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Automated Indices of Clustering and Switching of Semantic Verbal Fluency in Parkinson’s Disease

Published online by Cambridge University Press:  04 October 2018

Delaram Farzanfar Open the ORCID record for Delaram Farzanfar [Opens in a new window] ,
Marta Statucka  and
Melanie Cohn
Delaram Farzanfar
Affiliation:
Toronto Western Hospital, University Health Network, Toronto, Canada
Marta Statucka
Affiliation:
Toronto Western Hospital, University Health Network, Toronto, Canada
Melanie Cohn*
Affiliation:
Toronto Western Hospital, University Health Network, Toronto, Canada Krembil Research Institute, University Health Network, Toronto, Canada University of Toronto, Department of Psychology, Toronto, Canada
*
Correspondence and reprint requests to: Melanie Cohn, Neuropsychology Clinic, 4F409, Toronto Western Hospital, UHN. E-mail: melanie.cohn@uhn.ca
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Abstract

Objectives: Deficits in semantic verbal fluency (SVF) can stem from dysfunction of an executive control system and/or of semantic knowledge. Previous analyses of SVF responses were devised to characterize these two components including switching and mean cluster size (MCS) indices, but these rely on subjective experimenter-based assessment of the words’ relatedness. To address this limitation, computational data-driven SVF indices have been developed. Our aim is to assess the validity and usefulness of these automated indices in the context of cognitive decline in Parkinson’s disease (PD). Methods: This is a retrospective study including 50 advanced PD patients with (n=28) or without (n=22) mild cognitive impairment (PD-MCI). We analyzed animal SVF outputs using an automated computational approach yielding switching, MCS, and cumulative relatedness (CuRel) indices. We compared these indices to the classic experimenter-based switching and MCS indices to assess concurrent validity, and against neuropsychological measures of executive functioning and semantic knowledge to assess construct validity. We also examined whether these indices were impaired and predicted PD-MCI. Results: Automated switching indices, but not MCS or CuRel, showed evidence of concurrent and construct validity, and characterized individual difference in advanced PD. Automated switching indices also outperformed the experimenter-dependent index in predicting the presence of PD-MCI. Conclusion: Computational methods hold promise as fine-grained, unbiased indices reflecting the executive component of SVF, but none of the methods provided valid measures of semantic knowledge in PD. Our data also confirm that SVF are not adequate tests of semantic memory in patients with executive dysfunction such as PD. (JINS, 2018, 24, 1047–1056)

Keywords

Verbal fluencyExecutive functionSemanticParkinson’s diseaseMild cognitive impairmentLatent semantic analysis
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 24 , Issue 10 , November 2018 , pp. 1047 - 1056
Copyright
Copyright © The International Neuropsychological Society 2018 

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