1,721,016 research outputs found
Brain clocks capture diversity and disparity in aging and dementia
Get PDFFil: 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
Get PDFCharacterizing 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
Get PDFCharacterizing 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Functional connectivity analysis during processing of grammatical violations of natural and artificial language: evidence for shared mechanisms.
Get PDFLa 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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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