We reviewed the electronic medical records of 120 patients with relapsing-remitting multiple sclerosis and recorded signs and symptoms. Signs and symptoms were mapped to a neuro-ontology and then collapsed into 16 superclasses by subsumption. Bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using Gephi for both a normalized and unnormalized feature set. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps were used to visualize differences in features by community.

Network analysis of unipartite graphs yielded higher modularity scores (unnormalized 0.377, normalized 0.49) than the bipartite graphs (unnormalized 0.258, normalized 0.247). Across all graphs, we found large communities of subjects with predominantly sensory features and communities with predominantly motor features (weakness combined with hypertonia, hyperreflexia, or incoordination). When the feature dataset was normalized, network analysis showed motor and sensory communities and additional communities of subjects with a predominance of fatigue, pain, and cognitive features.