Howlett-Prieto Quentin, Oommen Chelsea, Carrithers Michael D, Wunsch Donald C, Hier Daniel B
Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States.
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, United States.
Front Digit Health. 2023 Jan 11;4:1063264. doi: 10.3389/fdgth.2022.1063264. eCollection 2022.
We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis.
我们使用网络分析，根据复发缓解型多发性硬化症患者的累积体征和症状来识别其亚型。回顾了113例复发缓解型多发性硬化症患者的电子病历，将体征和症状映射到神经本体论中的类别，并通过归类将这些类别合并为16个超类。在对数据进行归一化和向量化之后，使用NetworkX创建了二分（受试者-特征）和单分（受试者-受试者）网络图，并在Gephi中进行可视化。计算每个节点的度和加权度。使用模块度得分将图划分为社区。特征图可视化了不同社区特征的差异。单分图的网络分析产生的模块度得分（0.49）高于二分图（0.25）。根据特征，二分网络被划分为五个社区，分别命名为疲劳、行为、张力亢进/虚弱、异常步态/括约肌和感觉。单分网络根据特征被划分为五个社区，分别命名为疲劳、疼痛、认知、感觉和步态/虚弱/张力亢进。虽然在这个多发性硬化症患者队列中我们没有识别出纯亚型（例如，纯运动型、纯感觉型等），但我们证明了网络分析可以将这些患者划分为不同的亚型社区。需要更大的数据集和额外的划分算法来证实这些发现并阐明其意义。本研究通过将归类特征约简与网络分析相结合，为多发性硬化症亚型的文献研究做出了贡献。
