Browsing by Author "Tagliazucchi, Enzo"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Publication Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations(2024) Moguilner, Sebastian; Baez, Sandra; Hernandez, Hernan; Migeot, Joaquín; Legaz, Agustina; Gonzalez, Raul; Farina, Francesca; Prado, Pavel; Cuadros, Jhosmary; Tagliazucchi, Enzo; Altschuler, Florencia; Maito, Marcelo; Godoy, María; Cruzat, Josefina; Valdes, Pedro; Lopera, Francisco; Ochoa, John; Gonzalez, Alfredis; Bonilla, Jasmin; Gonzalez, Rodrigo; Anghinah, Renato; d'Almeida, Luís; Fittipaldi, Sol; Medel, Vicente; Olivares, Daniela; Yener, Görsev; Escudero, Javier; Babiloni, Claudio; Whelan, Robert; Güntekin, Bahar; Yırıkoğulları, Harun; Santamaria, Hernando; Fernández, Alberto; Huepe, David; Di Caterina, Gaetano; Soto, Marcio; Birba, Agustina; Sainz, Agustin; Coronel, Carlos; Yigezu, Amanuel; Behrens, Maria IsabelLos relojes cerebrales capturan la diversidad y las disparidades en el envejecimiento y la demencia en poblaciones geográficamente diversas. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Brain 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.Publication Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations(2024) Moguilner, Sebastian; Baez, Sandra; Hernandez, Hernan; Migeot, Joaquín; Legaz, Agustina; Gonzalez, Raul; Farina, Francesca; Prado, Pavel; Cuadros, Jhosmary; Tagliazucchi, Enzo; Altschuler, Florencia; Maito, Marcelo; Godoy, María; Cruzat, Josefina; Valdes, Pedro; Lopera, Francisco; Ochoa, John; González, Alfredis; Bonilla, Jazmín; Gonzalez, Rodrigo; Anghinah, Renato; d'Almeida, Luis; Fittipaldi, Sol; Medel, Vicente; Olivares, Daniela; Yener, Görsev; Escudero, Javier; Babiloni, Claudio; Whelan, Robert; Guntekin, Bahar; Yırıkoğulları, Harun; Santamaria, Hernando; Fernández, Alberto; Huepe, David; Di Caterina, Gaetano; Soto, Marcio; Birba, Agustina; Sainz, Agustin; Coronel, Carlos; Yigezu, Amanuel; Behrens, Maria IsabelBrain 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. Los relojes cerebrales, que cuantifican las discrepancias entre la edad cerebral y la edad cronológica, son prometedores para comprender la salud y la enfermedad cerebral. Sin embargo, se desconoce el impacto de la diversidad (incluida la geográfica, socioeconómica, sociodemográfica, sexual y neurodegenerativa) en la brecha de edad cerebral. Analizamos conjuntos de datos de 5306 participantes en 15 países (7 países de América Latina y el Caribe (ALC) y 8 países no pertenecientes a ALC). Con base en interacciones de orden superior, desarrollamos una arquitectura de aprendizaje profundo de brecha de edad cerebral para imágenes de resonancia magnética funcional (2953) y electroencefalografía (2353). Los conjuntos de datos comprendían controles sanos e individuos con deterioro cognitivo leve, enfermedad de Alzheimer y demencia frontotemporal variante conductual. Los modelos LAC evidenciaron edades cerebrales más avanzadas (imágenes por resonancia magnética funcional: error direccional medio = 5,60, error cuadrático medio (rmse) = 11,91; electroencefalografía: error direccional medio = 5,34, rmse = 9,82) asociadas con redes frontoposteriores en comparación con los modelos no LAC. La desigualdad socioeconómica estructural, la contaminación y las disparidades en la salud fueron predictores influyentes de mayores brechas de edad cerebral, especialmente en LAC (R² = 0,37, F² = 0,59, rmse = 6,9). Se encontró una brecha ascendente de edad cerebral desde controles sanos hasta deterioro cognitivo leve y enfermedad de Alzheimer. En LAC, observamos brechas de edad cerebral más grandes en mujeres en los grupos de control y enfermedad de Alzheimer en comparación con los respectivos hombres. Los resultados no se explicaron por variaciones en la calidad de la señal, la demografía o los métodos de adquisición. Estos hallazgos proporcionan un marco cuantitativo que captura la diversidad del envejecimiento cerebral acelerado.Publication Multi-feature computational framework for combined signatures of dementia in underrepresented settings(2022) Moguilner, Sebastián; Birba, Agustina; Fittipaldi, Sol; Gonzalez, Cecilia; Tagliazucchi, Enzo; Reyes, Pablo; Matallana, Diana; Parra, Mario; Slachevsky Chonchol, Andrea; Farías, Gonzalo; Cruzat, Josefina; García, Adolfo; Eyre, Harris; La Joie, Renaud; Rabinovici, Gil; Whelan, Robert; Ibáñez, AgustínObjective.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 countriesPublication Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia(2024) Ferrante, Franco J.; Migeot, Joaquín; Birba, Agustina; Amoruso, Lucía; Pérez, Gonzalo; Hesse, Eugenia; Tagliazucchi, Enzo; Estienne, Claudio; Serrano, Cecilia; Slachevsky, Andrea; Matallana, Diana; Reyes, Pablo; Ibáñez, Agustín; Fittipaldi, Sol; Gonzalez, Cecilia; García, Adolfo M.Introduction: Verbal 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). Methods: Patients 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. Results: Valid 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. Discussion: Word-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.Item The Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat): Driving Multicentric Research and Implementation Science(2021) Ibáñez, Agustín; Yokoyama, Jennifer S.; Possin, Katherine L.; Matallana, Diana; Lopera, Francisco; Nitrini, Ricardo; Takada, Leonel T.; Custodio, Nilton; Sosa Ortiz, Ana Luisa; Avila-Funes, José Alberto; Behrens, María Isabel; Slachevsky, Andrea; Myers, Richard M.; Cochran, J. Nicholas; Brusco, Luis Ignacio; Bruno, Martin A.; Brucki, Sonia M. D.; Pina-Escudero, Stefanie Danielle; Oliveira, Maira Okada de; Donnelly Kehoe, Patricio; Santamaria-Garcia, Hernando; Moguilner, Sebastián; Tagliazucchi, Enzo; Maito, Marcelo; Longoria Ibarrola, Erika Mariana; Pintado-Caipa, Maritza; Godoy, Maria Eugenia; Bakman, Vera; Javandel, Shireen; Kosik, Kenneth S.; Valcour, Victor; Miller, Bruce L.; The Latin America the Caribbean Consortium on Dementia (LAC-CD)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.