NBS: Network-based statistic for brain MRI data

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/NBS.R

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.

Print a summary of NBS analysis

Usage

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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,
  ...)

## S3 method for class 'NBS'
summary(object, contrast = NULL, digits = max(3L,
  getOption("digits") - 2L), ...)

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

object

A NBS object

contrast

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

digits

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

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

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

## End(Not run)

brainGraph documentation built on May 29, 2018, 9:03 a.m.