流暢型および非流暢型失語症における語彙検索：言語流暢性データのネットワーク分析

Lexical retrieval is commonly impaired in many persons with aphasia (PWA). Verbal fluency tasks are often used to assess lexical retrieval ability. However, common methods of analyzing verbal fluency data (e.g., total number of appropriate responses, clustering and switching) fail to adequately capture the underlying organization of the mental lexicon. To better understand the nature of lexical-semantic organization in aphasia, this study applied a semantic network approach to verbal fluency data obtained from 120 healthy controls and 127 PWA (64 fluent and 63 nonfluent). Participants named as many animal category members as they could in 1 min, and their responses were converted into semantic networks. Global network metrics were computed for each group, including average shortest path length, clustering coefficient, and modularity. Compared to the healthy control network, the PWA network was less integrated and more fragmented, reflected by longer average shortest path lengths, reduced clustering, and higher modularity. These disruptions were especially evident in the nonfluent PWA network compared to the fluent PWA network. Complementary spreading activation and percolation analyses demonstrated that PWA networks were both less efficient and less resilient to disruption. Our results demonstrate that network-based analyses of verbal fluency provide a sensitive measure of lexical-semantic organization in aphasia, revealing structural disruptions that are not fully captured by traditional analyses. More broadly, this approach highlights how network science can advance theories of lexical-semantic organization and inform the development of individualized clinical assessments and treatment strategies.