Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as the labels might be unreliable. More exploratory approaches, such as Sparse Partial Least Squares (SPLS), may provide insights into the brain’s mechanisms by finding relationships between neuroimaging and clinical/demographic data. The identification of these relationships has the potential to improve the current understanding of disease mechanisms, refine clinical assessment tools, and stratify patients. SPLS finds multivariate associative effects in the data by computing pairs of sparse weight vectors, where each pair is used to remove its corresponding associative effect from the data by matrix deflation, before computing additional pairs.

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Jo~ao M Monteiro, Anil Rao, John Shawe-Taylor, Janaina Mour~ao-Miranda, Alzheimer’s Disease Initiative, others




Journal of Neuroscience Methods, (271) 182–194.


Blockchain, Complex issues, Complex networks, and Complex Systems