Characterizing cognitive performance in a large longitudinal study of aging with computerized semantic indices of verbal fluency

Abstract

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.

Keywords: Attention; Clustering; Dementia; Memory; Semantic relatedness; Semantic verbal fluency; Word frequency.

Figure 1

Study Design. Note: N_p – number of participants; N_s – number of samples.

Figure 2

Distribution of sorted relatedness values computed from CN-Train corpus and supplemented with FAN values. (Actual values have been log-transformed and shifted up by 30 for readability)

Figure 3

Results of calibrating VFClust semantic relatedness threshold. The left y-axis reflects cluster count and cluster sum correlation coefficients. The right y-axis reflects the magnitude of the difference between manual and automatic cluster counts.

Figure 4

Differences in means across study variables by diagnostic group in the cross-sectional ADRC cohort.

Figure 5

Illustration of differences in trajectories in study variables across diagnostic groups in the longitudinal MCSA cohort.