Abstract
PURPOSE: Language decline has been associated with healthy aging and with various neurodegenerative conditions, making it challenging to differentiate among these conditions. This study examined the utility of linguistic measures derived from a short narrative language sample for 1) identifying language characteristics and cut-off scores to differentiate between healthy aging, Primary Progressive Aphasia (PPA), Mild Cognitive Impairment (MCI), and Alzheimer's dementia (AD); and 2) differentiating among PPA variants in which language is the primary impairment. METHOD: Participants were 25 neurologically healthy English speakers, 20 individuals with MCI, 20 with AD, and 26 with PPA (non-fluent/agrammatic N = 10, logopenic N = 9, semantic N = 7). Narrative language samples of the Cookie Theft Picture of persons with healthy aging, MCI and AD were retrospectively obtained from the DementiaBank database (https://talkbank.org/DementiaBank/) and PPA samples were obtained from an ongoing research study. The language samples were analyzed for fluency, word retrieval success, grammatical accuracy, and errors using automated and manual analysis methods. The sensitivity and specificity of various language measures was computed. RESULTS: Participants with PPA scored lower than neurologically healthy and MCI groups on fluency (words per minute and disfluencies), word retrieval (Correct Information Units and number of errors), and sentence grammaticality. PPA and AD groups did not differ on language measures. Agrammatic PPA participants scored lower than logopenic and semantic PPA groups on several measures, while logopenic and semantic PPA did not differ on any measures. CONCLUSION: Measures derived from brief language samples and analyzed using mostly automated methods are clinically useful in differentiating PPA from healthy aging and MCI, and agrammatic PPA from other variants. The sensitivity and specificity of these measures is modest and can be improved when coupled with clinical presentation.