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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2024 2024 2023 2023 2022 2022 2021 2021 2020 2020 0.0 0.0 0.5 0.5 1.0 1.0 1.5 1.5 2.0 2.0 2.5 2.5 3.0 3.0

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Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

2024 , Sebastian Moguilner , Sandra Baez , Hernan Hernandez , Joaquín Migeot , Agustina Legaz , Raul Gonzalez-Gomez , Francesca R. Farina , Pavel Prado , Jhosmary Cuadros , Enzo Tagliazucchi , Florencia Altschuler , Marcelo Adrián Maito , María E. Godoy , Josephine Cruzat , Pedro A. Valdes-Sosa , Francisco Lopera , John Fredy Ochoa-Gómez , Alfredis Gonzalez Hernandez , Jasmin Bonilla-Santos , Rodrigo A. Gonzalez-Montealegre , Renato Anghinah , Luís E. d’Almeida Manfrinati , Sol Fittipaldi , Vicente Medel , Daniela Olivares , Görsev G. Yener , Javier Escudero , Claudio Babiloni , Robert Whelan , Bahar Güntekin , Harun Yırıkoğulları , Hernando Santamaria-Garcia , Alberto Fernández Lucas , David Huepe , Gaetano Di Caterina , Marcio Soto-Añari , Agustina Birba , Agustin Sainz-Ballesteros , Carlos Coronel-Oliveros , Amanuel Yigezu , Eduar Herrera , Daniel Abasolo , Kerry Kilborn , Nicolás Rubido , Ruaridh A. Clark , Ruben Herzog , Deniz Yerlikaya , Kun Hu , Mario A. Parra , Pablo Reyes , Adolfo M. García , Diana L. Matallana , José Alberto Avila-Funes , Andrea Slachevsky , BEHRENS PELLEGRINO, MARIA ISABEL , Nilton Custodio , Juan F. Cardona , Pablo Barttfeld , Ignacio L. Brusco , Martín A. Bruno , Ana L. Sosa Ortiz , Stefanie D. Pina-Escudero , Leonel T. Takada , Elisa Resende , Katherine L. Possin , Maira Okada de Oliveira , Alejandro Lopez-Valdes , Brian Lawlor , Ian H. Robertson , Kenneth S. Kosik , Claudia Duran-Aniotz , Victor Valcour , Jennifer S. Yokoyama , Bruce Miller , Agustin Ibanez

AbstractBrain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

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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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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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Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

2024 , Sebastian Moguilner , Sandra Baez , Hernan Hernandez , Joaquín Migeot , Agustina Legaz , Raul Gonzalez-Gomez , Francesca R. Farina , Pavel Prado , Jhosmary Cuadros , Enzo Tagliazucchi , Florencia Altschuler , Marcelo Adrián Maito , María E. Godoy , Josephine Cruzat , Pedro A. Valdes-Sosa , Francisco Lopera , John Fredy Ochoa-Gómez , Alfredis Gonzalez Hernandez , Jasmin Bonilla-Santos , Rodrigo A. Gonzalez-Montealegre , Renato Anghinah , Luís E. d’Almeida Manfrinati , Sol Fittipaldi , Vicente Medel , Daniela Olivares , Görsev G. Yener , Javier Escudero , Claudio Babiloni , Robert Whelan , Bahar Güntekin , Harun Yırıkoğulları , Hernando Santamaria-Garcia , Alberto Fernández Lucas , David Huepe , Gaetano Di Caterina , Marcio Soto-Añari , Agustina Birba , Agustin Sainz-Ballesteros , Carlos Coronel-Oliveros , Amanuel Yigezu , Eduar Herrera , Daniel Abasolo , Kerry Kilborn , Nicolás Rubido , Ruaridh A. Clark , Ruben Herzog , Deniz Yerlikaya , Kun Hu , Mario A. Parra , Pablo Reyes , Adolfo M. García , Diana L. Matallana , José Alberto Avila-Funes , Andrea Slachevsky , BEHRENS PELLEGRINO, MARIA ISABEL , Nilton Custodio , Juan F. Cardona , Pablo Barttfeld , Ignacio L. Brusco , Martín A. Bruno , Ana L. Sosa Ortiz , Stefanie D. Pina-Escudero , Leonel T. Takada , Elisa Resende , Katherine L. Possin , Maira Okada de Oliveira , Alejandro Lopez-Valdes , Brian Lawlor , Ian H. Robertson , Kenneth S. Kosik , Claudia Duran-Aniotz , Victor Valcour , Jennifer S. Yokoyama , Bruce Miller , Agustin Ibanez

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Educational disparities in brain health and dementia across Latin America and the United States

2024 , Raul Gonzalez‐Gomez , Agustina Legaz , Sebastián Moguilner , Josephine Cruzat , Hernán Hernández , Sandra Baez , Rafael Cocchi , Carlos Coronel‐Olivero , Vicente Medel , Enzo Tagliazuchi , Joaquín Migeot , Carolina Ochoa‐Rosales , Marcelo Adrián Maito , Pablo Reyes , Hernando Santamaria Garcia , Maria E. Godoy , Shireen Javandel , Adolfo M. García , Diana L. Matallana , José Alberto Avila‐Funes , María I. Behrens , SLACHEVSKY CHONCHOL, ANDREA MARÍA , Nilton Custodio , Juan F. Cardona , Ignacio L. Brusco , Martín A. Bruno , Ana L. Sosa Ortiz , Stefanie D. Pina‐Escudero , Leonel T. Takada , Elisa de Paula França Resende , Victor Valcour , Katherine L. Possin , Maira Okada de Oliveira , Francisco Lopera , Brian Lawlor , Kun Hu , Bruce Miller , Jennifer S. Yokoyama , Cecilia Gonzalez Campo , Agustin Ibañez

AbstractBACKGROUNDEducation influences brain health and dementia. However, its impact across regions, specifically Latin America (LA) and the United States (US), is unknown.METHODSA total of 1412 participants comprising controls, patients with Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD) from LA and the US were included. We studied the association of education with brain volume and functional connectivity while controlling for imaging quality and variability, age, sex, total intracranial volume (TIV), and recording type.RESULTSEducation influenced brain measures, explaining 24%–98% of the geographical differences. The educational disparities between LA and the US were associated with gray matter volume and connectivity variations, especially in LA and AD patients. Education emerged as a critical factor in classifying aging and dementia across regions.DISCUSSIONThe results underscore the impact of education on brain structure and function in LA, highlighting the importance of incorporating educational factors into diagnosing, care, and prevention, and emphasizing the need for global diversity in research.Highlights Lower education was linked to reduced brain volume and connectivity in healthy controls (HCs), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD). Latin American cohorts have lower educational levels compared to the those in the United States. Educational disparities majorly drive brain health differences between regions. Educational differences were significant in both conditions, but more in AD than FTLD. Education stands as a critical factor in classifying aging and dementia across regions.

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