brainGraph (version 1.0.0)

NBS: Network-based statistic for 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

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

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.

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)
## ---------------------------------------------