Amyotrophic lateral sclerosis (ALS) is a neurological disease that starts at a focal point and gradually spreads to other parts of the nervous system. One of the main clinical symptoms of ALS is muscle weakness. To study spreading patterns of muscle weakness, we analyze spatiotemporal binary muscle strength data, which indicates whether observed muscle strengths are impaired or healthy. We propose a hidden Markov model‐based approach that assumes the observed disease status depends on two latent disease states. The model enables us to estimate the incidence rate of ALS disease and the probability of disease state transition. Specifically, the latter is modeled by a logistic autoregression in that the spatial network of susceptible muscles follows a Markov process. The proposed model is flexible to allow both historical muscle conditions and their spatial relationships to be included in the analysis. To estimate the model parameters, we provide an iterative algorithm to maximize sparse‐penalized likelihood with bias correction, and use the Viterbi algorithm to label hidden disease states. We apply the proposed approach to analyze the ALS patients' data from EMPOWER Study.

中文翻译：

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用于肌萎缩性侧索硬化病传播模式的空间网络二进制隐马尔可夫模型

肌萎缩性侧索硬化症（ALS）是一种神经系统疾病，始于焦点并逐渐蔓延至神经系统的其他部位。ALS的主要临床症状之一是肌肉无力。为了研究肌肉无力的蔓延模式，我们分析了时空二进制肌肉力量数据，该数据指示观察到的肌肉力量是否受损或健康。我们提出了一种基于隐马尔可夫模型的方法，该方法假定观察到的疾病状态取决于两个潜在的疾病状态。该模型使我们能够估计ALS疾病的发生率和疾病状态转变的可能性。具体而言，后者是通过逻辑自回归建模的，其中易感肌肉的空间网络遵循马尔可夫过程。所提出的模型非常灵活，可以将历史肌肉状况及其空间关系都包括在分析中。为了估计模型参数，我们提供了一种迭代算法，可通过偏差校正使稀疏惩罚的可能性最大化，并使用Viterbi算法标记隐藏的疾病状态。我们应用提出的方法来分析EMPOWER研究中的ALS患者数据。