph2 is already included automatically in the software. If you want to know the properties (e.g., P for CompareEnsemble or P1, P2, DIFF for ComparisonEnsemble), you can simply type the following commands in the MATLAB command window:
help CompareEnsemble
and
help ComparisonEnsemble

These commands will show you…[Read more]

strength_WU_diff = c_WU.getComparison(‘Strength’).get(‘DIFF’);
strength_WU_p1 = c_WU.getComparison(‘Strength’).get(‘P1’);
strength_WU_p2 =…[Read more]

Nice, thank you!

Best,
Yu-Wei

–
Sai Prasanna

Thanks for the further information.
Yes, based on your feedback, it sounds reasonable since, if I understand you correctly, you calculated all the available measures for all 800 subjects.

Should you have any questions, please feel free to write to us.

Best, Yu-Wei

Thanks for the feedback.
May you provide further information: what is the size of the adjacency matrix in your case? Additionally, I wanted to ask two things: (1) how long does it take to run the modified example file, example_FUN_WU.m, on your computer? and (2) have you installed the parallel computing toolbox?

For reference, on my computer…[Read more]

Thanks for bringing this to our attention. Please click on the “X” button in the top-right corner of the pop-up warning window, as shown in the attached figure, to close it. This will allow the program to launch the landing GUI of BRAPH2. Let me know if you need any further…[Read more]

Thanks for the follow-up feedback.
I would like to provide further information based on example_FUN_WU.m about the following
(1) Get individual graph metrics/graph measures.
(2) Get individual correlation matrices/adjacency matrices.

Kindly Follow the steps below:

Step 1: Copy and paste the following codes at the end of example_…[Read more]

1. I just tried as you mentioned. However, after running my script for calculating the analysis,

ms1 = cellfun(@(x) x.getMeasure(‘Degree’).memorize(‘M’), analysis.memorize(‘G_DICT’).getItems, ‘UniformOutput’, false)

gives me an error that says, “Unable to resolve the name…[Read more]

[Read more]

I would appreciate your guidance on saving the individual subject correlation matrices as well.

Also, the GUI is giving me an option of exporting the results as a JSON file only. Could you please let me know how I can save the group average results into an XLS file ?

The export to XLS file in the GUI is made on the group average results. Nevertheless, we keep the results for each subject. To obtain those, you will need to create your own script. Use ‘example_FUN_WU’ as a base, which is located in the Functional folder inside pipelines, and after calculating the analysis use the following…[Read more]

Thanks.
Sai Prasanna

We provide example data for using functional analysis pipelines. And by functional, we understand data that have a time series by region of analysis. In that case, the example data (generated data) provided under “example data FUN (fMRI)” is also valid for EEG.

In order to get started with the software and the analysis of EEG data…[Read more]

Thank you very much for the update!

In our newest release (https://github.com/softmatterlab/BRAPH-2-Matlab/releases/tag/2.0.0.a3) we added connectivity multiplex pipelines, where the input is an adjacency matrix for each layer per subject.
To understand the structure of the input data, you can check the following tutorial…[Read more]

Could you please let us know which release are you using?
We recommend using our latest one: http://braph.org/2022/11/15/braph-2-0-0-a2-released/

Sometimes parallel computing has problems while using Graphical user interfaces. You could try de-activating it.
Or you could deactivate in the GUI the Memorize Intermediate Results…[Read more]

Error in ComparisonEnsemble/calculateValue (line 1121)
[cp.diff, cp.p1, cp.p2, cp.ci_lower, cp.ci_upper] = calculate_results(cp);

Error in…[Read more]

Best,
Anna

35 L.CER Left-Cerebellum-Cortex 230 148 34 255

36 L.TH Left-Thalamus 0 118 14 255
36 L.TH Left-Thalamus-Proper 0 118 14 255
37 L.CA…[Read more]