Person: Behrens, Maria Isabel
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Behrens
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Maria Isabel
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Publication Author Correction: the BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds(2024) Prado, Pavel; Medel, Vicente; González, Raúl; Sainz, Agustín; Vidal, Víctor; Santamaría, Hernando; Moguilner, Sebastián; Mejía, Jhony; Slachevsky Chonchol, Andrea; Behrens, Maria Isabel; Aguillón, David; Lopera, Francisco; Parra, Mario; Matallana, Diana; Adrián, Marcelo; García, Adolfo; Custodio, Nilton; Ávila, Alberto; Piña, Stefanie; Birba, Agustina; Fittipaldi, Sol; Legaz, Agustina; Ibáñez, AgustínIn this article the author name Maria Isabel Behrens was incorrectly written as Maria Isabel Beherens. The original article has been corrected.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 Cardiovascular risk factors and the allostatic interoceptive network in dementia(2025) Hazelton, Jessica; Migeot, Joaquín; Gonzalez, Raul; Altschuler, Florencia; Duran, Claudia; Wen, Olivia; Galván, Dante; Barttfeld, Pablo; Medel , Vicente; González, Cecilia; Castro, Ana; Hernández, Hernán; Gonzalez, Carolina; Castaner, Olga; Hu, Kun; Li, Peng; Maria Isabel Behrens; Behrens, Maria Isabel; Bruno, Martin; Cardona, Juan; Custodio, Nilton; Santamaria, Hernando; Garcia, Adolfo; Godoy, Maria; Avila, José; Maito, Marce; Matallana, Diana; Miller, Bruce; Lopera, Francisco; Okada , Maira; Pina, Stefanie; Possin, Katherine; France, Elisa de Paula; Reyes, Pablo; Slachevsky Chonchol, Andrea; Sosa, Ana; Takada, Leonel; Yokoyama, Jennifer; Ibanez, AgustinAims: Cardiovascular risk factors, such diabetes, hypertension, blood pressure, obesity, and smoking, are linked with allostatic-interoception-the continuous monitoring of internal bodily states in anticipation of environmental demands. These risk factors are associated with dementia risk. How these factors affect brain networks vulnerable to neurodegeneration and involved in allostatic-interoception, such as the Allostatic-Interoceptive Network (AIN), is unknown. We investigated the relationship between cardiovascular risk and AIN structure and function in frontotemporal lobar degeneration (FTLD) and Alzheimer's disease (AD). Methods and results: We recruited 1501 participants (304 with FTLD, 512 with AD, and 685 healthy controls) from the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat). A cardiovascular risk score was calculated based on: age, sex, diabetes, hypertension, systolic blood pressure, body mass index, and smoking status. Cardiovascular risk was associated with grey matter integrity and functional connectivity in age- and sex-matched patient-control groups focusing on predefined regions of interest within the AIN. Higher cardiovascular risk was associated with reduced structural integrity and functional connectivity within the AIN in both FTLD and AD. FTLD patients showed more extensive structural and functional connectivity disruptions throughout the AIN. In AD patients, structural reductions in the AIN were prominent, with functional connectivity restricted to the hippocampus, parahippocampal gyrus, and orbitofrontal regions. Conclusion: Cardiovascular risk factors appear to adversely impact the AIN structure and function, with disease-specific patterns of vulnerability. Results underscore the importance of integrating cardiovascular health into models of neurodegenerative disease and managing cardiovascular health to support brain integrity in dementia.Publication Social exposome and brain health outcomes of dementia across Latin America(2025) Migeot, Joaquin; Pina, Stefanie; Hernandez, Hernan; Gonzalez, Raul; Legaz, Agustina; Fittipaldi, Sol; Resende, Elisa de Paula França; Duran, Claudia; Avila, Jose; Behrens, Maria Isabel; Bruno, Martin; Cardona, Juan; Custodio, Nilton; García, Adolfo; Godoy, Maria; Hu, Kun; Lanata, Serggio; Lawlor, Brian; Lopera, Francisco; Maito, Marcelo; Matallana, Diana; Miller, Bruce; Miranda, J Jaime; Okada, Maira; Reyes, Pablo; Santamaria, Hernando; Slachevsky Chonchol, Andrea; Sosa, Ana; Takada, Leonel; Torres, Jacqueline; Vanneste, Sven; Valcour, Victor; Wen, Olivia; Yokoyama, Jennifer; Possin, Katherine; Ibañez, AgustinA multidimensional social exposome (MSE)-the combined lifespan measures of education, food insecurity, financial status, access to healthcare, childhood experiences, and more-may shape dementia risk and brain health over the lifespan, particularly in underserved regions like Latin America. However, the MSE effects on brain health and dementia are unknown. We evaluated 2211 individuals (controls, Alzheimer's disease, and frontotemporal lobar degeneration) from a non-representative sample across six Latin American countries. Adverse exposomes associate with poorer cognition in healthy aging. In dementia, more complex exposomes correlate with lower cognitive and functional performance, higher neuropsychiatric symptoms, and brain structural and connectivity alterations in frontal-temporal-limbic and cerebellar regions. Food insecurity, financial resources, subjective socioeconomic status, and access to healthcare emerge as critical predictors. Cumulative exposome measures surpass isolated factors in predicting clinical-cognitive profiles. Multiple sensitivity analyses confirm our results. Findings highlight the need for personalized approaches integrating MSE across the lifespan, emphasizing prevention and interventions targeting social disparities.