Automated text-level semantic markers of Alzheimer’s disease

SANZ, CAMILA; CARRILLO, FACUNDO; SLACHEVSKY, ANDREA; FORNO, GONZALO; MARIA LUISA GORNO TEMPINI; ROQUE VILLAGRA; AGUSTÍN IBAÑEZ; ENZO TAGLIAZUCCHI; ADOLFO GARCÍA

Alzheimer's and Dementia

Wiley

Año: 2021

Introduction: Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer’s disease dementia (ADD). Yet, most research is undermined by low interpretability and specificity.Methods: Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate ADD patients from healthy controls (HCs) based on auto- mated measures of domains typically affected in ADD: semantic granularity (coarse- ness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson’s disease (PD) patients.Results: Relative to controls, ADD (but not PD) patients exhibited significant differ- ences in both measures. Also, these features robustly discriminated between ADD