64. A Computational Analysis of Verbal Fluency in Schizophrenia

Abstract

Abstract Background: Verbal fluency tasks are widely used to assess language organization and executive functioning. Meta-analyses of verbal fluency performance in individuals with schizophrenia (SZ) indicate greater impairment on semantic (SF) than phonemic fluency (PF). Because SF depends more on efficient word retrieval within subcategories (clustering) and PF more on shifting between subcategories (switching), fluency impairment has been linked to clustering difficulties in SZ. However, most scoring methods rely on subjective judgments to determine clusters and switches, which reduces reliability. Here, we test a novel computational method to objectively capture clustering, switching, and overall retrieval organization from fluency performance in SZ and demographically matched healthy controls (HC). Methods: PF and SF (“animals”) tasks were administered to SZ and HC. Responses were transcribed from audio recordings into text files for computational analysis using VFClust (Ryan et al, 2013). VFClust utilizes latent semantic analysis (LSA) to quantify the semantic similarity of ordered word pairs as well as a combination of the Cargenie Mellon Phonemic Dictionary (CMUDict) with the Levenshtein distance method to determine phonemic similarity of ordered word pairs. Similarity thresholds were established and applied to these pairwise similarity scores (PSS) to determine cluster or chain inclusion. Clusters included words in which every word pair in that cluster had a PSS above threshold. Chains consisted of words in which PSS of adjacent words were above threshold. Switches were counted when PSS were below threshold, signifying a shift from one category to another. Output metrics for each individual included total words produced, mean size of chains and clusters, number of chains, clusters, and switches, and mean PSS across all ordered word pairs for SF and PF. Results: SZ were impaired compared to HC on SF but not PF. SF deficit was driven by a lower number of chains and clusters produced by SZ compared with HC. Group differences in SF were not driven by any other measures. Negative symptoms were inversely correlated with the total number of words produced and the switch count in SF. Conclusion: Objective, computationally derived fluency metrics captured group differences observed in SF in SZ. Interestingly, the lack of difference in cluster size questions SZ deficits in generating sizable clusters. Instead, limitations in the ability to retrieve a variety of semantically associated words (multiple clusters) seem to underlie performance deficits. Intact switching despite lower number of clusters and chains could indicate increased activation between (conventionally) unrelated words. While altered, semantic relatedness could cascade to thought disorder and abnormal interpersonal interactions, in this study, we only observed a significant negative association with negative symptoms.