Automated semantic indices related to cognitive function and rate of cognitive decline

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27 Scopus citations


The objective of our study is to introduce a fully automated, computational linguistic technique to quantify semantic relations between words generated on a standard semantic verbal fluency test and to determine its cognitive and clinical correlates. Cognitive differences between patients with Alzheimer's disease and mild cognitive impairment are evident in their performance on the semantic verbal fluency test. In addition to the semantic verbal fluency test score, several other performance characteristics sensitive to disease status and predictive of future cognitive decline have been defined in terms of words generated from semantically related categories (clustering) and shifting between categories (switching). However, the traditional assessment of clustering and switching has been performed manually in a qualitative fashion resulting in subjective scoring with limited reproducibility and scalability. Our approach uses word definitions and hierarchical relations between the words in WordNet®, a large electronic lexical database, to quantify the degree of semantic similarity and relatedness between words. We investigated the novel semantic fluency indices of mean cumulative similarity and relatedness between all pairs of words regardless of their order, and mean sequential similarity and relatedness between pairs of adjacent words in a sample of patients with clinically diagnosed probable (n=55) or possible (n=27) Alzheimer's disease or mild cognitive impairment (n=31). The semantic fluency indices differed significantly between the diagnostic groups, and were strongly associated with neuropsychological tests of executive function, as well as the rate of global cognitive decline. Our results suggest that word meanings and relations between words shared across individuals and computationally modeled via WordNet and large text corpora provide the necessary context to account for the variability in language-based behavior and relate it to cognitive dysfunction observed in mild cognitive impairment and Alzheimer's disease.

Original languageEnglish (US)
Pages (from-to)2165-2175
Number of pages11
Issue number9
StatePublished - Jul 2012

Bibliographical note

Funding Information:
The work on this study was supported in part by the National Institutes of Health National Library of Medicine Grant [LM00962301 to S.P.]. We also would like to thank Michael Kuskowski for invaluable input on cross-sectional and longitudinal analyses performed in this study.


  • Alzheimer's disease
  • Computational semantics
  • Mild cognitive impairment
  • Semantic relatedness
  • Semantic similarity
  • Semantic verbal fluency


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