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

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dc.contributor.authorFerrante, Franco J.
dc.contributor.authorMigeot, Joaquín
dc.contributor.authorBirba, Agustina
dc.contributor.authorAmoruso, Lucía
dc.contributor.authorPérez, Gonzalo
dc.contributor.authorHesse, Eugenia
dc.contributor.authorTagliazucchi, Enzo
dc.contributor.authorEstienne, Claudio
dc.contributor.authorSerrano, Cecilia
dc.contributor.authorSlachevsky, Andrea
dc.contributor.authorMatallana, Diana
dc.contributor.authorReyes, Pablo
dc.contributor.authorIbáñez, Agustín
dc.contributor.authorFittipaldi, Sol
dc.contributor.authorGonzalez, Cecilia
dc.contributor.authorGarcía, Adolfo M.
dc.date.accessioned2024-06-06T17:10:06Z
dc.date.available2024-06-06T17:10:06Z
dc.date.issued2024
dc.description.abstractIntroduction: 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.
dc.description.versionVersión publicada
dc.format.extent16 p.
dc.identifier.citationFerrante FJ, Migeot J, Birba A, et al. Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia. Alzheimer's Dement. 2024; 20: 925–940. https://doi.org/10.1002/alz.13472
dc.identifier.doihttps://doi.org/10.1002/alz.13472
dc.identifier.urihttps://hdl.handle.net/11447/9062
dc.language.isoen
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/cl/
dc.subjectElectroencephalography
dc.subjectMachine learning
dc.subjectNeurodegeneration
dc.subjectNeuroimaging
dc.subjectSemantic memory
dc.subjectWord properties
dc.titleMultivariate word properties in fluency tasks reveal markers of Alzheimer's dementia
dc.typeArticle
dcterms.accessRightsAcceso abierto
dcterms.sourceAlzheimer’s & Dementia
dspace.entity.typePublication

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