ORIGINAL RESEARCH article
Front. Hum. Neurosci.
Sec. Speech and Language
This article is part of the Research TopicThe legacy of Dr. Paul Broca: Understanding language productionView all 3 articles
Lexical retrieval in fluent and nonfluent aphasia: A network analysis of verbal fluency data
Provisionally accepted
- 1Purdue University, West Lafayette, United States
- 2University at Buffalo, Buffalo, United States
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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 one minute, 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. Keywords: aphasia, lexical retrieval, verbal fluency, semantic network, graph theory, language
Keywords: Aphasia, lexical retrieval, verbal fluency, semantic network, graph theory, Language
Received: 22 Sep 2025; Accepted: 21 Nov 2025.
Copyright: © 2025 Pham, Castro and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Catherine Pham, ctpham@purdue.edu
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