MyPLS 2.0 - Partial least squares analysis for multivariate brain-behavior associations

Unsupervised learning methods such as Partial Least Squares (PLS) can allow to overcome the limitations that arise with classification when classes are not well defined. PLS is a data-driven multivariate statistical technique that aims to extract relationships between two data matrices (McIntosh et al., 2004). PLS has previously been used to link neural variability with age (Garrett et al., 2010), or atrophy to symptoms in Parkinson's disease (Zeighami et al., 2019).
Here, we present a toolbox that deploys Behavior PLS, which aims to maximize the covariance between neuroimaging and behavioral data by deriving latent components (LCs) that are optimally weighted linear combinations of the original variables.