help > RE: Using NBS to compare matrices between groups in cases where only 1 matrix exists per group
Feb 13, 2019  12:02 AM | Andrew Zalesky
RE: Using NBS to compare matrices between groups in cases where only 1 matrix exists per group
Hi Eric,

The current software implementing NBS does not include an option to compare structural covariance matrices.

However, the NBS as a method can definitely be used to compare structural covariance matrices and several previous papers have used this approach. You may need to write you own variation of the code or reach out to others that have used the NBS on structural covariance matrices. I can't think of any specific papers of the top of my head.

Andrew




Originally posted by Eric Plitman:
Hi,

I wanted to start by thanking everyone involved for the creation of NBS.

I was hoping to ask whether NBS can be utilized to compare structural covariance matrices between groups, in scenarios where only 1 matrix exists per group.

I have detailed my current approach below, which does lead to the cycling of Permutations up to 5000, for example; however, the "Max Size Random", "Max Size Actual", and "Lowest p-value" columns stay stagnant at 0.0, 0.0, and 1.0, respectively, despite any manipulation of the "Threshold" and "Significance" parameters.

Trying to compare two matrices (e.g. patients vs controls), I set:

Design Matrix to a path to a .txt file that contains:
0 1
1 0

Statistical Test to "t-test"
Contrast to [-1,1] or [1,-1]
Connectivity Matrices to a path to the first of two .txt files containing 20x20 covariance matrices (simply Pearson correlations)
Method to "Network-Based Statistic"
Component Size to "Extent"

Any potential advice that might be offered in this regard would be extremely appreciated.

Thank you very much for your time and assistance,
Eric

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TitleAuthorDate
Eric Plitman Feb 12, 2019
RE: Using NBS to compare matrices between groups in cases where only 1 matrix exists per group
Andrew Zalesky Feb 13, 2019
Klaas Bahnsen Dec 3, 2019
Andrew Zalesky Dec 3, 2019
Yi-Chia Kung Jul 15, 2021
Andrew Zalesky Jul 15, 2021