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.mat, con.type = c("t", "f"), X = NULL, con.name = NULL,
  p.init = 0.001, N = 1000, perms = NULL, symm.by = c("max", "min",
  "avg"), alternative = c("two.sided", "less", "greater"), long = FALSE,
  ...)

Arguments

A	Three-dimensional array of all subjects' connectivity matrices
covars	A data.table of covariates
con.mat	Numeric matrix specifying the contrast(s) of interest; if only one contrast is desired, you can supply a vector
con.type	Character string; either 't' or 'f' (for t or F-statistics). Default: 't'
X	Numeric matrix, if you wish to supply your own design matrix (default: NULL)
con.name	Character vector of the contrast name(s); if con.mat has row names, those will be used for reporting results (default: NULL)
p.init	Numeric; the initial p-value threshold (default: 0.001)
N	Integer; number of permutations to create (default: 5e3)
perms	Matrix of permutations, if you would like to provide your own (default: NULL)
symm.by	Character string; how to create symmetric off-diagonal elements (default: max)
alternative	Character string, whether to do a two- or one-sided test (default: 'two.sided')
long	Logical indicating whether or not to return all permutation results (default: FALSE)
...	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

An object of class NBS with some input arguments in addition to:

X	The design matrix
removed	Character vector of subject ID's removed due to incomplete data (if any)
T.mat	List of numeric matrices (symmetric) containing the statistics for each edge
p.mat	List of numeric matrices (symmetric) containing the P-values
components	List containing data tables of the observed and permuted connected component sizes and P-values

Author(s)

Christopher G. Watson, [email protected]

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_t

Other Group analysis functions: IndividualContributions, brainGraph_GLM, brainGraph_boot, brainGraph_mediate, brainGraph_permute, mtpc

Examples

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

## End(Not run)

brainGraph documentation built on Feb. 24, 2018, 1:01 a.m.