Graph Theory Analysis of Semantic Fluency in Russian-English Bilinguals.

Abstract

Semantic category fluency is a widely used task involving language, memory, and executive function. Previous studies of bilingual semantic fluency have shown only small differences between languages. Graph theory analyzes complex relationships in networks, including node and edge number, clustering coefficient, average path length, average number of direct neighbors, and scale-free and small-world properties. To shed light on whether the underlying neural processes involved in semantic category fluency testing yield substantially different networks in different languages. We compared languages and methods using both network analysis and conventional analysis of word production. We administered the animal naming task to 51 Russian-English bilinguals in each language. We constructed network graphs using three methods: (a) simple association of unique co-occurring neighbors, (b) corrected associations between consecutive words occurring beyond chance, and (c) a network community approach using planar maximally filtered graphs. We compared the resultant network analytics as well as their scale-free and small-world properties. Participants produced more words in Russian than in English. Small-worldness metrics were variable between Russian and English but were consistent across the three graph theory analytical methods. The networks had similar graph theory properties in both languages. The optimal methodology for creating networks from semantic category fluency remains to be determined.