98 research outputs found
Brain clocks capture diversity and disparity in aging and dementia
No full textFil: Ibanez, Agustin. Trinity College; Irlanda.Fil: Moguilner, Sebastian. Harvard Medical School; United States.Fil: Baez, Sandra. Universidad de los Andes; Colombia.Fil: Barttfeld, Pablo. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Barttfeld, Pablo. Consejo Nacional de Investigaciones Científicas y Tecnológicas. Instituto de Investigaciones Psicológicas; Argentina.Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer’s disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R²=0.37, F²=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer’s disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer’s disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.info:eu-repo/semantics/acceptedVersionFil: Ibanez, Agustin. Trinity College; Irlanda.Fil: Moguilner, Sebastian. Harvard Medical School; United States.Fil: Baez, Sandra. Universidad de los Andes; Colombia.Fil: Barttfeld, Pablo. Universidad Nacional de Córdoba. Facultad de Psicología; Argentina.Fil: Barttfeld, Pablo. Consejo Nacional de Investigaciones Científicas y Tecnológicas. Instituto de Investigaciones Psicológicas; Argentina
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference
No full textCharacterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.Fil: Gonzalez Gomez, Raul. Universidad Adolfo Ibañez; ChileFil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; ChileFil: Moguilner, Sebastian Gabriel. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chil
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference
No full textCharacterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.Fil: Gonzalez Gomez, Raul. Universidad Adolfo Ibañez; ChileFil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; ChileFil: Moguilner, Sebastian Gabriel. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chil
Functional connectivity analysis during processing of grammatical violations of natural and artificial language: evidence for shared mechanisms.
No full textLa comprensión del lenguaje es un proceso de extrema complejidad. El estudio de sus bases neurofisiológicas se ha facilitado gracias al registro de la actividad electroencefalográfica, (EEG), identificándose potenciales evocados relacionados con procesos cognitivos específicos durante el procesamiento de oraciones o palabras. Los potenciales evocados son el producto de la actividad en diversas bandas de frecuencia del EEG. La descomposición de la señal en dichas bandas posibilita distinguir diferentes actividades con distintos valores funcionales y la manera en la cual distintas regiones interactúan durante el proceso de comprensión del lenguaje. En este trabajo analizamos para tres bandas de frecuencias distintas (theta, alfa y beta), el grado de conectividad funcional entre electrodos durante el procesamiento de oraciones gramaticales y no gramaticales en lenguaje natural y artificial. 15 adultos sanos fueron entrenados en las reglas combinatorias de una gramática artificial. En el testeo se registró la actividad electroencefalográfica mientras se presentaban 80 ensayos nuevos, de los cuales 40 presentaba un error de las reglas entrenadas. Se presentaron además 80 oraciones en castellano, 40 de ellas con un error gramatical. La aparición de un error elicitó un potencial N400 y P600 equivalente en ambas gramáticas, e indujo en ambos casos un mismo patrón de conectividad funcional entre electrodos. Los resultados muestran que el procesamiento de oraciones no gramaticales durante la comprensión del lenguaje natural es funcionalmente equivalente a la detección de errores combinatorios de reglas estadísticas, como las entrenadas en gramática artificial.Functional connectivity analysis during processing of grammatical violations of natural and artificial language: evidence for shared mechanisms. Language comprehension is an extremely complex process. The study of its neurophysiological bases has been facilitated due to the use of electroencephalographic (EEG) recordings, identifying evoked potentials related to specific cognitive processes during sentence or word processing. Evoked potentials are the product of activity in different frequency bands of the EEG. Signal decomposition into these frequency bands allows to distinguish between activities with different functional values and the manner in which regions interact during language comprehension. In the present work we analyzed for three frequency bands (theta, alpha and beta), the level of functional connectivity between electrodes while processing grammatical and non-grammatical sentences in natural and artificial language. 15 normotypic adults were trained in the use of combinatorial rules of an artificial grammar. In the test phase, EEG activity was recorded while 80 new trials were presented, 40 of which showed an error of the previously trained rules. In addition, 80 Spanish sentences were presented, 40 of which had a grammatical error. The appearance of an error elicited a biphasic N400/P600 complex, and induced the same pattern of functional connectivity in both grammars. Results show that processing of non-grammatical sentences during natural language comprehension is functionally equivalent to the detection of combinatorial errors of statistical rules, such as those trained in the artificial grammar.Fil: Moguilner, Sebastian Gabriel. Comisión Nacional de Energía Atómica; ArgentinaFil: Tabullo, Angel Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; ArgentinaFil: Wainselboim, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentin
Biophysical models reveal gender and region links between dementia, hyperexcitability and severity
No full textBrain clocks capture diversity and disparity in aging and dementia
No full textThis repository contains the code and input preprocessed data to perform the analyses of the project: "Brain clocks capture diversity and disparity in aging and dementia".
Contact: [email protected]
Biophysical models reveal gender and region links between dementia, hyperexcitability and severity
No full textBrain clocks capture diversity and disparity in aging and dementia
No full textThis repository contains the code and input preprocessed data to perform the analyses of the project: "Brain clocks capture diversity and disparity in aging and dementia".
Contact: [email protected]
Factors associated with healthy aging in Latin American populations
No full textLatin American populations may present patterns of sociodemographic, ethnic and cultural diversity that can defy current universal models of healthy aging. The potential combination of risk factors that influence aging across populations in Latin American and Caribbean (LAC) countries is unknown. Compared to other regions where classical factors such as age and sex drive healthy aging, higher disparity-related factors and between-country variability could influence healthy aging in LAC countries. We investigated the combined impact of social determinants of health (SDH), lifestyle factors, cardiometabolic factors, mental health symptoms and demographics (age, sex) on healthy aging (cognition and functional ability) across LAC countries with different levels of socioeconomic development using cross-sectional and longitudinal machine learning models (n = 44,394 participants). Risk factors associated with social and health disparities, including SDH (β > 0.3), mental health (β > 0.6) and cardiometabolic risks (β > 0.22), significantly influenced healthy aging more than age and sex (with null or smaller effects: β < 0.2). These heterogeneous patterns were more pronounced in low-income to middle-income LAC countries compared to high-income LAC countries (cross-sectional comparisons), and in an upper-income to middle-income LAC country, Costa Rica, compared to China, a non-upper-income to middle-income LAC country (longitudinal comparisons). These inequity-associated and region-specific patterns inform national risk assessments of healthy aging in LAC countries and regionally tailored public health interventions.Fil: Santamaria Garcia, Hernando. Pontificia Universidad Javeriana; ColombiaFil: Sainz Ballesteros, Agustín. Universidad Adolfo Ibañez; ChileFil: Hernandez, Hernán. Universidad Adolfo Ibañez; Chile. Universidad de Concepción; ChileFil: Moguilner, Sebastian Gabriel. Universidad Adolfo Ibañez; Chile. University of California; Estados UnidosFil: Maito, Marcelo. Universidad Adolfo Ibañez; ChileFil: OchoaRosales, Carolina. Universidad Adolfo Ibañez; ChileFil: Corley, Michael. No especifíca;Fil: Valcour, Victor. University of California; Estados UnidosFil: Miranda, J. Jaime. No especifíca;Fil: Lawlor, Brian. University of California; Estados UnidosFil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; Chil
An unaware agenda: Interictal consciousness impairments in epileptic patients
No full textConsciousness impairments have been described as a cornerstone of epilepsy. Generalized seizures are usually characterized by a complete loss of consciousness, while focal seizures have more variable degrees of responsiveness. In addition to these impairments that occur during ictal episodes, alterations of consciousness have also been repeatedly observed between seizures (i.e., during interictal periods). In this opinion paper, we review evidence supporting the novel hypothesis that epilepsy produces consciousness impairments which remain present interictally. Then, we discuss therapies aimed to reduce seizure frequency, which may modulate consciousness between epileptic seizures. We conclude with a consideration of relevant pathophysiological mechanisms. In particular, the thalamocortical network seems to be involved in both seizure generation and interictal consciousness impairments, which could inaugurate a promising translational agenda for epilepsy studies.Fil: Moguilner, Sebastian Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Fundación Escuela de Medicina Nuclear; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; ArgentinaFil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; ArgentinaFil: Mikulan, Ezequiel Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; ArgentinaFil: García, Maria del Carmen. Hospital Italiano; ArgentinaFil: Vaucheret, Esteban. Hospital Italiano; ArgentinaFil: Amarillo Gómez, Yimy. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bekinchstein, Tristán. University of Cambridge; Reino UnidoFil: Ibáñez Barassi, Agustín Mariano. Universidad Autonoma del Caribe; Colombia. Universidad Adolfo Ibañez; Chile. Australian Research Council; Australia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentin
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