Cognitive Phenotyping of Parkinson S Disease Patients Via Digital Analysis of Spoken Word Properties - Chana-Cuevas, P; Garcia-Serrano, A (John Wiley and Sons Inc, 2025)
Background: Cognitive symptoms are highly prevalent in Parkinson s disease (PD), often manifesting as mild cognitive impairment (MCI). Yet, their detection and characterization remain suboptimal because standard approaches rely on subjective impressions derived from lengthy, univariate tests. Objective: We examined whether digital analysis of verbal fluency predicts cognitive status in PD. Methods: We asked 464 Spanish speakers with PD to complete taxonomic (animal), thematic (supermarket), and phonemic (/p/) fluency tasks. We quantified six response properties: semantic variability, granularity, concreteness, length, frequency, and phonological neighborhood. In Study 1, these properties were fed to a ridge regressor to predict Mattis Dementia Rating Scale (MDRS) scores and subscores. In Study 2, we used the same properties to compare (via a generalized linear model) and classify (via random forest) between 123 patients with and 124 without MCI. Results: In Study 1, predicted MDRS scores and subscores strongly correlated with actual ones, adjusting for clinical and cognitive variables (R = 0.51, P < 0.001). In Study 2, MCI patients words were less semantically variable, less concrete, and shorter, adjusting for clinical and cognitive variables (P-values < 0.05). Machine learning discrimination between patients with and without MCI was robust in the validation set (area under the curve [AUC] = 0.76), with good generalization to unseen pre-surgical (AUC = 0.68) and post-surgical (AUC = 0.72) samples, surpassing MDRS scores (AUC = 0.54). Results were consistently driven by semantic variability, granularity, and concreteness. Conclusions: Digital word property analysis predicts cognitive symptom severity and distinguishes between cognitive phenotypes of PD, enabling scalable neuropsychological screenings. © 2025 International Parkinson and Movement Disorder Society. © 2025 International Parkinson and Movement Disorder Society.