brainAnalysis: True Discovery Guarantee for Cluster Analysis of Brain...

In annavesely/sumSome: True Discovery Guarantee by Sum-Based Tests

True Discovery Guarantee for Cluster Analysis of Brain Imaging Data

Description

This function uses permutation t-statistics/p-values to determine a true discovery guarantee for fMRI cluster analysis. It computes confidence bounds for the number of true discoveries and the true discovery proportion within each cluster. The bounds are simultaneous over all sets, and remain valid under post-hoc selection.

Usage

brainAnalysis(sumBrain, clusters = NULL, nMax = 50, silent = FALSE)

Arguments

sumBrain	an object of class sumBrain, as returned by the functions brainScores and brainPvals.
clusters	3D numeric array of cluster indices, or character for a Nifti file name. If NULL, the whole brain is considered.
nMax	maximum number of iterations per cluster.
silent	logical, FALSE to print a summary of active clusters.

Value

brainAnalysis returns a list containing summary (data frame) and TDPmap (3D numeric array of the true discovery proportions). The data frame summary contains, for each cluster,

• size: size

• TD: lower (1-alpha)-confidence bound for the number of true discoveries

• maxTD: maximum value of TD that could be found under convergence of the algorithm

• TDP: lower (1-alpha)-confidence bound for the true discovery proportion

• maxTD: maximum value of TDP that could be found under convergence of the algorithm

• dim1, dim2, dim3: coordinates of the center of mass.

Author(s)

Anna Vesely.

References

Goeman J. J. and Solari A. (2011). Multiple testing for exploratory research. Statistical Science, doi: 10.1214/11-STS356.

Vesely A., Finos L., and Goeman J. J. (2023). Permutation-based true discovery guarantee by sum tests. Journal of the Royal Statistical Society, Series B (Statistical Methodology), doi: 10.1093/jrsssb/qkad019.

See Also

Permutation statistics for brain imaging: brainScores, brainPvals

Suprathreshold clusters: brainClusters

Examples

# simulate 20 copes with dimensions 10x10x10
set.seed(42)
copes <- list()
for(i in seq(20)){copes[[i]] <- array(rnorm(10^3, mean = -10, sd = 30), dim=c(10,10,10))}

# cluster map where t scores are grater than 2.8, in absolute value
thr <- 2.8
cl <- brainClusters(copes = copes, thr = thr)

# create object of class sumBrain
res <- brainScores(copes = copes, alpha = 0.2, seed = 42, truncFrom = thr)
res
summary(res)

# confidence bound for the number of true discoveries and the TDP within clusters
out <- brainAnalysis(res, clusters = cl$clusters)
out$summary

annavesely/sumSome documentation built on Jan. 28, 2025, 8:15 a.m.