brainGraph (version 0.62.0)

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. If you would like to perform a t-test at each element, then supply a covariates data.table with only a Study.ID and Group column. The graph that is returned by this function will have a t.stat edge attribute which is the t-statistic for that particular connection.

Usage

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

Arguments

A

Three-dimensional array of all subjects' connectivity matrices

covars

A data.table of covariates

alternative

Character string, whether to do a two- or one-sided test (default: 'two.sided')

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)

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

References

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

Examples

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