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.

翻译：多变量皮质-肌肉分析近年来已成为评估皮质脊髓神经通路的一种有前景的方法。然而，当前的多变量方法面临高维度和有限样本量等挑战，从而限制了其进一步应用。本文提出一种结构化稀疏偏最小二乘相干性算法（ssPLSC），用于提取与皮质-肌肉相互作用相关的共享潜在空间表示。我们的方法通过整合基于偏最小二乘（PLS）的目标函数、稀疏性约束和基于连接性的结构化约束，构建了一个嵌入式优化框架，以解决泛化性、可解释性和空间结构问题。为求解该优化问题，我们在统一框架内开发了一种高效的交替迭代算法，并通过实验证明了其收敛性。基于一个合成数据集和多个真实数据集的广泛实验结果表明，ssPLSC相较于若干代表性多变量皮质-肌肉融合方法能够取得竞争性甚至更优的性能，尤其在样本量有限和噪声水平较高的场景中表现突出。本研究为皮质-肌肉分析提供了一种新颖的多变量融合方法，为神经系统疾病中皮质脊髓通路完整性的评估提供了变革性工具。