Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered stroke researchers’ ability to achieve an integrated picture of the complex time-evolving interactions among symptoms. Here, we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes, characterized both by distinct clusters of symptoms, as well as differing degrees of symptom severity. We then validated our approach using graph neural networks to determine how well our model performed predictively for stratifying patients into these trajectory profiles at early vs. later time points post-stroke. We demonstrate that trajectory profile clustering is an effective method for identifying clinically relevant recovery subtypes in multidimensional longitudinal datasets, and for early prediction of symptom progression subtypes in individual patients. This paper is the first work introducing network trajectory approaches for stroke recovery phenotyping, and is aimed at enhancing the translation of such novel computational approaches for practical clinical application.

中文翻译：

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通过网络轨迹方法和图形神经网络进行中风恢复表型分析

中风是神经损伤的主要原因，其特征是多个神经领域的损伤，包括认知、语言、感觉和运动功能。这些领域的临床恢复使用范围广泛的措施进行跟踪，这些措施可能是连续的、有序的、间隔的或分类的，这可能对多元回归方法提出挑战。这阻碍了中风研究人员全面了解症状之间随时间演变的复杂相互作用的能力。在这里，我们使用来自网络科学和机器学习的工具，这些工具特别适合提取此类数据中的潜在模式，并且可能有助于预测恢复模式。为了证明这种方法的效用，我们使用轨迹曲线聚类 (TPC) 方法分析了来自 NINDS tPA 试验的数据，以在 5 个离散时间点识别 11 个不同神经域的不同中风恢复模式。我们的分析确定了 3 种不同的中风轨迹配置文件，这些配置文件与临床相关的中风综合征相一致，其特征在于不同的症状群以及不同程度的症状严重程度。然后，我们使用图神经网络验证了我们的方法，以确定我们的模型在中风后早期和晚期时间点将患者分层到这些轨迹配置文件中的预测性能如何。我们证明轨迹曲线聚类是一种在多维纵向数据集中识别临床相关恢复亚型的有效方法，以及早期预测个体患者的症状进展亚型。本文是第一项引入网络轨迹方法进行中风恢复表型分析的工作，旨在加强此类新型计算方法在实际临床应用中的转化。