Cognitive Neuroscience​


Alzheimer’s & Dementia




Formal publication: October 2023

Authors: Ferrante, F. J., Migeot, J., Birba, A., Amoruso, L., Pérez, G., Hesse, E., Tagliazucchi, E., Estienne, C., Serrano, C., Slachevsky, A., Matallana, D., Reyes, P., Ibáñez, A., Fittipaldi, S., Campo, C. G., & García, A. M.

Abstract:  INTRODUCTION: Verbal fluency tasks are common in Alzheimer’s disease (AD) assessments. Yet, standard valid response counts fail to reveal disease-specific semantic memory patterns. Here, we leveraged automated word-property analysis to capture neurocognitive markers of AD vis-à-vis behavioral variant frontotemporal dementia (bvFTD).

METHODS: Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word’s frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group-level discrimination, patient-level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns.

RESULTS: Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group- and subject-level discrimination only in AD, also predicting executive outcomes. Disease-specific cortical thickness patterns were predicted by frequency in both disorders. Default-mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD.

DISCUSSION: Word-property analysis of fluency can boost AD characterization and diagnosis.