fmri.sgroupICA: Spatial group ICA for fmri

In WIAS-BERLIN/fmri: Analysis of fMRI Experiments

Spatial group ICA for fmri

Description

Combine ICA results from multiple runs or multiple subjects in group fMRI studies

Usage

fmri.sgroupICA(icaobjlist, thresh = 0.75, minsize=2)

Arguments

icaobjlist	List of results obtained by function fmri.sICA for a series of fmri data sets (multiple runs or multiple subjects).
thresh	threshold for cluster aggregation. Needs to be in (0,1).
minsize	Minimal size of cluster to consider in IC aggregation. Needs to be larger than 1.

Details

The fMRI time series need to be preprocessed and registered before thr ICA decomposition is performed.

The function employs a hierarchical clustering algorithm (complete linkage) on the combined set of spatial independent components obtained from the individual time series. A distance matrix is obtained from correlations of the independent component images. Aggregation of two components from the same fmri series is prevented in the algorithm.

Value

An object of class ”fmrigroupICA” with components

icacomp	Mean IC's over cluster members for cluster of size larger or equal minsize
size	Size of selected clusters
cl	Number of selected clusters
cluster	Cluster membership corresponding to thresh.
height	Distance value at which the cluster was created. Elements correspond to elements of cluster.
hdm	Object returned by function hclust.

Author(s)

Joerg Polzehl [e-mail]

References

F. Esposito et al (2005) Independent component analysis of fMRI group studies by self-organizing clustering, Neuroimage, pp. 193-205.

See Also

fmri.sICA, plot.fmrigroupICA, hclust

WIAS-BERLIN/fmri documentation built on Sept. 18, 2023, 4:26 a.m.