NBS_lme: NBS

In FCtools: Functional Connectivity Tools

NBS

Description

Network-based statistics analysis

Usage

NBS_lme(
  model,
  contrast,
  random,
  FC_data,
  nperm = 100,
  nthread = 1,
  p = 0.001,
  perm_type = "row"
)

Arguments

model	A data.frame or matrix containing all the predictors in the model
contrast	The predictor of interest. The edge- and network-wise statistics will only be estimated for this predictor
random	A N x 1 numeric vector or object containing the values of the random variable (optional). Its length should be equal to the number of subjects in model (it should NOT be inside the model data.frame).
FC_data	An N x E matrix containing the vectorized edges; where N = number of subjects, E=number of edges
nperm	The number of permutations to generate the null distribution of network strengths. Set to 100 by default
nthread	The number of CPU threads to use. Set to 1 by default
p	the edge-wise threshold. Set to 0.001 by default
perm_type	A string object specifying whether to permute the rows ("row"), between subjects ("between"), within subjects ("within") or between and within subjects ("within_between") for random subject effects. Default is "row".

Details

This function implements the NBS analysis described in Zalesky et al. (2010) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.neuroimage.2010.06.041")}

Value

A list object containing

• results Edge- and network-wise results in a data.frame object

• t.orig Edge-wise t-stats

• tcrit The critical t-value

• max.netstr A vector containing the null distribution of the permuted network strengths

Examples

demomat=get('demomat')[,1:4005] 
contrast=c(1,1,2,2)
random=c('sub1','sub2','sub3','sub4')
model1=NBS_lme(model=contrast, 
               contrast=contrast, 
               random=random, 
               FC_data=demomat, 
               nperm=1,
               nthread=1, 
               p=0.001)

FCtools documentation built on Sept. 15, 2026, 5:09 p.m.