The analysis of semantic networks in multiple sclerosis identifies preferential damage of long-range connectivity

Abstract

ObjectiveTo analyze the characteristics of semantic networks derived from fluency tests in patients with multiple sclerosis (MS). MethodsWe built semantic networks by applying co-occurrence statistics to the data from verbal fluency tests performed on patients with MS (n=36) and matched controls (n=200), assessing the differences in network topology. ResultsAs expected, the semantic networks from both patients and controls showed 'small-world' properties. Topological analysis of these semantic networks indicated that there were fewer nodes (words) and links (defined by significant co-occurrence of words) in those derived from MS patients. The average connectivity was not significantly affected, while the local connectivity (clustering coefficient) is preserved. Quantifiers of the cohesiveness of the network, which reflect long distance connectivity, such as assortativity and maximum centrality coefficients, differed significantly between MS patients and controls. ConclusionsThe analysis of semantic networks reveals quantitative differences in MS patients and identifies preferential damage of long-range connectivity. The analysis of semantic networks may be useful in clinical practice for the assessment of cognitive impairment or recovery after damage.