brainGraph (version 0.72.0)

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, 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: A list containing:

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.

Examples

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