Cognitive Neuroscience


Alzheimer’s & dementia (Amsterdam, Netherlands)




Formal publication: January 2022

Authors: Sanz, C., Carrillo, F., Slachevsky, A., Forno, G., Gorno Tempini, M. L., Villagra, R., Ibáñez, A., Tagliazucchi, E., & García, A. M.

Abstract: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 automated measures of domains typically affected in ADD: semantic granularity (coarseness 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 differences in both measures. Also, these features robustly discriminated between ADD patients and HC, while yielding near-chance classification between PD patients and HCs.

Discussion: Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.