REYES ARAMBURU, EDISON PABLO
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REYES ARAMBURU, EDISON PABLO
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preyes@udd.cl
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35870354700

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2023 2023 2022 2022 2021 2021 2020 2020 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 0.8 0.8 1.0 1.0

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Publication

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach

2020 , M. Belen Bachli , Lucas Sedeño , Jeremi K. Ochab , Olivier Piguet , Fiona Kumfor , Pablo Reyes , Teresa Torralva , María Roca , Juan Felipe Cardona , Cecilia Gonzalez Campo , Eduar Herrera , Andrea Slachevsky , Diana Matallana , Facundo Manes , Adolfo M. García , Agustín Ibáñez , Dante R. Chialvo

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

2023 , Franco J. Ferrante , Joaquín Migeot , Agustina Birba , Lucía Amoruso , Gonzalo Pérez , Eugenia Hesse , Enzo Tagliazucchi , Claudio Estienne , Cecilia Serrano , SLACHEVSKY CHONCHOL, ANDREA MARÍA , Diana Matallana , Pablo Reyes , Agustín Ibáñez , Sol Fittipaldi , Cecilia Gonzalez Campo , Adolfo M. García

AbstractINTRODUCTIONVerbal 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).METHODSPatients 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.RESULTSValid 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.DISCUSSIONWord‐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.

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