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Colloquium on Neuroimaging of Human Brain Function
sponding to the p largest eigenvalues, is transpose of Vp, and Xa is calculated from Eq. 10. Ap is a now smaller but full-rank matrix of eigenimages of Xa. The number p may be taken as the number of eigenvectors required to explain a predetermined proportion of the variance in the original data (e.g., >99%). ICA decomposition of the resulting eigenimages. Ap, gives,
where Ca is the p by n matrix of component maps, and WE is the computed unmixing matrix.
Substituting for Ap from Eq. 11 gives:
since because eigenvectors are mutually orthogonal. Finding the p time courses (of length n) associated with each of the p maps can now be determined by examining the columns of the matrix,
The Heart and Stroke Foundation of Ontario, the Howard Hughes Medical Institute, and the Office of Naval Research supported this research.
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