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Non redundant connectivity (NRC) maps

Non-Redundant Connectivity (NRC) maps are summary maps of functional connectivity levels in the brain, estimated from functional Magnetic Resonance (fMRI) series. They are similar to weighted Global Brain Connectivity (GBC) maps (Cole et al. 2010), which are based on averages of correlations with each voxel. However, NRC is based on estimates of multiple correlations instead of bivariate correlations. To avoid dimensionality problems it applies the method of Supervised Principal Components (Bair et al. 2006).

A full description of the method is available in:

Non redundant functional brain connectivity in schizophrenia.
R. Salvador, R. Landín-Romero, M. Anguera, E. J. Canales-Rodríguez, J. Radua, A. Guerrero-Pedraza, S. Sarró, T. Maristany, P.J. McKenna, E. Pomarol-Clotet.
Brain Imaging Behav. 2016 Mar 21. [Epub ahead of print].

Downloading the nrcon program

To run a NRC analysis you can use nrcon, a 64 bit linux executable that can be downloaded from the Download button located at the bottom of this page. The program is contained in the file which also contains other relevant files necessary for its execution. Once has been downloaded, it should be decompressed in a user chosen directory.

Running nrcon

Before running nrcon a linux computer terminal should be opened on the user directory which contains the executable. Apart from the NRC image, the weighted GBC and the Degree Centrality (DC) images will be also calculated by nrcon. Specifically, the nrcon call should be followed by a single command line argument, which is the name of a plain text file containing the following information:

  1. Name of preprocessed and normalized 4D fMRI dataset. It should be a real image (of floats) in compressed NIFTI format (.nii.gz extension).
  2. Name of an integer Mask image (.nii.gz extension) with non zero values for the voxels to be considered in the analysis. It should have the same geometry as the 4D fMRI dataset.
  3. Correlation value or quantile value to threshold matrix of correlations (only for NRC and DC).
  4. Binary variable to indicate if the previous value is a correlation (0) or a quantile (1).
  5. Binary variable to indicate if we should consider the absolute values of correlations (only for GBC and DC). 0 = NO, 1 = YES.
  6. Amount of random subsampling to speed up NRC calculations (not applied in GBC or DC). 1 = no subsample, 2 = take (the equivalent of ) one every two voxels (in all three directions), 3 = take one in three, ...
  7. Fraction (value between 0 and 1) of variance to be kept from the components in the NRC analysis.
  8. Prefix to be added to the output images.

An example of the content of the input file could be:


Output: It generates six files prefix_nrc.nii.gz (NRC map), prefix_gbc.nii.gz (GBC map), prefix_neigen.nii.gz (map of number of components used), prefix_std.nii.gz (map of voxel amplitudes), prefix_degree.nii.gz (DC map) and a log.txt file.

Some tips

For further help you can email me (Raymond Salvador): rsalvador at fidmag dot com


Cole MW, Pathak S, Schneider W (2010): Identifying the brain's most globally connected regions. NeuroImage. 49:3132-3148.

Bair E, Hastie T, Debashis P, Tibshirani R (2006): Prediction by Supervised Principal Components. Journal of the American Statistical Association. 101:119-137.