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Analyse multifractale pour l'étude de la criticité dans la dynamique neuronale
No full textThe brain criticality hypothesis suggests that the brain operates near a critical point of a phase transition between order and disorder.Scale invariance, where the properties of an object do not depend upon the scale at which it is considered, is the hallmark of critical systems and is evident in the brain both spatially and temporally, as in the case of the dynamics of observed brain oscillations.Multifractal analysis extends the characterization of scale invariance beyond the second-order properties of time series, thereby providing a measure of their temporal fluctuations in pointwise regularity.So far, multifractal analysis has received very limited attention from the neuroscientific community. This is due to its increased complexity compared to traditional approaches and the absence of a neurophysiologically grounded explanation of the results.In particular, no explicit connection has been established between the brain criticality hypothesis and multifractality in the brain.This doctoral thesis aims to bridge the gap between our understanding of brain criticality and the multifractality observed in brain dynamics.The significance of this body of work lies in its two key contributions: It is the first systematic investigation of temporal multifractality in a model for critical brain dynamics, and second, it provides the first proper characterization of multifractality in brain oscillatory dynamics at rest, using a large magnetoencephalography (MEG) dataset.The first part of this thesis introduces the fundamental constructs of brain criticality and multifractality and provides the necessary background to appreciate the significance of the three articles that make up the second section of this dissertation.The first article presents multifractality in the temporal dynamics of simulations of a model of brain criticality.The second article addresses a methodological issue in multifractal analysis by introducing an algorithm that handles outliers, which has so far been neglected when applying multifractal analysis to electrophysiological recordings of brain activity.The third article applies multifractal analysis to oscillatory brain dynamics recorded using MEG, uncovering the multifractal properties of neural oscillations for the first time.This body of work advances our understanding of how multifractality may arise within the context of critical brain dynamics, offering the possibility to both better understand the results of multifractal analysis and better characterize the scale invariance found in oscillatory dynamics.L'hypothèse de la criticité cérébrale suggère que le cerveau fonctionne près d'un point critique d'une transition de phase entre l'ordre et le désordre. L'invariance d'échelle, selon laquelle les propriétés d'un objet ne dépendent pas de l'échelle à laquelle il est considéré, est la marque des systèmes critiques et se manifeste dans le cerveau tant sur le plan spatial que temporel, comme c'est le cas pour la dynamique des oscillations cérébrales observées.L'analyse multifractale étend la caractérisation de l'invariance d'échelle au-delà des propriétés de second ordre des séries temporelles, fournissant ainsi une mesure des fluctuations temporelles de leur régularité ponctuelle. Jusqu'à présent, l'analyse multifractale a reçu très peu d'attention de la part de la communauté neuroscientifique. Cela s'explique par sa complexité accrue par rapport aux approches traditionnelles et par l'absence d'une explication neurophysiologique des résultats. En particulier, aucun lien explicite n'a été établi entre l'hypothèse de la criticité cérébrale et la multifractalité dans le cerveau.Cette thèse de doctorat vise à combler l'écart entre notre compréhension de la criticité cérébrale et la multifractalité observée dans la dynamique cérébrale. L'importance de ce travail réside dans ses deux contributions clés : d'une part, il s'agit de la première étude systématique de la multifractalité temporelle dans un modèle de dynamique cérébrale critique et, d'autre part, il offre la première caractérisation adéquate de la multifractalité dans la dynamique oscillatoire cérébrale au repos, en utilisant un vaste ensemble de données de magnétoencéphalographie (MEG).La première partie de cette thèse présente les concepts fondamentaux de la criticité cérébrale et de la multifractalité, et fournit le contexte nécessaire pour apprécier l'importance des trois articles qui composent la deuxième section de cette dissertation. Le premier article présente la multifractalité dans la dynamique temporelle issue des simulations d'un modèle de criticité cérébrale. Le deuxième article aborde une problématique méthodologique dans l'analyse multifractale en introduisant un algorithme qui gère les valeurs aberrantes, jusqu'à présent négligées lors de l'application de l'analyse multifractale aux enregistrements électrophysiologiques de l'activité cérébrale. Le troisième article applique l'analyse multifractale aux dynamiques oscillatoires cérébrales enregistrées par MEG, révélant pour la première fois les propriétés multifractales de la dynamique temporelle des oscillations neuronales.Ce travail fait progresser notre compréhension de la manière dont la multifractalité peut émerger dans le cadre des dynamiques cérébrales critiques, offrant ainsi la possibilité de mieux interpréter les résultats de l'analyse multifractale et de mieux caractériser l'invariance d'échelle présente dans la dynamique des oscillations cérébrales
Multifractal analysis for studying criticality in neural dynamics
No full textThe brain criticality hypothesis suggests that the brain operates near a critical point of a phase transition between order and disorder. Scale invariance, where the properties of an object do not depend upon the scale at which it is considered, is the hallmark of critical systems and is evident in the brain both spatially and temporally, as in the case of the dynamics of observed brain oscillations.
Multifractal analysis extends the characterization of scale invariance beyond the second-order properties of time series, thereby providing a measure of their temporal fluctuations in pointwise regularity. So far, multifractal analysis has received very limited attention from the neuroscientific community. This is due to its increased complexity compared to traditional approaches and the absence of a neurophysiologically grounded explanation of the results. In particular, no explicit connection has been established between the brain criticality hypothesis and multifractality in the brain.
This doctoral thesis aims to bridge the gap between our understanding of brain criticality and the multifractality observed in brain dynamics. The significance of this body of work lies in its two key contributions: It is the first systematic investigation of temporal multifractality in a model for critical brain dynamics, and second, it provides the first proper characterization of multifractality in brain oscillatory dynamics at rest, using a large magnetoencephalography (MEG) dataset.
The first part of this thesis introduces the fundamental constructs of brain criticality and multifractality and provides the necessary background to appreciate the significance of the three articles that make up the second section of this dissertation. The first article presents multifractality in the temporal dynamics of simulations of a model of brain criticality. The second article addresses a methodological issue in multifractal analysis by introducing an algorithm that handles outliers, which has so far been neglected when applying multifractal analysis to electrophysiological recordings of brain activity. The third article applies multifractal analysis to oscillatory brain dynamics recorded using MEG, uncovering the multifractal properties of neural oscillations for the first time.
This body of work advances our understanding of how multifractality may arise within the context of critical brain dynamics, offering the possibility to both better understand the results of multifractal analysis and better characterize the scale invariance found in oscillatory dynamics
Analyse multifractale pour l'étude de la criticité dans la dynamique neuronale
No full textThe brain criticality hypothesis suggests that the brain operates near a critical point of a phase transition between order and disorder.Scale invariance, where the properties of an object do not depend upon the scale at which it is considered, is the hallmark of critical systems and is evident in the brain both spatially and temporally, as in the case of the dynamics of observed brain oscillations.Multifractal analysis extends the characterization of scale invariance beyond the second-order properties of time series, thereby providing a measure of their temporal fluctuations in pointwise regularity.So far, multifractal analysis has received very limited attention from the neuroscientific community. This is due to its increased complexity compared to traditional approaches and the absence of a neurophysiologically grounded explanation of the results.In particular, no explicit connection has been established between the brain criticality hypothesis and multifractality in the brain.This doctoral thesis aims to bridge the gap between our understanding of brain criticality and the multifractality observed in brain dynamics.The significance of this body of work lies in its two key contributions: It is the first systematic investigation of temporal multifractality in a model for critical brain dynamics, and second, it provides the first proper characterization of multifractality in brain oscillatory dynamics at rest, using a large magnetoencephalography (MEG) dataset.The first part of this thesis introduces the fundamental constructs of brain criticality and multifractality and provides the necessary background to appreciate the significance of the three articles that make up the second section of this dissertation.The first article presents multifractality in the temporal dynamics of simulations of a model of brain criticality.The second article addresses a methodological issue in multifractal analysis by introducing an algorithm that handles outliers, which has so far been neglected when applying multifractal analysis to electrophysiological recordings of brain activity.The third article applies multifractal analysis to oscillatory brain dynamics recorded using MEG, uncovering the multifractal properties of neural oscillations for the first time.This body of work advances our understanding of how multifractality may arise within the context of critical brain dynamics, offering the possibility to both better understand the results of multifractal analysis and better characterize the scale invariance found in oscillatory dynamics.L'hypothèse de la criticité cérébrale suggère que le cerveau fonctionne près d'un point critique d'une transition de phase entre l'ordre et le désordre. L'invariance d'échelle, selon laquelle les propriétés d'un objet ne dépendent pas de l'échelle à laquelle il est considéré, est la marque des systèmes critiques et se manifeste dans le cerveau tant sur le plan spatial que temporel, comme c'est le cas pour la dynamique des oscillations cérébrales observées.L'analyse multifractale étend la caractérisation de l'invariance d'échelle au-delà des propriétés de second ordre des séries temporelles, fournissant ainsi une mesure des fluctuations temporelles de leur régularité ponctuelle. Jusqu'à présent, l'analyse multifractale a reçu très peu d'attention de la part de la communauté neuroscientifique. Cela s'explique par sa complexité accrue par rapport aux approches traditionnelles et par l'absence d'une explication neurophysiologique des résultats. En particulier, aucun lien explicite n'a été établi entre l'hypothèse de la criticité cérébrale et la multifractalité dans le cerveau.Cette thèse de doctorat vise à combler l'écart entre notre compréhension de la criticité cérébrale et la multifractalité observée dans la dynamique cérébrale. L'importance de ce travail réside dans ses deux contributions clés : d'une part, il s'agit de la première étude systématique de la multifractalité temporelle dans un modèle de dynamique cérébrale critique et, d'autre part, il offre la première caractérisation adéquate de la multifractalité dans la dynamique oscillatoire cérébrale au repos, en utilisant un vaste ensemble de données de magnétoencéphalographie (MEG).La première partie de cette thèse présente les concepts fondamentaux de la criticité cérébrale et de la multifractalité, et fournit le contexte nécessaire pour apprécier l'importance des trois articles qui composent la deuxième section de cette dissertation. Le premier article présente la multifractalité dans la dynamique temporelle issue des simulations d'un modèle de criticité cérébrale. Le deuxième article aborde une problématique méthodologique dans l'analyse multifractale en introduisant un algorithme qui gère les valeurs aberrantes, jusqu'à présent négligées lors de l'application de l'analyse multifractale aux enregistrements électrophysiologiques de l'activité cérébrale. Le troisième article applique l'analyse multifractale aux dynamiques oscillatoires cérébrales enregistrées par MEG, révélant pour la première fois les propriétés multifractales de la dynamique temporelle des oscillations neuronales.Ce travail fait progresser notre compréhension de la manière dont la multifractalité peut émerger dans le cadre des dynamiques cérébrales critiques, offrant ainsi la possibilité de mieux interpréter les résultats de l'analyse multifractale et de mieux caractériser l'invariance d'échelle présente dans la dynamique des oscillations cérébrales
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
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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