# connectomestats¶

## Synopsis¶

Connectome group-wise statistics at the edge level using non-parametric permutation testing

## Usage¶

connectomestats [ options ]  input algorithm design contrast output

• input: a text file listing the file names of the input connectomes
• algorithm: the algorithm to use in network-based clustering/enhancement. Options are: nbs, tfnbs, none
• design: the design matrix. Note that a column of 1’s will need to be added for correlations.
• contrast: the contrast vector, specified as a single row of weights
• output: the filename prefix for all output.

## Description¶

For the TFNBS algorithm, default parameters for statistical enhancement have been set based on the work in: Vinokur, L.; Zalesky, A.; Raffelt, D.; Smith, R.E. & Connelly, A. A Novel Threshold-Free Network-Based Statistics Method: Demonstration using Simulated Pathology. OHBM, 2015, 4144; and: Vinokur, L.; Zalesky, A.; Raffelt, D.; Smith, R.E. & Connelly, A. A novel threshold-free network-based statistical method: Demonstration and parameter optimisation using in vivo simulated pathology. In Proc ISMRM, 2015, 2846. Note however that not only was the optimisation of these parameters not very precise, but the outcomes of statistical inference (for both this algorithm and the NBS method) can vary markedly for even small changes to enhancement parameters. Therefore the specificity of results obtained using either of these methods should be interpreted with caution.

## Options¶

### Options for permutation testing¶

• -notest don’t perform permutation testing and only output population statistics (effect size, stdev etc)
• -nperms num the number of permutations (Default: 5000)
• -permutations file manually define the permutations (relabelling). The input should be a text file defining a m x n matrix, where each relabelling is defined as a column vector of size m, and the number of columns, n, defines the number of permutations. Can be generated with the palm_quickperms function in PALM (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM). Overrides the nperms option.
• -nonstationary perform non-stationarity correction
• -nperms_nonstationary num the number of permutations used when precomputing the empirical statistic image for nonstationary correction (Default: 5000)
• -permutations_nonstationary file manually define the permutations (relabelling) for computing the emprical statistic image for nonstationary correction. The input should be a text file defining a m x n matrix, where each relabelling is defined as a column vector of size m, and the number of columns, n, defines the number of permutations. Can be generated with the palm_quickperms function in PALM (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM) Overrides the nperms_nonstationary option.

### Options for controlling TFCE behaviour¶

• -tfce_dh value the height increment used in the tfce integration (default: 0.1)
• -tfce_e value tfce extent exponent (default: 0.4)
• -tfce_h value tfce height exponent (default: 3)

• -threshold value the t-statistic value to use in threshold-based clustering algorithms

### Standard options¶

• -info display information messages.
• -quiet do not display information messages or progress status; alternatively, this can be achieved by setting the MRTRIX_QUIET environment variable to a non-empty string.
• -debug display debugging messages.
• -force force overwrite of output files (caution: using the same file as input and output might cause unexpected behaviour).
• -config key value (multiple uses permitted) temporarily set the value of an MRtrix config file entry.
• -help display this information page and exit.
• -version display version information and exit.

### References¶

• If using the NBS algorithm: Zalesky, A.; Fornito, A. & Bullmore, E. T. Network-based statistic: Identifying differences in brain networks. NeuroImage, 2010, 53, 1197-1207
• If using the TFNBS algorithm: Baggio, H.C.; Abos, A.; Segura, B.; Campabadal, A.; Garcia-Diaz, A.; Uribe, C.; Compta, Y.; Marti, M.J.; Valldeoriola, F.; Junque, C. Statistical inference in brain graphs using threshold-free network-based statistics.HBM, 2018, 39, 2289-2302
• If using the -nonstationary option: Salimi-Khorshidi, G.; Smith, S.M. & Nichols, T.E. Adjusting the effect of nonstationarity in cluster-based and TFCE inference. Neuroimage, 2011, 54(3), 2006-19

Author: Robert E. Smith (robert.smith@florey.edu.au)