Partial Least Squares (PLS), was first introduced to the neuroimaging community in 1996 (McIntosh et al., 1996), for measuring distributed task responses (Mean-Centering PLS and Non-Rotated Task PLS). It has also been applied to measuring distributed patterns that impact on task performance (Regular Behav PLS, Non-Rotated Behav PLS and Multiblock PLS) and finally to both task-dependent and resting state regional connectivity (McIntosh and Lobaugh, 2004).

The NPAIRS (Nonparametric, Prediction, Activation, Influence, Reproducibility, re-Sampling) package was first introduced with canonical variates analysis (i.e., linear discriminant analysis) and a reproducibility metric (Strother et al., 1997) followed by the addition of prediction metrics (Strother et al., 2002). NPAIRS uses a penalized PCA basis (PCA denoising) adapted to optimize the reproducibility and prediction metrics for CVA. In addition to measuring distributed task and resting state responses NPAIRS provides a statistical resampling framework with basic building blocks for benchmarking and comparing preprocessing and data analysis, (i.e., processing pipeline) choices (Strother et al., 2004).

Both PLS and NPAIRS/CVA have proven to be robust methods for extracting distributed signal changes related to changing task demands in neuroimaging. Their relative strengths and weaknesses are currently being evaluated at the Rotman Research Institute.

_{Note that the code is currently in beta development (version 1.1.6).}

• See what changes have been made in the latest version.
• Download the latest version of PLSNPAIRS (beta 1.1.6) and run it from the command line using Java 1.6;

• Run PLSNPAIRS via command line! See User Guide Batch PLSNPAIRS.

• See How To Run PLSNPAIRS for details about how to run PLSNPAIRS.

• PLSNPAIRS currently has the same restriction on input data orientation as PLS Matlab, i.e. input data must be RAS in order to use mm brain location information shown in Results Viewer or saved in extracted Nifti result image volumes.