f.icast.fmri {AnalyzeFMRI}R Documentation

Applies Spatial or Temporal ICA (Independent Component Analysis) to fMRI NIFTI datasets


Decomposes an fMRI dataset into a specified number of Spatially or Temporally Independent Components maps and associated time-courses using the FastICA algorithm





path and filename to fMRI dataset (NIFTI format .img or .nii file)


path and filename to fMRI maskfile (0 and 1 values to determine if you are inside or outside the brain) dataset (NIFTI format .img or .nii file)


Logical. Should we perform a spatial or temporal ICA.


Logical. Should we estimate the number of components to exatract. If FALSE, n.comp value (>0) should be provided


number of components to extract


Logical. Should we perform high-pass filtering on the data


TODO!!! The fMRI dataset is rearranged into a 2-dimensional data matrix X, where the column vectors are voxel time-series. A mask is used to specify which voxels are included. If this is not supplied by the user then a mask is constructed automatically using a 10% intensity threshold.

The data matrix is considered to be a linear combination of non-Gaussian (independent) components i.e. X = AS where rows of S contain the independent components and A is a linear mixing matrix. In short ICA attempts to 'un-mix' the data by estimating an un-mixing matrix U where UX = S.

Under this generative model the measured 'signals' in X will tend to be 'more Gaussian' than the source components (in S) due to the Central Limit Theorem. Thus, in order to extract the independent components/sources we search for an un-mixing matrix U that maximizes the non-gaussianity of the sources.

In FastICA, non-gaussianity is measured using approximations to negentropy (J) which are more robust than kurtosis based measures and fast to compute.

The approximation takes the form

J(y)=[E{G(y)}-E{G(v)}]^2 where v is a N(0,1) r.v

The following choices of G are included as options G(u)=\frac{1}{α} \log \cosh (α u) and G(u)=-\exp(\frac{-u^2}{2})

The FastICA algorithm is used to 'un-mix' the data and recover estimates of the mixing matrix A and the source matrix S. Rows of the source matrix S represent spatially independent components of the dataset (these are arranged spatially in the output). Columns of A contain the associated time-courses of the independent components.

Pre-processing involves removing the mean of each row of the data matrix and (optionally) standardizing the columns of the data matrix to have zero mean and unit variance.

All computations are done using C code. This avoids reading the entire dataset into R and thus saves memory space.


Nothing for the moment ... TODO!! The spatial and temporal components are written on disk


P Lafaye de Micheaux <plafaye@club.fr>


A. Hyvarinen and E. Oja (2000) Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430

Beckmann C. (2000) Independent Component Analysis for fMRI. First Year D.Phil Report, Dept. of Engineering Science, University of Oxford.

See Also


[Package AnalyzeFMRI version 1.1-23 Index]