NBS: Network-based statistic for brain MRI data

In brainGraph: Graph Theory Analysis of Brain MRI Data

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 class 'NBS'
summary(object, contrast = NULL, digits = max(3L,
  getOption("digits") - 2L), ...)

## S3 method for class 'NBS'
nobs(object, ...)

## S3 method for class 'NBS'
terms(x, ...)

## S3 method for class 'NBS'
formula(x, ...)

## S3 method for class 'NBS'
labels(object, ...)

## S3 method for class 'NBS'
case.names(object, ...)

## S3 method for class 'NBS'
variable.names(object, ...)

## S3 method for class 'NBS'
df.residual(object, ...)

## S3 method for class 'NBS'
nregions(object)

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

Details

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

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

Note

It is assumed that the order of the subjects in covars matches that of the input array A. You will need to ensure that this is the case. Prior to v3.0.0, the covars table was sorted by Study.ID before creating the design matrix.

Author(s)

Christopher G. Watson, [e-mail]

References

Zalesky, A. and Fornito, A. and Bullmore, E.T. (2010) Network-based statistic: identifying differences in brain networks. NeuroImage, 53(4), 1197–1207. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.neuroimage.2010.06.041")}

See Also

Other Group analysis functions: Bootstrapping, GLM, Mediation, 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 June 22, 2024, 7:36 p.m.