The Connectome Analyzer also provides extra features for *P-value matrix relaxation* and compute *Single subject measures*

**Matrix file**

Select the file containing the matrix of p-values to relax. The file can be in csv format or matlab ”.mat” format. In the case of a ”.mat” file, make sure that the variable containing the p-values is named “matrix”. The p-values can come from any test (Student t-test, Wilcoxon test, multivariate Hotelling test, etc...), and should be ordered as a single column in the file

**Data separator**

If the p-value file is in csv format, select the character that separates columns in the file. For matlab ”.mat” files, this setting is ignored

**Decomposition method**

- Automatic: This feature should actually not be used for single p-values relaxation. Set the decomposition manually using the file option.
- File: provide a file containing the decomposition of the p-values. The file should have 2 columns and as much rows as p-values. The first column should contain the p-value label, ranging from 1 to the maximum number of p-values. The second column should contain the sub-region to which the corresponding p-value belongs. Sub-regions labels can range from 1 to the number of sub-regions, without skipping numbers

**Correction**

Select the method used for the correction for multiple testing

**Alpha1**

Threshold to detect significantly different sub-networks

**Alpha2**

Threshold bellow which the null hypothesis Group1 == Group2 can be rejected in favor of the H1 used to compute the original p-values

**Strong**

Factor by which the p-values of non-significatively different sub-networks should be multiplied in the RMIO procedure

**Connectivity matrix**

Select the file containing the connectivity matrix. The file can be in csv format or matlab ”.mat” format. In the case of a ”.mat” file, make sure that the matrix variable is named “matrix”.

**Data separator**

If the connectivity matrix file is in csv format, select the character that separates columns in the file. For matlab ”.mat” files, this setting is ignored

**Network measures**

- You can choose to compute the network measures on:

- Non-Weighted graphs: the edge values represent the number of connections between the vertices
- Weighted graphs: the edge values represent some measure of the connection strength between the vertices
- Binarized graphs: the edges that have non-zero values are set to one before computing the network measures
- Select the set of measures you want to test:

- Degree: computes the mean degree of the network. If the matrix is weighted, the mean strength is computed
- Betweenness: computes the network mean node betweenness
- Closeness: computes the network mean closeness
- Diameter: computes the network mean diameter
- Efficiency: compute the network mean efficiency