00_MixfMRI-package: fMRI Clustering Analysis

In MixfMRI: Mixture fMRI Clustering Analysis

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

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.

Author(s)

Wei-Chen Chen and Ranjan Maitra.

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”, doi:10.1002/hbm.26425.

See Also

fclust(), set.global().

Examples

library(MixfMRI, quietly = TRUE)

.rem <- function(){

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

}

MixfMRI documentation built on Sept. 8, 2023, 5:06 p.m.