Abstract
Abstract
Objective:
Cognitive impairment is associated with language changes that emerge during verbal responses to neuropsychological assessments that traditional scoring does not capture. The current study investigated the utility of a linguistic analysis of paragraph recall responses for detecting cognitive impairment.
Methods:
Digital voice recordings of logical memory (LM) were available from 598 Long Life Family Study participants with normal cognition and 112 with cognitive impairment. Linguistic polyfeature scores for immediate (PFS-IR) and delayed recall (PFS-DR) were created from weighted sums of features associated with cognitive impairment. Logistic regression models assessed the predictive value of each PFS for classifying cognitive impairment. Repeated measures models with generalized estimating equations assessed whether PFSs predict cognitive screener performance over time.
Results:
Twelve linguistic features and higher PFSs were associated with cognitive impairment (PFS-IR OR = 1.05; PFS-DR OR = 1.07). PFS-DR scores approximated the accuracy of traditional LM scoring (AUC-PR = 0.77 vs. 0.81, respectively). A higher PFS-DR was also associated with lower cognitive screener scores over an average of 7 years of follow-up (
β
= –0.08, 95% CI [–0.11, –0.06]).
Conclusion:
Quantification of linguistic features from paragraph recall identified distinct linguistic markers that reflect differences in learning, retention, and self-monitoring. Summary feature scores detected cognitive impairment and predicted lower cognitive performance over time. This approach has potential implications for automated testing, recording, and scoring pipelines allowing for implementation of gold-standard neuropsychological assessments in broader clinical and research settings. Further studies are needed to validate these findings across clinical and community-based populations.