This topic contains 1 reply, has 2 voices, and was last updated by Mite Mijalkov 3 months ago.

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marco.castellaroDear Braph experts,
First of all, thank you for this beautiful software!! I really like it and it provides helpful functions in a easy and user friendly way.
We are using BRAPH to perform graph group analysis on a fMRI resting state dataset composed by AD patients.
Our subjects moves a lot in the scanner, hence we need to perform censoring of corrupted volumes. Therefore, our data has variable number of time point for each parcel, since we perform scrubbing, so different subject could have also different number of volumes.
We have noticed that if we feed to the software an excel file containing, for each parcel, a time series with a odd number of volumes, the software adds itself a dummy volume in the end of the timeseries for every parcel.
This obviously alter the calculation of the Pearson correlation coefficient, introducing a huge bias in the calculation of FCs. Our matrices have majority of values greater than 0.95.
We have solved eliminating the last volume in the time series. Afterwards the FCs values looks correct. I was wondering if you could correct the code.
All the Best
Marco
Dear Marco,
Thank you for your kind thoughts. I hope that using Braph will prove beneficial for you and will help you conducting your research.
From what I understood, you have the same brain atlas for all patients, however due to process you perform, not all brain regions have time series with equal length.
However, could you please further explain whether you observe one of the following:
1) Does the scan of an individual patient results in time series that vary from region to region?
2) Or alternatively, do all brain regions in a single patient have time series with the same length, and, instead the length of the time series is different only for different patients?I have done few preliminary trials, and I believe that the problem you are experiencing is not due to the odd number of time series points. Instead, Braph automatically fills the empty spaces in the shorter time series in order to make them of equal length and then execute the code for the calculation of the adjacency matrix.
I was wondering whether you would be able to send me a sample of your data, with only few patients that exemplify your problem. Then, I could consult with my colleagues and we will try to make the necessary adjustments to the code so that it performs the correct analysis for you.
I hope that this helps you. If you have any further questions, I will be glad to help.
Best,
Mite 
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