Comparing global and local semantic coherence of spontaneous speech in persons with Alzheimer's disease and healthy controls

There is growing evidence that corpus-based computational tools are useful in identifying changes in speech that appear to accompany cognitive decline in Alzheimer's disease (AD). It has long been known that semantic coherence of speech is altered in AD, but only recently have computational tools been developed that allow for cohesion indices to be computed in an automated fashion on larger data sets. To that end, this study examined semantic coherence in persons with AD and healthy controls. Speech transcripts from 81 individuals with probable AD (Mage = 72.7 years, SD = 8.80, 70.4% female) and 61 healthy controls (Mage = 63.9 years, SD = 8.52, 62.3% female) from DementiaBank were analyzed. Machine learning analyses of coherence were conducted, and models evaluated for classification accuracy (i.e., AD vs controls) as well as ROC-AUC. Relationships between coherence indices and MMSE performance were also quantified. Though no significant group differences emerged in local semantic coherence among adjacent words, persons with AD produced less globally coherent speech relative to healthy controls. Furthermore, global coherence indices predicted AD diagnoses with accuracy between 75% and 78% and were significantly associated with MMSE scores. These findings suggest that automated measures of global coherence can distinguish individuals with AD from healthy controls, which may point to eventual diagnostic utility in clinical settings.