1,721,470 research outputs found
Reconstruction of neural activity from M/EEG non-stationary data using time varying spatiotemporal constraints
Get PDFMagneto-Electroencephalogram(M/EEG)-based neuroimaging is a widely used technique that allows to non invasively explore brain activity. One of the most prominent advantages of using M/EEG measures to analyze brain activity is its outstanding temporal resolution. However, spatial measurement points (electrodes) are relatively low -a couple hundreds in the best case-, while the discretized brain activity generators -termed current dipoles or sources- are several thousands. This leads to a heavily ill-posed mathematical problem commonly known as the M/EEG inverse problem. To solve such problems, additional information must be a-priori assumed in order to obtain an unique and optimal solution. In the present work, several approaches to improve the accuracy and interpretability of the inverse problem solution are proposed, using physiologically motivated assumptions. Firstly, a method that infers neural states from the M/EEG recordings to dynamically constraint the M/EEG inverse problem is proposed, relaxing the brain activity stationarity assumption that is usually made in state-of-art algorithms. This is done by assuming a physiologically motivated time-varying a-priori covariance matrix. Secondly, a realistic time varying autoregressive model is proposed, aiming to explicitly constraining temporal evolution of brain activity. Finally, a novel source connectivity analysis method is proposed by taking advantage of the temporal dynamics provided by the M/EEG recordings. The proposed methods are compared with classic and state-of-art techniques in a simulated environment, and afterwards, are validated using real world data. In general, the contributed approaches are efficient and competitive compared to state-of-art brain mapping and source connectivity methodsResumen : El mapeo cerebral basado en señales de magneto/electroencefalografía (M/EEG), es una técnica muy usada para explorar la actividad cerebral de forma no invasiva. Una de las ventajas que provee la utilización de señales EEG para analizar la actividad cerebral es su bajo costo y su sobresaliente resolución temporal. Sin embargo la cantidad de puntos de medición (electrodos) es extremadamente baja comparada con la cantidad de puntos discretizados dentro del cerebro sobre los cuales se debe realizar la estimación de la actividad. Esto conlleva a un problema mal condicionado comúnmente conocido como el problema inverso de M/EEG. Para resolver este tipo de problemas, información apriori debe ser supuesta para así obtener una solución única y óptima. En el presente trabajo investigativo, se proponen distintas aproximaciones a la solución del problema con el objetivo de mejorar la precisión e interpretabilidad de las estimaciones de actividad cerebral. En primer lugar se propone un método que infiere estados neuronales a partir de los registros M/EEG para restringir dinámicamente el problema inverso de M/EEG relajando la asunción de estacionariedad hecha en los algoritmos del estado del arte. Esto se logra a través de la creación de matrices de covarianza variantes en el tiempo que permiten adaptarse a los cambios espacio temporales de la dinámica cerebral. En segundo lugar, un modelo autorregresivo variante en el tiempo con restricciones espacio-temporales basadas en modelos fisiológicos es propuesto, con el fin de restringir la evolución temporal de la actividad cerebral. Finalmente, un método novedoso de análisis de conectividad en fuentes es propuesto, incluyendo explícitamente las dinámicas temporales de los registros M/EEG. Los métodos propuestos se comparan con técnicas del estado del arte en ambientes de simulación, y también se validan en escenarios reales. En general, los métodos propuestos son eficientes y competitivos comparados con los métodos de comparaciónDoctorad
Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints
Get PDFMagneto/Electroencephalography (M/EEG)-based neuroimaging is a widely used noninvasive technique for functional analysis of neuronal activity. One of the most prominent advantages of using M/EEG measures is the very low implementation cost and its height temporal resolution. However, the number of locations measuring magnetic/electrical is relatively small (a couple of hundreds at best) while the discretized brain activity generators (sources) are several thousand. This fact corresponds an ill-posed mathematical problem commonly known as the M/EEG inverse problem. To solve such problems, additional information must be apriori assumed to obtain a unique and optimal solution. In the present work, a methodology to improve the accuracy and interpretability of the inverse problem solution is proposed, using physiologically motivated assumptions. Firstly, a method constraining the solution to a sparse representation in the space-time domain is introduce given a set of methodologies to syntonize the present parameters. Secondly, we propose a new source connectivity approach explicitly including spatiotemporal information of the neural activity extracted from M/EEG recordings. The proposed methods are compared with the state-of-art techniques in a simulated environment, and afterward, are validated using real-world data. In general, the contributed approaches are efficient and competitive compared to state-of-art brain mapping methodsResumen : El mapeo cerebral basado en señales de magneto/electroencefalografía (M/EEG), es una técnica muy usada para el análisis de la actividad neuronal en forma no invasiva. Una de las ventajas que provee la utilización de señales M/EEG es su bajo costo de implementación además de su sobresaliente resolución temporal. Sin embargo el número de posiciones magnéticas/eléctricas medidas son extremadamente bajas comparadas con la cantidad de puntos discretizados dentro del cerebro sobre los cuales se debe realizar la estimación de la actividad. Esto conlleva a un problema mal condicionado comúnmente conocido como el problema inverso de M/EEG. Para resolver este tipo de problemas, información apriori debe ser supuesta para así obtener una solución única y óptima. En el presente trabajo investigativo, se propone una metodología para mejorar la exactitud e interpretación a la solución del problema inverso teniendo en cuenta el contexto fisiológico del problema. En primer lugar se propone un algoritmo en el cual se representa la actividad cerebral a través de un conjunto de funciones espacio-temporales dando metodologías para sintonizar los parámetros presentes. En segundo lugar, proponemos un nuevo enfoque mediante conectividad en fuentes que explícitamente incluye información espacial y temporal de la actividad neuronal extraída del M/EEG. Los métodos propuestos son comparados con métodos del estado del arte usando señales simuladas, y finalmente son validados usando datos reales de M/EEG. En general, los métodos propuestos son eficientes y competitivos en comparación a los métodos de referenciaMaestrí
The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease
No full textCurrent biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease. However, in spite of its added value they are not part of the standard of care in clinical practice in dementia. In this paper we review what these neurophysiological techniques can add to the early diagnosis of AD, whether results in both modalities are related to each other or not, as well as the need of its validation against current biomarkers. We discuss their potential implications for the better understanding of the pathophysiological mechanisms of the disease as well as the need of performing simultaneous M/EEG recordings to better understand discrepancies between these two techniques. Finally, more studies are needed studying M/EEG with amyloid and Tau biomarkers.Depto. de Psicología Experimental, Procesos Cognitivos y LogopediaFac. de PsicologíaTRUEpu
Dictionary learning for M/EEG multidimensional data
Get PDFInternational audienceSignals obtained from magneto- or electroencephalography (M/EEG) are very noisy and inherently multi-dimensional, i.e. provide a vector of measurements at each single time instant. To cope with noise, researchers traditionally acquire measurements over multiple repetitions (trials) and average them to classify various patterns of activity. This is not optimal because of trial-to-trial variability (waveform variation, jitters). The jitter-adaptivedictionary learning method (JADL) has been developed to better handle for this variability (with a particular emphasis on jitters). JADL is a data-driven method that learns a dictionary (prototype pieces) from a set of signals, but is currently limited to a single channel, which restricts its capacity to work with very noisy data such as M/EEG. We propose an extension to the jitter-adaptive dictionary learning method, that is able to handle multidimensional measurements such as M/EEG
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
Connectivity-informed M/EEG inverse problem
Get PDFInternational audienceInformation between brain regions is transferred through white matter fibers with delays that are measurable with magnetoencephalography and electroencephalography (M/EEG) due to its millisecond temporal resolution. Therefore, a useful representation of the brain is that of a graph where its nodes are the cortical areas and edges are the physical connections between them: either local (between adjacent vertices on the cortical mesh) or non-local (long-range white matter fibers). These long-range anatomical connections can be obtained by diffusion MRI (dMRI) tractography, thus giving us an insight on interaction delays of the macroscopic brain network. A fundamental role in shaping the rich temporal structure of functional connectivity is played by the structural connectivity [6] that places constraints on which functional interactions occur in the network. In the context of regularizing the dynamics of M/EEG and recovering electrical activity of the brain from M/EEG measurements, traditional linear inverse methods deploy different constraints such as minimum norm, maximum-smoothness in space and/or time along the cortical surface. However, they usually do not take into account the structural connectivity and very few include delays supported by dMRI as a prior information [1]. The goal of this work is to include these delays into the MEG source reconstruction process by imposing temporal smoothness in structurally connected sources, with the corresponding delays. We propose to encapsulate delays provided by dMRI in a graph representation and show their potential in improving the MEG source reconstruction when compared to a state-of-the-art approach [4]
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
M/EEG analysis of naturalistic stories: a review from speech to language processing
Get PDFM/EEG research using naturally spoken stories as stimuli has focused largely on speech and not language processing. The temporal resolution of M/EEG is a two-edged sword, allowing for the study of the fine acoustic structure of speech, yet easily overwhelmed by the temporal noise of variation in constituent length. Recent theories on the neural encoding of linguistic structure require the temporal resolution of M/EEG, yet suffer from confounds when studied on traditional, heavily controlled stimuli. Recent methodological advances allow for synthesising naturalistic designs and traditional, controlled designs into effective M/EEG research on naturalistic language. In this review, we highlight common threads throughout the at-times distinct research traditions of speech and language processing. We conclude by examining the tradeoffs and successes of three M/EEG studies on fully naturalistic language paradigms and the future directions they suggest
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
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