Abstract
Partial least squares (PLS) analysis has been used to characterize
distributed signals measured by neuroimaging methods like positron
emission tomography (PET), functional magnetic resonance imaging
(fMRI), event-related potentials (ERP) and magnetoencephalography
(MEG). In the application to PET, it has been used to extract activity
patterns differentiating cognitive tasks, patterns relating distributed
activity to behavior, and to describe large-scale interregional interactions
or functional connections. This paper reviews the more recent extension
of PLS to the analysis of spatiotemporal patterns present in fMRI,
ERP, and MEG data. We present a basic mathematical description of
PLS and discuss the statistical assessment using permutation testing
and bootstrap resampling. These two resampling methods provide complementary
information of the statistical strength of the extracted activity
patterns (permutation test) and the reliability of regional contributions
to the patterns (bootstrap resampling). Simulated ERP data are used
to guide the basic interpretation of spatiotemporal PLS results,
and examples from empirical ERP and fMRI data sets are used for further
illustration. We conclude with a discussion of some caveats in the
use of PLS, including nonlinearities, nonorthogonality, and interpretation
difficulties. We further discuss its role as an important tool in
a pluralistic analytic approach to neuroimaging.
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