brainGraph (version 3.1.0)

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. Requires a three-dimensional array of all subjects' connectivity matrices and a data.table of covariates, in addition to a contrast matrix or list. A null distribution of the largest connected component size is created by fitting a GLM to permuted data. For details, see GLM.

Usage

NBS(A, covars, contrasts, con.type = c("t", "f"), X = NULL,
  con.name = NULL, p.init = 0.001, perm.method = c("freedmanLane",
  "terBraak", "smith", "draperStoneman", "manly", "stillWhite"),
  part.method = c("beckmann", "guttman", "ridgway"), N = 1000,
  perms = NULL, symm.by = c("max", "min", "avg"),
  alternative = c("two.sided", "less", "greater"), long = FALSE, ...)
# S3 method for NBS
summary(object, contrast = NULL, digits = max(3L,
  getOption("digits") - 2L), ...)
# S3 method for NBS
nobs(object, ...)
# S3 method for NBS
terms(x, ...)
# S3 method for NBS
formula(x, ...)
# S3 method for NBS
labels(object, ...)
# S3 method for NBS
case.names(object, ...)
# S3 method for NBS
variable.names(object, ...)
# S3 method for NBS
df.residual(object, ...)
# S3 method for NBS
nregions(object)

Value

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

X

The design matrix

removed.subs

Character vector of subject ID's removed due to incomplete data (if any)

T.mat

3-d array of (symmetric) numeric matrices containing the statistics for each edge

p.mat

3-d array of (symmetric) numeric matrices containing the P-values

components

List containing data tables of the observed and permuted connected component sizes and P-values

rank,df.residual,qr,cov.unscaled

The rank, residual degrees of freedom, QR decomposition, and unscaled covariance matrix of the design matrix

Arguments

A

Three-dimensional array of all subjects' connectivity matrices

covars

A data.table of covariates

contrasts

Numeric matrix (for T statistics) or list of matrices (for F statistics) specifying the contrast(s) of interest; if only one contrast is desired, you can supply a vector (for T statistics)

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. Ignored if outcome != measure.

con.name

Character vector of the contrast name(s); if contrasts has row/list names, those will be used for reporting results

p.init

Numeric; the initial p-value threshold (default: 0.001)

perm.method

Character string indicating the permutation method. Default: 'freedmanLane'

part.method

Character string; the method of partitioning the design matrix into covariates of interest and nuisance. Default: 'beckmann'

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

...

Arguments passed to brainGraph_GLM_design

object, x

A NBS object

contrast

Integer specifying the contrast to plot/summarize; defaults to showing results for all contrasts

digits

Integer specifying the number of digits to display for P-values

Author

Christopher G. Watson,

Details

When printing a summary, you can include arguments to printCoefmat.

References

Zalesky, A. and Fornito, A. and Bullmore, E.T. (2010) Network-based statistic: identifying differences in brain networks. NeuroImage, 53(4), 1197--1207. tools:::Rd_expr_doi("10.1016/j.neuroimage.2010.06.041")

See Also

Other Group analysis functions: Bootstrapping, GLM, Mediation, brainGraph_permute, mtpc

Examples

if (FALSE) {
max.comp.nbs <- NBS(A.norm.sub[[1]], covars.dti, N=5e3)
}