Person: Slachevsky Chonchol, Andrea
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Slachevsky Chonchol
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Andrea
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Andrea María Slachevsky Conchol
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Publication The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds(2023) Prado, Pavel; Medel, Vicente; Gonzalez, Raul; Sainz, Agustín; Vidal , Victor; Santamaría, Hernando; Moguilner, Sebastian; Mejia, Jhony; Slachevsky Chonchol, Andrea; Behrens, Maria; Aguillon, David; Lopera, Francisco; Parra, Mario; Matallana,Diana; Maito, Marcelo; Garcia, Adolfo; Custodio, Nilton; Ávila, Alberto; Piña, Stefanie; Birba, Agustina; Fittipaldi, Sol; Legaz, Agustina; Ibañez, AgustínThe 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.Publication Classification of Alzheimer’s disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study(2023) Maito, Marcelo Adrián; Santamaría-García, Hernando; Moguilner, Sebastián; Possin, Katherine L.; Godoy, María E.; Avila-Funes, José Alberto; Behrens, Maria Isabel; Brusco, Ignacio L. Maira Okada de Oliveira,b,r,s,ae Stefanie D. Pina-Escuder; Bruno, Martín A.; Cardona, Juan F.; Custodio, Nilton; García, Adolfo M.; Javandel, Shireen; Lopera, Francisco; Matallana, Diana L.; Miller, Bruce; Okada de Oliveira, Maira; Pina Escudero, Stefanie; Slachevsky Chonchol, Andrea; Ana L Sosa Ortiz; Takada, Leonel T.; Tagliazuchi, Enzo; Valcour, Victor; Yokoyama, Jennifer S.; Ibañez, AgustínBackground Global brain health initiatives call for improving methods for the diagnosis of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) in underrepresented populations. However, diagnostic procedures in upper middle-income countries (UMICs) and lower-middle income countries (LMICs), such as Latin American countries (LAC), face multiple challenges. These include the heterogeneity in diagnostic methods, lack of clinical harmonisation, and limited access to biomarkers. Methods This cross-sectional observational study aimed to identify the best combination of predictors to discriminate between AD and FTD using demographic, clinical and cognitive data among 1794 participants [904 diagnosed with AD, 282 diagnosed with FTD, and 606 healthy controls (HCs)] collected in 11 clinical centres across five LAC (ReDLat cohort). Findings A fully automated computational approach included classical statistical methods, support vector machine procedures, and machine learning techniques (random forest and sequential feature selection procedures). Results demonstrated an accurate classification of patients with AD and FTD and HCs. A machine learning model produced the best values to differentiate AD from FTD patients with an accuracy = 0.91. The top features included social cognition, neuropsychiatric symptoms, executive functioning performance, and cognitive screening; with secondary contributions from age, educational attainment, and sex. Interpretation Results demonstrate that data-driven techniques applied in archival clinical datasets could enhance diagnostic procedures in regions with limited resources. These results also suggest specific fine-grained cognitive and behavioural measures may aid in the diagnosis of AD and FTD in LAC. Moreover, our results highlight an opportunity for harmonisation of clinical tools for dementia diagnosis in the region.Publication Biomarkers for dementia in Latin American countries: Gaps andopportunities(2023) Parra, Mario A.; Orellana, Paulina; León, Tomas; Victoria, Cabello G.; Henriquez, Fernando; Gomez, Rodrigo; Avalos, Constanza; Damian, Andres; Slachevsky Chonchol, Andrea; Ibañez, Agustin; Zetterberg, Henrik; Tijms, Betty M.; Yokoyama, Jennifer S.; Piña-Escudero, Stefanie D.; Cochran, Nicholas; Matallana, Diana L.; Acosta, Daisy; Allegri, Ricardo; Arias-Suáres, Bianca P.; Barra, Bernardo; Behrens, María Isabel; Brucki, Sonia M.D.; Busatto, Geraldo; Caramelli, Paulo; Castro-Suarez, Sheila; Contreras, Valeria; Custodio, Nilton; Dansilio, Sergio; De la Cruz-Puebla, Myriam; Cruz de Souza, Leonado; Díaz, Monica M.; Duque, Lissette; Farias, Gonzalo A.; Ferreira, Sergio T.; Magrath Guimet, Nahuel; Kmaid, Ana; Lira, David; Lopera, Francisco; Mar Meza, Beatriz; Miotto, Eliane C.Limited knowledge on dementia biomarkers in Latin American and Caribbean (LAC)countries remains a serious barrier. Here, we reported a survey to explore the ongo-ing work, needs, interests, potential barriers, and opportunities for future studiesrelated to biomarkers. The results show that neuroimaging is the most used biomarker(73%), followed by genetic studies (40%), peripheral fluids biomarkers (31%), and cere-brospinal fluid biomarkers (29%). Regarding barriers in LAC, lack of funding appears toundermine the implementation of biomarkers in clinical or research settings, followedby insufficient infrastructure and training. The survey revealed that despite the abovebarriers, the region holds a great potential to advance dementia biomarkers research.Considering the unique contributions that LAC could make to this growing field,we highlight the urgent need to expand biomarker research. These insights allowedus to propose an action plan that addresses the recommendations for a biomarkerframework recently proposed by regional experts.Publication Validation of Picture Free and Cued Selective Reminding Test for Illiteracy in Lima, Perú(2022) Montesinos, Rosa; Parodi, José; Díaz, Mónica; Herrera, Eder; Valeriano, Elizabeth; Soto, Ambar; Delgado, Carolina; Slachevsky Chonchol, Andrea; Custodio, NiltonDementia in Latin America is a crucial public health problem. Identifying brief cognitive screening (BCS) tools for the primary care setting is crucial, particularly for illiterate individuals. We evaluated tool performance characteristics and validated the free and total recall sections of the Free and Cued Selective Reminding Test-Picture version (FCSRT-Picture) to discriminate between 63 patients with early Alzheimer's disease dementia (ADD), 60 amnestic mild cognitive impairment (aMCI) and 64 cognitively healthy Peruvian individuals with illiteracy from an urban area. Clinical, functional, and cognitive assessments were performed. FCSRT-Picture performance was assessed using receiver operating characteristic curve analyses. The mean ± standard deviation scores were 7.7 ± 1.0 in ADD, 11.8 ± 1.6 in aMCI, and 29.5 ± 1.8 in controls. The FCSRT-Picture had better performance characteristics for distinguishing controls from aMCI compared with several other BCS tools, but similar characteristics between controls and early ADD. The FCSRT-Picture is a reliable BCS tool for illiteracy in Perú.