Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia

Franco J. Ferrante, Joaquín Migeot, Agustina Birba, Lucía Amoruso, Gonzalo Pérez, Eugenia Hesse, Enzo Tagliazucchi, Claudio Estienne, Cecilia Serrano, Andrea Slachevsky, Diana Matallana, Pablo Reyes, Agustín Ibáñez, Sol Fittipaldi, Cecilia Gonzalez Campo, Adolfo M. García

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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. Highlights: We report novel word-property analyses of verbal fluency in AD and bvFTD. Standard valid response counts captured deficits and brain patterns in both groups. Specific word properties (e.g., frequency, granularity) were altered only in AD. Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD. Word-property analysis of fluency can boost AD characterization and diagnosis.

Original languageEnglish
Pages (from-to)925-940
Number of pages16
JournalAlzheimer's and Dementia
Volume20
Issue number2
DOIs
StatePublished - Feb 2024
Externally publishedYes

Keywords

  • electroencephalography
  • machine learning
  • neurodegeneration
  • neuroimaging
  • semantic memory
  • word properties

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