SLACHEVSKY CHONCHOL, ANDREA MARÍA
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SLACHEVSKY CHONCHOL, ANDREA MARÍA
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aslachevsky@med.uchile.cl
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56394322900

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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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Author Correction: The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds

2024 , Pavel Prado , Vicente Medel , Raul Gonzalez-Gomez , Agustín Sainz-Ballesteros , Victor Vidal , Hernando Santamaría-García , Sebastian Moguilner , Jhony Mejia , SLACHEVSKY CHONCHOL, ANDREA MARÍA , BEHRENS PELLEGRINO, MARIA ISABEL , David Aguillon , Francisco Lopera , Mario A. Parra , Diana Matallana , Marcelo Adrián Maito , Adolfo M. Garcia , Nilton Custodio , Alberto Ávila Funes , Stefanie Piña-Escudero , Agustina Birba , Sol Fittipaldi , Agustina Legaz , Agustín Ibañez

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The Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat): Driving Multicentric Research and Implementation Science

2021 , Agustin Ibanez , Jennifer S. Yokoyama , Katherine L. Possin , Diana Matallana , Francisco Lopera , Ricardo Nitrini , Leonel T. Takada , Nilton Custodio , Ana Luisa Sosa Ortiz , José Alberto Avila-Funes , BEHRENS PELLEGRINO, MARIA ISABEL , Andrea Slachevsky , Richard M. Myers , J. Nicholas Cochran , Luis Ignacio Brusco , Martin A. Bruno , Sonia M. D. Brucki , Stefanie Danielle Pina-Escudero , Maira Okada de Oliveira , Patricio Donnelly Kehoe , Adolfo M. Garcia , Juan Felipe Cardona , Hernando Santamaria-Garcia , Sebastian Moguilner , Claudia Duran-Aniotz , Enzo Tagliazucchi , Marcelo Maito , Erika Mariana Longoria Ibarrola , Maritza Pintado-Caipa , Maria Eugenia Godoy , Vera Bakman , Shireen Javandel , Kenneth S. Kosik , Victor Valcour , Bruce L. Miller

Dementia is becoming increasingly prevalent in Latin America, contrasting with stable or declining rates in North America and Europe. This scenario places unprecedented clinical, social, and economic burden upon patients, families, and health systems. The challenges prove particularly pressing for conditions with highly specific diagnostic and management demands, such as frontotemporal dementia. Here we introduce a research and networking initiative designed to tackle these ensuing hurdles, the Multi-partner consortium to expand dementia research in Latin America (ReDLat). First, we present ReDLat's regional research framework, aimed at identifying the unique genetic, social, and economic factors driving the presentation of frontotemporal dementia and Alzheimer's disease in Latin America relative to the US. We describe ongoing ReDLat studies in various fields and ongoing research extensions. Then, we introduce actions coordinated by ReDLat and the Latin America and Caribbean Consortium on Dementia (LAC-CD) to develop culturally appropriate diagnostic tools, regional visibility and capacity building, diplomatic coordination in local priority areas, and a knowledge-to-action framework toward a regional action plan. Together, these research and networking initiatives will help to establish strong cross-national bonds, support the implementation of regional dementia plans, enhance health systems' infrastructure, and increase translational research collaborations across the continent.

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Dementia in Latin America: Paving the way toward a regional action plan

2020 , Mario Alfredo Parra , Sandra Baez , Lucas Sedeño , Cecilia Gonzalez Campo , Hernando Santamaría‐García , Ivan Aprahamian , Paulo HF Bertolucci , Julian Bustin , Maria Aparecida Camargos Bicalho , Carlos Cano‐Gutierrez , Paulo Caramelli , Marcia L. F. Chaves , Patricia Cogram , Bárbara Costa Beber , Felipe A. Court , Leonardo Cruz Souza , Nilton Custodio , Andres Damian , Myriam de la Cruz , Roberta Diehl Rodriguez , Sonia Maria Dozzi Brucki , Lais Fajersztajn , Gonzalo A. Farías , Fernanda G. De Felice , Raffaele Ferrari , Fabricio Ferreira de Oliveira , Sergio T. Ferreira , Ceres Ferretti , Marcio Luiz Figueredo Balthazar , Norberto Anizio Ferreira Frota , Patricio Fuentes , Adolfo M. García , Patricia J. Garcia , Fábio Henrique de Gobbi Porto , Lissette Duque Peñailillo , Henry Willy Engler , Irene Maier , Ignacio F. Mata , Christian Gonzalez‐Billault , Oscar L. Lopez , Laura Morelli , Ricardo Nitrini , Yakeel T. Quiroz , Alejandra Guerrero Barragan , David Huepe , Fabricio Joao Pio , Claudia Kimie Suemoto , Renata Kochhann , Silvia Kochen , Fiona Kumfor , Serggio Lanata , Bruce Miller , Leticia Lessa Mansur , Mirna Lie Hosogi , Patricia Lillo , Jorge Llibre Guerra , David Lira , Francisco Lopera , Adelina Comas , José Alberto Avila‐Funes , Ana Luisa Sosa , Claudia Ramos , Elisa de Paula França Resende , Heather M. Snyder , Ioannis Tarnanas , Jenifer Yokoyama , Juan Llibre , Juan Felipe Cardona , Kate Possin , Kenneth S. Kosik , Rosa Montesinos , Sebastian Moguilner , Patricia Cristina Lourdes Solis , Renata Eloah de Lucena Ferretti‐Rebustini , Jeronimo Martin Ramirez , Diana Matallana , Lingani Mbakile‐Mahlanza , Alyne Mendonça Marques Ton , Ronnielly Melo Tavares , Eliane C Miotto , Graciela Muniz‐Terrera , Luis Arnoldo Muñoz‐Nevárez , David Orozco , Maira Okada de Oliveira , Olivier Piguet , Maritza Pintado Caipa , Stefanie Danielle Piña Escudero , Lucas Porcello Schilling , André Luiz Rodrigues Palmeira , Mônica Sanches Yassuda , Jose Manuel Santacruz‐Escudero , Rodrigo Bernardo Serafim , Jerusa Smid , Andrea Slachevsky , Cecilia Serrano , Marcio Soto‐Añari , Leonel Tadao Takada , Lea Tenenholz Grinberg , Antonio Lucio Teixeira , Maira Tonidandel Barbosa , Dominic Trépel , Agustin Ibanez

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The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds

2023 , Pavel Prado , Vicente Medel , Raul Gonzalez-Gomez , Agustín Sainz-Ballesteros , Victor Vidal , Hernando Santamaría-García , Sebastian Moguilner , Jhony Mejia , SLACHEVSKY CHONCHOL, ANDREA MARÍA , BEHRENS PELLEGRINO, MARIA ISABEL , David Aguillon , Francisco Lopera , Mario A. Parra , Diana Matallana , Marcelo Adrián Maito , Adolfo M. Garcia , Nilton Custodio , Alberto Ávila Funes , Stefanie Piña-Escudero , Agustina Birba , Sol Fittipaldi , Agustina Legaz , Agustín Ibañez

The Latin American Brain Health Institute (BrainLat) has released a unique multimodal neuroimaging dataset of 780 participants from Latin American. The dataset includes 530 patients with neurodegenerative diseases such as Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), multiple sclerosis (MS), Parkinson’s disease (PD), and 250 healthy controls (HCs). This dataset (62.7 ± 9.5 years, age range 21–89 years) was collected through a multicentric effort across five Latin American countries to address the need for affordable, scalable, and available biomarkers in regions with larger inequities. The BrainLat is the first regional collection of clinical and cognitive assessments, anatomical magnetic resonance imaging (MRI), resting-state functional MRI (fMRI), diffusion-weighted MRI (DWI), and high density resting-state electroencephalography (EEG) in dementia patients. In addition, it includes demographic information about harmonized recruitment and assessment protocols. The dataset is publicly available to encourage further research and development of tools and health applications for neurodegeneration based on multimodal neuroimaging, promoting the assessment of regional variability and inclusion of underrepresented participants in research.

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Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia

2024 , Pamela Lopes da Cunha , Fabián Ruiz , Franco Ferrante , Lucas Federico Sterpin , Agustín Ibáñez , SLACHEVSKY CHONCHOL, ANDREA MARÍA , Diana Matallana , Ángela Martínez , Eugenia Hesse , Adolfo M. García , Lorenzo Pini

Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer’s disease (AD) compromises the processing of entities expressed by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective with increased third-person references. Yet, no study has examined whether these patterns can be captured in connected speech via natural language processing tools. To tackle such gaps, we asked 96 participants (32 AD patients, 32 bvFTD patients, 32 healthy controls) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers (via part-of-speech and morphological tagging). We also extracted objective properties (frequency, phonological neighborhood, length, semantic variability) from each content word. In our main study (with 21 AD patients, 21 bvFTD patients, and 21 healthy controls), we used inferential statistics and machine learning for group-level and subject-level discrimination. The above linguistic features were correlated with patients’ scores in tests of general cognitive status and executive functions. We found that, compared with HCs, (i) AD (but not bvFTD) patients produced significantly fewer nouns, (ii) bvFTD (but not AD) patients used significantly more third-person markers, and (iii) both patient groups produced more frequent words. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.71). A generalizability test, with a model trained on the entire main study sample and tested on hold-out samples (11 AD patients, 11 bvFTD patients, 11 healthy controls), showed even better performance, with AUCs of 0.76 and 0.83 for AD and bvFTD, respectively. No linguistic feature was significantly correlated with cognitive test scores in either patient group. These results suggest that specific cognitive traits of each disorder can be captured automatically in connected speech, favoring interpretability for enhanced syndrome characterization, diagnosis, and monitoring.

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Does culture shape our understanding of others’ thoughts and emotions? An investigation across 12 countries.

2022 , François Quesque , Antoine Coutrot , Sharon Cox , Leonardo Cruz de Souza , Sandra Baez , Juan Felipe Cardona , Hannah Mulet-Perreault , Emma Flanagan , Alejandra Neely-Prado , Maria Florencia Clarens , Luciana Cassimiro , Gada Musa , Jennifer Kemp , Anne Botzung , Nathalie Philippi , Maura Cosseddu , Catalina Trujillo-Llano , Johan Sebastián Grisales-Cardenas , Sol Fittipaldi , Nahuel Magrath Guimet , Ismael Luis Calandri , Lucia Crivelli , Lucas Sedeno , Adolfo M. Garcia , Fermin Moreno , Begoña Indakoetxea , Alberto Benussi , Millena Vieira Brandão Moura , Hernando Santamaria-Garcia , Diana Matallana , Galina Pryanishnikova , Anna Morozova , Olga Iakovleva , Nadezda Veryugina , Oleg Levin , Lina Zhao , Junhua Liang , Thomas Duning , Thibaud Lebouvier , Florence Pasquier , David Huepe , Myriam Barandiaran , Andreas Johnen , Elena Lyashenko , Ricardo F. Allegri , Barbara Borroni , Frederic Blanc , Fen Wang , Mônica Sanches Yassuda , Patricia Lillo , Antônio Lúcio Teixeira , Paulo Caramelli , Carol Hudon , Andrea Slachevsky , Agustin Ibáñez , Michael Hornberger , Maxime Bertoux

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Multi-feature computational framework for combined signatures of dementia in underrepresented settings

2022 , Sebastian Moguilner , Agustina Birba , Sol Fittipaldi , Cecilia Gonzalez-Campo , Enzo Tagliazucchi , REYES, PABLO , Diana Matallana , Mario A Parra , SLACHEVSKY CHONCHOL, ANDREA MARÍA , Gonzalo Farías , Josefina Cruzat , Adolfo García , Harris A Eyre , Renaud La Joie , Gil Rabinovici , Robert Whelan , Agustín Ibáñez

Abstract Objective. The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings. Approach. We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat). Main results. The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). Results. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data. Significance. The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.

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Advancements in dementia research, diagnostics, and care in Latin America: Highlights from the 2023 Alzheimer's Association International conference satellite symposium in Mexico City

2024 , Ana Luisa Sosa , Sonia MD Brucki , Lucia Crivelli , Francisco Javier Lopera , Daisy M Acosta , Juliana Acosta‐Uribe , Diego Aguilar , Sara G Aguilar‐Navarro , Ricardo F Allegri , Paulo HF Bertolucci , Ismael L Calandri , Maria C Carrillo , Patricio Alexis Chrem Mendez , Mario Cornejo‐Olivas , Nilton Custodio , Andrés Damian , Leonardo Cruz de Souza , Claudia Duran‐Aniotz , Adolfo M García , Carmen García‐Peña , Mitzi M Gonzales , Lea T Grinberg , Agustin M Ibanez , Maryenela Zaida Illanes‐Manrique , Clifford R Jack , Jorge Mario Leon‐Salas , Jorge J Llibre‐Guerra , José Luna‐Muñoz , Diana Matallana , Bruce L Miller , Lorina Naci , Mario A Parra , Margaret Pericak‐Vance , Stefanie D Piña‐Escudero , Elisa de Paula França Resende , John M Ringman , Gustavo Sevlever , SLACHEVSKY CHONCHOL, ANDREA MARÍA , Claudia Kimie Suemoto , Victor Valcour , Andres Villegas‐Lanau , Mônica S Yassuda , Simin Mahinrad , Claire Sexton

AbstractINTRODUCTIONWhile Latin America (LatAm) is facing an increasing burden of dementia due to the rapid aging of the population, it remains underrepresented in dementia research, diagnostics, and care.METHODSIn 2023, the Alzheimer's Association hosted its eighth satellite symposium in Mexico, highlighting emerging dementia research, priorities, and challenges within LatAm.RESULTSSignificant initiatives in the region, including intracountry support, showcased their efforts in fostering national and international collaborations; genetic studies unveiled the unique genetic admixture in LatAm; researchers conducting emerging clinical trials discussed ongoing culturally specific interventions; and the urgent need to harmonize practices and studies, improve diagnosis and care, and use affordable biomarkers in the region was highlighted.DISCUSSIONThe myriad of topics discussed at the 2023 AAIC satellite symposium highlighted the growing research efforts in LatAm, providing valuable insights into dementia biology, genetics, epidemiology, treatment, and care.

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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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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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