Brain clocks capture diversity and disparity in aging and dementia

Fil: 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

Characterizing 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

AbstractCharacterizing 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

Functional connectivity analysis during processing of grammatical violations of natural and artificial language: evidence for shared mechanisms.

La 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

An unaware agenda: interictal consciousness impairments in epileptic patients.

Consciousness impairments have been described as a cornerstone of epilepsy. Generalized seizures are usually characterized by a complete loss of consciousness, whereas 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 article, 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

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

Objetivo. El diagnóstico diferencial de la variante conductual de la demencia frontotemporal (bvFTD) y La enfermedad de Alzheimer (EA) sigue siendo un desafío en grupos subrepresentados y subdiagnosticados, incluidos los latinos, ya que los biomarcadores avanzados rara vez están disponibles. Directrices recientes para el estudio de demencia destacan el papel fundamental de los biomarcadores. Por lo tanto, nuevos complementarios rentables Se requieren enfoques en entornos clínicos. Acercarse. Desarrollamos un marco novedoso basado en un clasificador de aprendizaje automático que aumenta el gradiente, ajustado por la optimización bayesiana, en una función múltiple enfoque multimodal (que combina imágenes demográficas, neuropsicológicas y de resonancia magnética) (IRM) y electroencefalografía/datos de conectividad de IRM funcional) para caracterizar neurodegeneración utilizando la armonización del sitio y la selección de características secuenciales. Evaluamos 54 DFTvc y 76 pacientes con EA y 152 controles sanos (HC) de un consorcio latinoamericano (ReDLat). Resultados principales. El modelo multimodal arrojó una alta clasificación de área bajo la curva (pacientes con DFTvc frente a HC: 0,93 (±0,01); pacientes con EA frente a HC: 0,95 (±0,01); DFTvv frente a EA pacientes: 0,92 (±0,01)). El enfoque de selección de características filtró con éxito información no informativa marcadores multimodales (de miles a decenas). Resultados. Probado robusto contra multimodal heterogeneidad, variabilidad sociodemográfica y datos faltantes. Significado. El modelo con precisión subtipos de demencia identificados utilizando medidas fácilmente disponibles en entornos subrepresentados, con un rendimiento similar al de los biomarcadores avanzados. Este enfoque, si se confirma y replica, puede complementar potencialmente las evaluaciones clínicas en los países en desarrollo.Q1Q1Abstract 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.https://orcid.org/0000-0001-6529-7077https://scholar.google.com/citations?hl=es&user=kaGongoAAAAJ&view_op=list_works&sortby=pubdatehttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000055000Revista Internacional - IndexadaS

The impact of regional heterogeneity in whole-brain dynamics in the presence of oscillations

Large variability exists across brain regions in health and disease, considering their cellular and molecular composition, connectivity and function. Large-scale whole-brain models comprising coupled brain regions provide insights into the underlying dynamics that shape complex patterns of spontaneous brain activity. In particular, biophysically grounded mean-field whole-brain models in the asynchronous regime were used to demonstrate the dynamical consequences of including regional variability. Nevertheless, the role of heterogeneities when brain dynamics are supporting by synchronous oscillating state, which is a ubiquitous phenomenon in brain, remains poorly understood. Here, we implemented two models capable of presenting oscillatory behaviour with different levels of abstraction: a phenomenological Stuart Landau model and an exact mean-field model. The fit of these models informed by structural-to-functional–weighted MRI signal (T1w/T2w) allowed to explore the implication of the inclusion of heterogeneities for modelling resting-state fMRI recordings from healthy participants. We found that disease-specific regional functional heterogeneity imposed dynamical consequences within the oscillatory regime in fMRI recordings from neurodegeneration with specific impacts in brain atrophy/structure (Alzheimer patients). Overall, we found that models with oscillations perform better when structural and functional regional heterogeneities are considered showing that phenomenological and biophysical models behave similarly at the brink of the Hopf bifurcation.Fil: Sanz Perl Hernandez, Yonatan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina. Universidad de San Andrés; Argentina. Universitat Pompeu Fabra; EspañaFil: Zamora Lopez, Gorka. Universitat Pompeu Fabra; EspañaFil: Montbrió, Ernest. Universitat Pompeu Fabra; EspañaFil: Monge Asensio, Martí. Universitat Pompeu Fabra; EspañaFil: Vohryzek, Jakub. Universitat Pompeu Fabra; España. University of Oxford; Reino UnidoFil: Fittipaldi, María Sol. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Trinity College; IrlandaFil: Gonzalez Campo, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; ArgentinaFil: Moguilner, Sebastian Gabriel. University of California; Estados Unidos. Trinity College; Irlanda. Universidad Adolfo Ibañez; ChileFil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Trinity College; Irlanda. Universidad Adolfo Ibañez; ChileFil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; ChileFil: Yeo, B. T. Thomas. National University of Singapore; SingapurFil: Kringelbach, Morten L.. University of Oxford; Reino Unido. University Aarhus; Dinamarca. Universidade do Minho; PortugalFil: Deco, Gustavo. Universitat Pompeu Fabra; España. Max Planck Institute for Human Cognitive and Brain Sciences; Alemania. Monash University; Australi

Telemedicine across the globe-position paper from the COVID-19 pandemic health system resilience PROGRAM (REPROGRAM) international consortium (Part 1)

Coronavirus disease 2019 (COVID-19) has accelerated the adoption of telemedicine globally. The current consortium critically examines the telemedicine frameworks, identifies gaps in its implementation and investigates the changes in telemedicine framework/s during COVID-19 across the globe. Streamlining of global public health preparedness framework that is interoperable and allow for collaboration and sharing of resources, in which telemedicine is an integral part of the public health response during outbreaks such as COVID-19, should be pursued. With adequate reinforcement, telemedicine has the potential to act as the “safety-net” of our public health response to an outbreak. Our focus on telemedicine must shift to the developing and under-developing nations, which carry a disproportionate burden of vulnerable communities who are at risk due to COVID-19

Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization

Introduction -- Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. Methods -- We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, and multi-metric EEG source space connectomics analyses. Results -- Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Discussion --Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia

The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations

Global initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (N = 23,694; M = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.Fil: Santamaria Garcia, Hernando. Pontificia Universidad Javeriana; Colombia. Hospital Universitario San Ignacio; Colombia. University of California; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Moguilner, Sebastian Gabriel. Universidad de San Andrés; Argentina. Massachusetts General Hospital; Estados Unidos. Universidad Adolfo Ibañez; ChileFil: Rodriguez Villagra, Odir Antonio. Universidad de Costa Rica; Costa RicaFil: Botero Rodriguez, Felipe. Pontificia Universidad Javeriana; ColombiaFil: Pina Escudero, Stefanie Danielle. University of California; Estados UnidosFil: O’Donovan, Gary. Universidad Adolfo Ibañez; Chile. Universidad de los Andes; ColombiaFil: Albala, Cecilia. Universidad de Chile; ChileFil: Matallana, Diana. Fundacion Santa Fe de Bogota; Colombia. Hospital Universitario San Ignacio; Colombia. Pontificia Universidad Javeriana; ColombiaFil: Schulte, Michael. Universidad Adolfo Ibañez; ChileFil: Slachevsky, Andrea. Universidad del Desarrollo; Chile. Universidad de Chile; ChileFil: Yokoyama, Jennifer S.. University of California; Estados UnidosFil: Possin, Katherine. University of California; Estados UnidosFil: Ndhlovu, Lishomwa C.. Weill Cornell Medicine; Estados UnidosFil: Al-Rousan, Tala. University of California at San Diego; Estados UnidosFil: Corley, Michael J.. Weill Cornell Medicine; Estados UnidosFil: Kosik, Kenneth. University of California; Estados UnidosFil: Muniz Terrera, Graciela. University of Edinburgh; Reino Unido. Ohio University; Estados UnidosFil: Miranda, J. Jaime. George Institute For Global Health; Australia. Cronicas Centro de Excelencia En Enfermedades Crónicas; Perú. Universidad Peruana Cayetano Heredia; PerúFil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. Trinity College Dublin; Irlanda. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of California; Estados Unidos. Universidad Adolfo Ibañez; Chil