NBS: Network-based statistic for brain MRI data

In brainGraph: Graph Theory Analysis of Brain MRI Data

Description

Calculates the network-based statistic (NBS), which allows for family-wise error (FWE) control over network data, introduced for brain MRI data by Zalesky et al. Accepts a three-dimensional array of all subjects' connectivity matrices and a data.table of covariates, and creates a null distribution of the largest connected component size by permuting subjects across groups. The covariates data.table must have (at least) a Group column.

Usage

NBS(A, covars, con.vec, X = NULL, p.init = 0.001, N = 1000,
  symmetric = FALSE, alternative = c("two.sided", "less", "greater"), ...)

Arguments

A	Three-dimensional array of all subjects' connectivity matrices
covars	A data.table of covariates
con.vec	A numeric vector specifying the contrast of interest
X	A numeric matrix (optional), if you would like to supply your own design matrix (default: NULL)
p.init	Numeric; the initial p-value threshold (default: 0.001)
N	Integer; the number of permutations (default: 1e3)
symmetric	Logical indicating if input matrices are symmetric (default: FALSE)
alternative	Character string, whether to do a two- or one-sided test (default: two.sided)
...	Other arguments passed to brainGraph_GLM_design

Details

The graph that is returned by this function will have a t.stat edge attribute which is the t-statistic for that particular connection, along with a p edge attribute, which is the p-value for that connection. Additionally, each vertex will have a p.nbs attribute representing 1 - the p-value associated with that vertex's component.

Value

A list containing:

g.nbs	The igraph graph object based on the initial threshold
obs	Integer vector of the observed connected component sizes
perm	Integer vector of the permutation distribution of largest connected component sizes
p.perm	Numeric vector of the permutation p-values for each component
p.init	Numeric; the initial p-value threshold used

Author(s)

Christopher G. Watson, [e-mail]

References

Zalesky A., Fornito A., Bullmore E.T. (2010) Network-based statistic: identifying differences in brain networks. NeuroImage, 53(4):1197-1207.

See Also

brainGraph_GLM_design, brainGraph_GLM_fit

Examples

## Not run: 
max.comp.nbs <- NBS(A.norm.sub[[1]], covars.dti, N=5e3)

## End(Not run)

brainGraph documentation built on May 30, 2017, 1:34 a.m.

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