Partial least squares analysis of neuroimaging data: applications and advances
Section snippets
Partial least squares
The term “partial least squares” refers to the computation of the optimal least-squares fit to part of a correlation or covariance matrix (Wold, 1982). The part is the “cross-block” correlation between some set of exogenous and dependent measures. PLS is similar to principal components analysis (PCA), but one important feature of PLS is that the solutions are constrained to the part of the covariance structure that is attributable to experimental manipulations or that relates to behavior.
Computation
Computational overhead may be a concern when conducting PLS analysis, especially in the case of fMRI or ERP/MEG data when high density spatial or temporal sampling is used. For an average-sized event-related fMRI study, an analysis including 500 permutations and 100 bootstraps takes 30 to 60 min when running on a Linux-based Intel workstation with 4GB of RAM, depending on the workstation load. In systems with parallel architectures, it is possible to distribute the resampling operations across
Conclusions
Starting from the basic emphasis on explaining the relation between two or more blocks of data, PLS can address questions ranging from identifying task-specific patterns of activity to extracting neural activity patterns that predict behavior. The flexibility of PLS, especially when used in concert with other analytic methods, enables more thorough testing of specific hypotheses and development of neurocognitive theory.
As noted in the introduction, PLS is part of a family of multivariate data
Acknowledgments
This work was supported by Canadian Institutes for Health Research grants to ARM and NJL, and a JS McDonnell Foundation grant to ARM.
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