MixfMRI (version 0.1-3)

Generalized Cluster-Based Analysis (CBA) Method: Generalized Cluster-Based Analysis (CBA) Method

Description

Find clusters in 2D or 3D based on a generalized CBA method. The CBA method is originally proposed by Heller, et.al. (2006) using the correlation of two time series as the similarity of two spatial locations.

Usage

cba.cor(da.ts, da.m = NULL, adj.dist = TRUE, fun.sim = stats::cor)
  cba.cor.2d(da.ts, da.m = NULL, adj.dist = TRUE, fun.sim = stats::cor)
  cba.cor.3d(da.ts, da.m = NULL, adj.dist = TRUE, fun.sim = stats::cor)

Value

Return the cluster ids for each voxel. NA for outside of brain if

da.m is provided.

Arguments

da.ts

a time series array of dimensions x * y * z * t.

da.m

a mask determining inside of brain or not.

adj.dist

if adjust correlations by distance.

fun.sim

a function computing simility of two locations.

Author

Wei-Chen Chen.

Details

These functions implement the 2D and 3D versions of CBA proposed by Heller, et.al. (2006).

da.ts should have dimensions x * y * z * t for 3D data and x * y * time for 2D data. Similarly, da.m would have x * y * z and x * y correspondingly.

da.m has values 0 or 1 indicating outside or inside a brain, respectively.

fun.sim(a, B) is a function return similarity between a location a and N neighboring locations B where a is of dimension t * 1 and B is of dimensiont * N. Ideally, fun.sim() should return values of similarity which take values between 0 and 1 where 0 means totally different and 1 means completely identical of two spatial locations. By default, stats::cor is used. See the example section next for user defined functions for fun.sim().

References

Heller, et.al. (2006) ``Cluster-based analysis of FMRI data'', NeuroImage, 33(2), 599-608.

Chen, W.-C. and Maitra, R. (2021) “A Practical Model-based Segmentation Approach for Accurate Activation Detection in Single-Subject functional Magnetic Resonance Imaging Studies”, arXiv:2102.03639.

See Also

fdr.bh.p1(), fdr.bh.p2().

Examples

### Simulated data
library(MixfMRI, quietly = TRUE)
dim <- c(4, 5, 4, 10)
set.seed(123)
da.ts <- array(rnorm(prod(dim)), dim = dim)
id.class <- suppressWarnings(cba.cor(da.ts))
table(id.class)

fun.tanh <- function(a, B){
  d <- 1 / apply(B, 2, function(b){ dist(rbind(as.vector(a), b)) })
  tanh(d)
}
id.class.tanh <- suppressWarnings(cba.cor(da.ts, fun.sim = fun.tanh))
table(id.class.tanh)

fun.logit <- function(a, B){
  d <- dist(t(cbind(a, B)))[1:ncol(B)]
  (1 / (1 + exp(-d))) * 2 - 1
}
id.class.logit <- suppressWarnings(cba.cor(da.ts, fun.sim = fun.logit))
table(id.class.logit)

# \donttest{
.rem <- function(){

  ### Real data
  # library(AnalyzeFMRI, quietly = TRUE)
  # library(oro.nifti, quietly = TRUE)

  # fn <- "pb02_volreg_tlrc.nii"
  # da <- readNIfTI(fn)
  # da.ts <- da@.Data

  # fn <- "mask_anat.nii"
  # da <- readNIfTI(fn)
  # da.m <- da@.Data

  # id.class <- suppressWarnings(cba.cor(da.ts, da.m))
  # dim(id.class) <- dim(da.m)
  # length(table(id.class))

}
# }