A computational approach for estimating several indices of performance on the animal category verbal fluency task was validated, and examined in a large longitudinal study of aging. The performance indices included the traditional verbal fluency score, size of semantic clusters, density of repeated words, as well as measures of semantic and lexical diversity. Change over time in these measures was modeled using mixed effects regression in several groups of participants, including those that remained cognitively normal throughout the study (CN) and those that were diagnosed with mild cognitive impairment (MCI) or Alzheimer's disease (AD) dementia at some point subsequent to the baseline visit. The results of the study show that, with the exception of mean cluster size, the indices showed significantly greater declines in the MCI and AD dementia groups as compared to CN participants. Examination of associations between the indices and cognitive domains of memory, attention and visuospatial functioning showed that the traditional verbal fluency scores were associated with declines in all three domains, whereas semantic and lexical diversity measures were associated with declines only in the visuospatial domain. Baseline repetition density was associated with declines in memory and visuospatial domains. Examination of lexical and semantic diversity measures in subgroups with high vs. low attention scores (but normal functioning in other domains) showed that the performance of individuals with low attention was influenced more by word frequency rather than strength of semantic relatedness between words. These findings suggest that various automatically semantic indices may be used to examine various aspects of cognitive performance affected by dementia.
|Original language||English (US)|
|Number of pages||15|
|State||Published - Aug 1 2016|
Bibliographical noteFunding Information:
This work was supported in part by grants from the Alzheimer's Association ( DNCFI-12-242985 ) and the National Institutes of Health ( P50 AG16574 and U01 AG06786 ). We also would like to thank Jennifer Strommen, Benjamin Eischens, Mara Anderson, the University of Minnesota students that worked on digitizing thousands of hand-written SVF tests, and James Ryan and Thomas Christie that worked on programming the VFClust package. We also would like to thank Dana Swenson-Dravis and Dorla Burton for extracting and collating psychometric information at the Mayo Clinic for the ADRC and MCSA study participants. Last but not least, we extend special thanks to the study participants, their families, and caregivers.
© 2016 Elsevier Ltd.
- Semantic relatedness
- Semantic verbal fluency
- Word frequency