Structured and sparse partial least squares coherence for multivariate cortico-muscular analysis
This provides a novel multivariate fusion method for evaluating corticospinal pathway integrity in neurological disorders, though it appears incremental as it builds on existing partial least squares approaches.
The paper tackled the problem of high dimensionality and limited sample sizes in multivariate cortico-muscular analysis by proposing a structured and sparse partial least squares coherence algorithm (ssPLSC), which achieved competitive or better performance over existing methods, especially in scenarios with limited samples and high noise.
Multivariate cortico-muscular analysis has recently emerged as a promising approach for evaluating the corticospinal neural pathway. However, current multivariate approaches encounter challenges such as high dimensionality and limited sample sizes, thus restricting their further applications. In this paper, we propose a structured and sparse partial least squares coherence algorithm (ssPLSC) to extract shared latent space representations related to cortico-muscular interactions. Our approach leverages an embedded optimization framework by integrating a partial least squares (PLS)-based objective function, a sparsity constraint and a connectivity-based structured constraint, addressing the generalizability, interpretability and spatial structure. To solve the optimization problem, we develop an efficient alternating iterative algorithm within a unified framework and prove its convergence experimentally. Extensive experimental results from one synthetic and several real-world datasets have demonstrated that ssPLSC can achieve competitive or better performance over some representative multivariate cortico-muscular fusion methods, particularly in scenarios characterized by limited sample sizes and high noise levels. This study provides a novel multivariate fusion method for cortico-muscular analysis, offering a transformative tool for the evaluation of corticospinal pathway integrity in neurological disorders.