The Connectome Analyzer provides to subnetwork analyses: the *SNWA Analysis* and the *NBS Analysis* by Zalesky

**Study directory**

Specify the directory where “Group1” and “Group2” are located

**Data separator**

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

**Mask data**

Select if you want to apply a mask to the data before running the analysis. The mask should be a file containing a matrix with the same dimensions as the ones for all subjects. The Connectome Analyzer will put to zero all the values from the connectivity matrices that have a zero value in the mask

**Regress data**

Select if you want to regress information from the computed network measures before performing the statistical analysis. Specify a file where the regressors are stored. Note that only csv files are supported. The file should contain one line per subject (Group1 + Group2), one column with the subject filename (only the file name, eg: ‘file_for_Subj1.csv’) and one extra column per regressor

**Subnetwork measures**

- You can choose to compute the subnetwork 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 subnetwork measures
- Select the set of measures you want to test:

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

**SNWA parameters**

- Decomposition method

- Automatic: decomposes the network subnetworks using the algorithm of your choice (walktrap, leading eigenvector or spinglass).
- File: provide a file containing the decomposition of the nodes into sub-regions. The file should have 2 columns and as much rows as nodes. The first column should contain the node label, ranging from 1 to the maximum number of nodes. The second column should contain the sub-region label to which the corresponding node belongs. Sub-regions labels should range from 1 to the number of sub-regions without skipping numbers
- Correction
- Select the method used for the correction for multiple testing
- Test type
- Define if the test should be parametric (Student t-test) or non-parametric (Wilcoxon test)
- Pairing
- Define if the data is paired or unpaired. If the data is paired, make sure that the paired files have the same alphanumerical rank in the two folders, otherwise R will load matrices that do not correspond.
- Alternative
- Define the H1 hypothesis (Group1 != Group2, Group1 > Group2 or Group1 < Group2)
- Alpha
- Threshold bellow which the null hypothesis Group1 == Group2 can be rejected in favor of H1 after correction for multiple testing
- Plot results
- Check to save plots of the subregions showing significantly different subnetwork measures

**Study directory**

Specify the directory where “Group1” and “Group2” are located

**Data separator**

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

**Mask data**

Select if you want to apply a mask to the data before running the analysis. The mask should be a file containing a matrix with the same dimensions as the ones for all subjects. The Connectome Analyzer will put to zero all the values from the connectivity matrices that have a zero value in the mask

**NBS parameters**

- Subnetwork forming threshold
- The subnetworks are computed as the connected components formed by the set of connections whose T value is above this threshold
- Permutations
- Number of permutations performed in order to infer the null distribution
- Tail
- Define the H1 hypothesis (Group1 != Group2, Group1 > Group2 or Group1 < Group2)