MixfMRI (version 0.1-1)

MixfMRI-package: fMRI Clustering Analysis

Description

Utilizing model-based clustering (unsupervised) for fMRI data especially in a distributed manner. The methods includes 2D and 3D clustering analyses and segmentation analyses for fMRI signals where p-values are significant levels of active voxels which respond to stimulate of interesting. The analyses are mainly identifying active voxels/signals from normal brain behaviors. Workflows are also implemented utilizing high performance techniques.

Details

Package:	MixfMRI
Type:	Package
License:	GPL (>= 2)
LazyLoad:	yes

The main function of this package is fclust() that implements model-based clustering algorithm for fMRI signal data and provides unsupervised clustering results for the data. Several workflows implemented with high-performance computing techniques are also built in for automatically process clustering, hypothesis, cluster merging, and visualizations.

References

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

fclust(), set.global().

Examples

# NOT RUN {
library(MixfMRI, quietly = TRUE)
# }
# NOT RUN {
.rem <- function(){

  demo(fclust3d,'MixfMRI',ask=FALSE,echo=FALSE)
  demo(fclust2d,'MixfMRI',ask=FALSE,echo=FALSE)

}
# }