MixfMRI (version 0.1-4)

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.

Author

Wei-Chen Chen and Ranjan Maitra.

Details

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. (2023) “A practical model-based segmentation approach for improved activation detection in single-subject functional magnetic resonance imaging studies”, Human Brain Mapping, 44(16), 5309--5335. (doi:10.1002/hbm.26425)

See Also

fclust(), set.global().

Examples

library(MixfMRI, quietly = TRUE)
# \donttest{
.rem <- function(){

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

}
# }