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Subnetwork analysis configuration
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The Connectome Analyzer provides to subnetwork analyses: the :ref:`SNWA_label` and the :ref:`NBS_label` by `Zalesky <http://www.sciencedirect.com/science/article/pii/S1053811910008852>`_

.. _SNWA_label:

SNWA Analysis
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.. image:: snapshots/SNWA_analysis.jpg

**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

.. _NBS_label:

NBS Analysis
------------

.. image:: snapshots/NBS_analysis.jpg

**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)