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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: [email protected]
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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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References

REFERENCES

Abwender, D.A., Swan, J.G., Bowerman, J.T., & Connolly, S.W. (2001). Qualitative analysis of verbal fluency output: Review and comparison of several scoring methods. Assessment, 8(3), 323–336.Google Scholar
Bertola, L., Lima, M.L.C., Romano-Silva, M.A., de Moraes, E.N., Diniz, B.S., & Malloy-Diniz, L.F. (2014). Impaired generation of new subcategories and switching in a semantic verbal fluency test in older adults with mild cognitive impairment. Frontiers in Aging Neuroscience, 6, 141.Google Scholar
Bird, S. (2006). NLTK: The natural language toolkit. Paper presented at the Proceedings of the COLING/ACL on Interactive presentation sessions.Google Scholar
Clark, D.G., McLaughlin, P.M., Woo, E., Hwang, K., Hurtz, S., Ramirez, L., DeRamus, T.P. (2016). Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment. Alzheimer’s & Dementia (Amsterdam Netherlands), 2, 113–122.Google Scholar
Demakis, G.J., Mercury, M.G., Sweet, J.J., Rezak, M., Eller, T., & Vergenz, S. (2003). Qualitative analysis of verbal fluency before and after unilateral pallidotomy. The Clinical Neuropsychologist, 17(3), 322–330.Google Scholar
Donovan, K., Siegert, R., McDowall, J., & Abernethy, D. (1999). Clustering and switching in verbal fluency in Parkinson’s disease. New Zealand Journal of Psychology, 28(1), 61.Google Scholar
Epker, M.O., Lacritz, L.H., & Munro Cullum, C. (1999). Comparative analysis of qualitative verbal fluency performance in normal elderly and demented populations. Journal of Clinical and Experimental Neuropsychology, 21(4), 425–434.Google Scholar
Friendly, M. (2013). Working with categorical data with R and the vcd and vcdExtra packages. Retrieved from https://cran.r-project.org/web/packages/vcdExtra/vignettes/vcd-tutorial.pdf Google Scholar
Galtier, I., Nieto, A., Lorenzo, J.N., & Barroso, J. (2017). Mild cognitive impairment in Parkinson’s disease: Clustering and switching analyses in verbal fluency test. Journal of the International Neuropsychological Society, 23, 511–520.Google Scholar
Haugrud, N., Crossley, M., & Vrbancic, M. (2011). Clustering and switching strategies during verbal fluency performance differentiate Alzheimer’s disease and healthy aging. Journal of the International Neuropsychological Society, 17(6), 1153–1157.Google Scholar
Henry, J.D., & Crawford, J.R. (2004a). A meta-analytic review of verbal fluency performance following focal cortical lesions. Neuropsychology, 18, 284–295.Google Scholar
Henry, J.D., & Crawford, J.R. (2004b). A meta-analytic review of verbal fluency performance in patients with traumatic brain injury. Neuropsychology, 18(4), 621.Google Scholar
Henry, J.D., Crawford, J.R., & Phillips, L.H. (2004). Verbal fluency performance in dementia of the Alzheimer’s type: a meta-analysis. Neuropsychologia, 42(9), 1212–1222.Google Scholar
Henry, J.D., & Crawford, J.R. (2004c). Verbal fluency deficits in Parkinson’s disease: A meta-analysis. Journal of the International Neuropsychological Society, 10(4), 608–622. doi:10.1017/s1355617704104141 Google Scholar
Ho, A.K., Sahakian, B.J., Robbins, T.W., Barker, R.A., Rosser, A.E., & Hodges, J.R. (2002). Verbal fluency in Huntington’s disease: A longitudinal analysis of phonemic and semantic clustering and switching. Neuropsychologia, 40(8), 1277–1284. doi:10.1016/s0028-3932(01)00217-2 Google Scholar
Hughes, A.J., Daniel, S.E., Kilford, L., & Lees, A.J. (1992). Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: A clinico-pathological study of 100 cases. Journal of Neurology, Neurosurgery, & Psychiatry, 55(3), 181–184.Google Scholar
Juhasz, B.J., Chambers, D., Shesler, L.W., Haber, A., & Kurtz, M.M. (2012). Evaluating lexical characteristics of verbal fluency output in schizophrenia. Psychiatry Research, 200(2-3), 177–183. doi:10.1016/j.psychres.2012.06.035 Google Scholar
Koerts, J., Meijer, H.A., Colman, K.S., Tucha, L., Lange, K.W., & Tucha, O. (2013). What is measured with verbal fluency tests in Parkinson’s disease patients at different stages of the disease? Journal of Neural Transmission, 120(3), 403–411.Google Scholar
Litvan, I., Goldman, J.G., Tröster, A.I., Schmand, B.A., Weintraub, D., Petersen, R.C., Williams‐Gray, C.H. (2012). Diagnostic criteria for mild cognitive impairment in Parkinson’s disease: Movement Disorder Society Task Force guidelines. Movement Disorders, 27(3), 349–356.Google Scholar
Mayr, U. (2002). On the dissociation between clustering and switching in verbal fluency: Comment on Troyer, Moscovitch, Winocur, Alexander and Stuss. Neuropsychologia, 40(5), 562–566.Google Scholar
Ober, B.A., Dronkers, N.F., Koss, E., Delis, D.C., & Friedland, R.P. (1986). Retrieval from semantic memory in Alzheimer-type dementia. Journal of Clinical and Experimental Neuropsychology, 8(1), 75–92.Google Scholar
Pakhomov, S.V., Eberly, L., & Knopman, D. (2016). Characterizing cognitive performance in a large longitudinal study of aging with computerized semantic indices of verbal fluency. Neuropsychologia, 89, 42–56.Google Scholar
Pakhomov, S.V., & Hemmy, L.S. (2014). A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the Nun Study. Cortex, 55, 97–106.Google Scholar
Pakhomov, S.V., Hemmy, L.S., & Lim, K.O. (2012). Automated semantic indices related to cognitive function and rate of cognitive decline. Neuropsychologia, 50(9), 2165–2175.Google Scholar
Pakhomov, S.V., Jones, D.T., & Knopman, D.S. (2015). Language networks associated with computerized semantic indices. NeuroImage, 104, 125–137.Google Scholar
Pedersen, T., Pakhomov, S.V., Patwardhan, S., & Chute, C.G. (2007). Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics, 40(3), 288–299.Google Scholar
Price, S.E., Kinsella, G.J., Ong, B., Storey, E., Mullaly, E., Phillips, M., Perre, D. (2012). Semantic verbal fluency strategies in amnestic mild cognitive impairment. Neuropsychology, 26(4), 490.Google Scholar
Ralph, M.A.L., Jefferies, E., Patterson, K., & Rogers, T.T. (2017). The neural and computational bases of semantic cognition. Nature Reviews. Neuroscience, 18(1), 42.Google Scholar
Raoux, N., Amieva, H., Le Goff, M., Auriacombe, S., Carcaillon, L., Letenneur, L., &Dartigues, J.-F. (2008). Clustering and switching processes in semantic verbal fluency in the course of Alzheimer’s disease subjects: Results from the PAQUID longitudinal study. Cortex, 44(9), 1188–1196.Google Scholar
Raskin, S.A., Sliwinski, M., & Borod, J.C. (1992). Clustering strategies on tasks of verbal fluency in Parkinson’s disease. Neuropsychologia, 30(1), 95–99.Google Scholar
Ray, N.J., & Strafella, A.P. (2012). The neurobiology and neural circuitry of cognitive changes in Parkinson’s disease revealed by functional neuroimaging. Movement Disorders, 27(12), 1484–1492.Google Scholar
Reverberi, C., Cherubini, P., Baldinelli, S., & Luzzi, S. (2014). Semantic fluency: Cognitive basis and diagnostic performance in focal dementias and Alzheimer’s disease. Cortex, 54, 150–164.Google Scholar
Reverberi, C., Laiacona, M., & Capitani, E. (2006). Qualitative features of semantic fluency performance in mesial and lateral frontal patients. Neuropsychologia, 44(3), 469–478. doi:10.1016/j.neuropsychologia.2005.05.011 Google Scholar
Rosen, V.M., Sunderland, T., Levy, J., Harwell, A., McGee, L., Hammond, C., Lefkowitz, C. (2005). Apolipoprotein E and category fluency: Evidence for reduced semantic access in healthy normal controls at risk for developing Alzheimer’s disease. Neuropsychologia, 43(4), 647–658. doi:10.1016/j.neuropsychologia.2004.06.022 Google Scholar
Ryan, J.O. (2013). A system for computerized analysis of verbal fluency tests. Available from https://users.soe.ucsc.edu/~jor/publications/ryanMastersThesis.pdf Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.Google Scholar
Steiger, J.H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87(2), 245.Google Scholar
Strauss, E., Sherman, E.M., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary. Washington, DC: American Chemical Society.Google Scholar
Taler, V., Johns, B.T., Young, K., Sheppard, C., & Jones, M.N. (2013). A computational analysis of semantic structure in bilingual verbal fluency performance. Journal of Memory and Language, 69(4), 607–618. doi:10.1016/j.jml.2013.08.004 Google Scholar
Tröster, A.I., Fields, J.A., Testa, J.A., Paul, R.H., Blanco, C.R., Hames, K.A., Beatty, W.W. (1998). Cortical and subcortical influences on clustering and switching in the performance of verbal fluency tasks. Neuropsychologia, 36(4), 295–304. doi:10.1016/s0028-3932(97)00153-x Google Scholar
Troyer, A.K. (2000). Normative data for clustering and switching on verbal fluency tasks. Journal of Clinical and Experimental Neuropsychology, 22(3), 370–378.Google Scholar
Troyer, A.K., Moscovitch, M., & Winocur, G. (1997). Clustering and switching as two components of verbal fluency: Evidence from younger and older healthy adults. Neuropsychology, 11(1), 138.Google Scholar
Troyer, A.K., Moscovitch, M., Winocur, G., Alexander, M.P., & Stuss, D. (1998). Clustering and switching on verbal fluency: The effects of focal frontal-and temporal-lobe lesions. Neuropsychologia, 36(6), 499–504.Google Scholar
Troyer, A.K., Moscovitch, M., Winocur, G., Leach, L., & Freedman, M. (1998). Clustering and switching on verbal fluency tests in Alzheimer’s and Parkinson’s disease. Journal of the International Neuropsychological Society, 4(2), 137–143.Google Scholar
Weakley, A., & Schmitter-Edgecombe, M. (2014). Analysis of verbal fluency ability in Alzheimer’s disease: The role of clustering, switching and semantic proximities. Archives of Clinical Neuropsychology, 29(3), 256–268.Google Scholar
Williams-Gray, C., Evans, J., Goris, A., Foltynie, T., Ban, M., Robbins, T., Sawcer, S. (2009). The distinct cognitive syndromes of Parkinson’s disease: 5 year follow-up of the CamPaIGN cohort. Brain, 132(Pt 11), 2958.Google Scholar
Williams-Gray, C., Foltynie, T., Brayne, C., Robbins, T., & Barker, R. (2007). Evolution of cognitive dysfunction in an incident Parkinson’s disease cohort. Brain, 130(7), 1787–1798.Google Scholar
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