Understanding the relationship between structural and functional organization represents one of the most important challenges in neuroscience. An increasing amount of studies show that this ...organization can be better understood by considering the brain as an interactive complex network. This approach has inspired a large number of computational models that combine experimental data with numerical simulations of brain interactions. In this paper, we present a summary of a data-driven computational model of synchronization between distant cortical areas that share a large number of overlapping neighboring (anatomical) connections. Such connections are derived from in vivo measures of brain connectivity using diffusion-weighted magnetic resonance imaging and are additionally informed by the presence of significant resting-state functionally correlated links between the areas involved. The dynamical processes of brain regions are simulated by a combination of coupled oscillator systems and a hemodynamic response model. The coupled oscillatory systems are represented by the Kuramoto phase oscillators, thus modeling phase synchrony between regional activities. The focus of this modeling approach is to characterize topological properties of functional brain correlation related to synchronization of the regional neural activity. The proposed model is able to reproduce remote synchronization between brain regions reaching reasonable agreement with the experimental functional connectivities. We show that the best agreement between model and experimental data is reached for dynamical states that exhibit a balance of synchrony and variations in synchrony providing the integration of activity between distant brain regions.
With the aim of further advancing the understanding of the human brain's functional connectivity, we propose a network metric which we term the
. This metric quantifies the Shannon entropy of the ...distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.
Abstract
The physical basis of consciousness is one of the most intriguing open questions that contemporary science aims to solve. By approaching the brain as an interactive information system, ...complex network theory has greatly contributed to understand brain process in different states of mind. We study a non-ordinary state of mind by comparing resting-state functional brain networks of individuals in two different conditions: before and after the ingestion of the psychedelic brew Ayahuasca. In order to quantify the functional, statistical symmetries between brain region connectivity, we calculate the pairwise information parity of the functional brain networks. Unlike the usual approach to quantitative network analysis that considers only local or global scales, information parity instead quantifies pairwise statistical similarities over the entire network structure. We find an increase in the average information parity on brain networks of individuals under psychedelic influences. Notably, the information parity between regions from the limbic system and frontal cortex is consistently higher for all the individuals while under the psychedelic influence. These findings suggest that the resemblance of statistical influences between pair of brain regions activities tends to increase under Ayahuasca effects. This could be interpreted as a mechanism to maintain the network functional resilience.
Navigating through the ebbs and flows of language Viol, Aline; Treves, Alessandro; Ciaramelli, Elisa
Current opinion in neurobiology,
October 2021, 2021-10-00, 20211001, Letnik:
70
Journal Article
Recenzirano
Is progress in understanding the neural basis for spatial navigation relevant to the human language faculty? Not so much at the shortest scale, where movement is continuous, a recent study in the ...space of vowels suggests. At a much larger scale, however, that of the verbalization of run-away thoughts, a rich phenomenology appears to involve critical contributions by some of the brain structures also involved in spatial cognition. Their interactions may have to be approached with models operating at an integrated cortical level and allowing for the compositionality of multiple local attractor states. A useful window on the latching dynamics enabled by cortico-cortical interactions may be offered by altered states of consciousness. As an example, psychedelic states have been reported to alter the graph properties of functional connectivity in the cortex so as to facilitate wide-ranging trips.
A growing interest in complex networks theory results in an ongoing demand for new analytical tools. We propose a novel quantity based on information theory that provides a new perspective for a ...better understanding of networked systems: Termed ”information parity”, it quantifies the consonance of influence among nodes with respect to the whole network architecture. Considering the statistics of geodesic distances, information parity assesses how similarly a pair of nodes can influence and be influenced by the network. This allows us to quantify the access of information gathered by the nodes. To demonstrate the method’s potential, we evaluate a social network and human brain networks. Our results indicate that emerging phenomena like an ideological orientation of nodes in social networks can be detected by their information parities. We also show the potential of information parity to identify central network regions in structural brain networks placed near mid-sagittal plane. We find that functional networks have, on average, greater information parity for inter-hemispheric homologous regions in comparison to the whole network. This property of information parity suggests that the functional correlations between regional activities could be explained by the symmetry of their overall influences on the whole brain.
•Information parity quantifies topological symmetries in networks.•Information parity allows assessing long-range influences in complex networks.•Information parity can shed light on behaviors of social or brain networks.
Abstract Functional magnetic resonance imaging (fMRI) has just completed 20 years of existence. It currently serves as a research tool in a broad range of human brain studies in normal and ...pathological conditions, as is the case of epilepsy. To date, most fMRI studies aimed at characterizing brain activity in response to various active paradigms. More recently, a number of strategies have been used to characterize the low-frequency oscillations of the ongoing fMRI signals when individuals are at rest. These datasets have been largely analyzed in the context of functional connectivity, which inspects the covariance of fMRI signals from different areas of the brain. In addition, resting state fMRI is progressively being used to evaluate complex network features of the brain. These strategies have been applied to a number of different problems in neuroscience, which include diseases such as Alzheimer's, schizophrenia, and epilepsy. Hence, we herein aimed at introducing the subject of complex network and how to use it for the analysis of fMRI data. This appears to be a promising strategy to be used in clinical epilepsy. Therefore, we also review the recent literature that has applied these ideas to the analysis of fMRI data in patients with epilepsy. This article is part of a Special Issue entitled “NEWroscience 2013”.
A growing interest in complex networks theory results in an ongoing demand for new analytical tools. We propose a novel measure based on information theory that provides a new perspective for a ...better understanding of networked systems: Termed "information parity," it quantifies the consonance of influence among nodes with respect to the whole network architecture. Considering the statistics of geodesic distances, information parity detects how similar a pair of nodes can influence and be influenced by the network. This allows us to quantify the quality of information gathered by the nodes. To demonstrate the method's potential, we evaluate a social network and human brain networks. Our results indicate that emerging phenomena like an ideological orientation of nodes in a social network is severely influenced by their information parities. We also show that anatomical brain networks have a greater information parity in inter-hemispheric correspondent regions placed near the sagittal plane. Finally, functional networks have, on average, greater information parity for inter-hemispheric correspondent regions in comparison to the whole network. We find that a pair of regions with high information parity exhibits higher correlation, suggesting that the functional correlations between cortical regions can be partially explained by the symmetry of their overall influences of the whole brain.
The physical basis of consciousness is one of the most intriguing open questions that contemporary science aims to solve. By approaching the brain as an interactive information system, complex ...network theory has greatly contributed to understand brain process in different states of mind. We study an non-ordinary state of mind by comparing resting-state functional brain networks of individuals in two different conditions: before and after the ingestion of the psychedelic brew Ayahuasca. In order to quantify the functional, statistical symmetries between brain region connectivity, we calculate the pairwise information parity of the functional brain networks. Unlike most of usual network quantification, which is done on a local or global scale, information parity quantifies the pairwise statistical similarities considering the entire network structure. We detect an increase in the average information parity on brain networks of individuals under psychedelic influences. Notably, the information parity between regions from the limbic system and frontal cortex is consistently higher for all the individuals while under the psychedelic influence.
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The characterization of fractals and the search for general rules and mechanisms for description of non-linear phenomena brought the attention of the scientific community to the interdisciplinary area of complex systems. Complex phenomena can be found in almost every branch of natural science. One example is the simulation using cellular automata of self-organization in life. This work focuses on cellular automata models to simulate the growth of branched structures in animal body guided by the characteristics of interactions in cell migration. Our models are guided by the dynamics of angiogenesis and neurogenesis, apparently two different mechanisms that can be classified under the same rules regarding local interactions and emergent behaviors. The proposed models can be very useful in studies of neurological diseases and cancer therapies.
A caracterização de estruturas fractais e a busca por mecanismos ou regras universais para descrição de diversos fenômenos não-linares fez com que a comunidade científica se voltasse para uma área que pode ser considerada uma das pioneiras na interdiciplinaridade: os Sistemas Complexos. Praticamente toda disciplina possui em seu domínio fenômenos complexos. Uma das linha de destaque dessa área são as simulações em autômatos celulares, principalmente quando voltada para estudos de auto-organização em seres vivos. O presente trabalho propõe uma modelagem em autômatos celulares para simulação de crescimento de estruturas ramificadas no organismo animal guiadas pelas interações características da migração celular. Nosso modelo foi baseado na angiogênese e na neurôgenese, estruturas que são aparentemente distintas, mas que na verdade pertencem a um mesmo grupo se classificados quanto às regras de interações locais e seus comportamentos emergentes. Esse modelo pode ser de grande utilidade no estudo de doenças relacionadas ao sistema nervoso e de terapias de combate ao câncer.
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O recente desenvolvimento do poder de processamento computacional vem trazendo para dentro do escopo da f?sica e outras ci?ncia exatas desafios at? ent?o considerados exclusivos de ci?ncias qualitativas. M?todos e conceitos da F?sica t?m contribu?do para avan?os em diversas ?reas, dentre elas neuroci?ncia. Vimos na presente tese de doutorado estudar o comportamento do c?rebro humano em estado alterado de consci?ncia, a partir de mapas funcionais gerados por resson?ncia magn?tica funcional (fMRI, {\it functional Magnetic Ressonance}) usando ferramentas da f?sica estat?stica e da teoria de redes complexas. Analisamos dados de fMRI do c?rebro de sujeitos em estado de repouso em duas condi??es distintas: em estado natural e em estado alterado de consci?ncia pela ingest?o de uma infus?o psicoativa, proveniente da cultura ind?gena amaz?nica, chamada Ayahuasca. Em linhas gerais fomos guiados por duas perguntas. O Ayahuasca causa diferen?as nas redes funcionais do c?rebro? Como quantificar essas diferen?as? Inicialmente constru?mos redes complexas usando os dados de fMRI para mapear informa??es das redes funcionais do c?rebro de cada sujeito em ambas condi??es. A seguir analisamos as propriedades estat?sticas e topol?gicas dessas redes. Comparando as redes geradas a partir dos dados adquiridos antes e depois da ingest?o do Ayahuasca, detectamos duas mudan?as importantes nas propriedades estat?sticas e topol?gicas. Encontramos primeiramente um aumento na entropia de Shannon da distribui??o de grau. Encontramos tamb?m uma segunda mudan?a importante: Uma varia??o na topologia que interfere nas efici?ncias das redes referentes ao estado alterado. Parte das mudan?as nas efici?ncias de rede v?o al?m do que pode ser explicado exclusivamente pelo aumento de entropia. Interpretamos e discutimos esses dois resultados no contexto de neuroci?ncia. Comentamos tamb?m sobre a como a quantifica??o de informa??es das redes funcionais pode ser melhorar nossa compreens?o do funcionamento do c?rebro humano e, consequentemente, contribuir para o desenvolvimento de novas metodologias em diagn?stico e tratamento de dist?rbios psiqui?tricos ainda pouco compreendidos.
An important problem in nonlinear dynamics and statistical physics relates to the;
quantitative description of the behavior of complex systems. The human brain is one such;
system. Methods and concepts used in physics have contributed to the development of diverse;
fields, including neuroscience. In this thesis, we investigate the behavior of the human brain;
in altered states of consciousness. We study the functional maps of the brain generated by;
functional magnetic resonance imaging (fMRI), using the tools of statistical physics and the;
theory of complex networks. We analyze resting state fMRI data of the brains of 9 human;
subjects under two distinct conditions: under normal waking state and in an altered state;
of consciousness, induced by ingestion of the psychoactive infusion known as Ayahuasca, of;
Amazonian indigenous origin. Our study was broadly motivated by two questions: Does;
Ayahuasca affect the functional brain networks? How can we quantify these effects? We;
initially constructed complex network models of the brain using the fMRI data, before and;
after ingestion of Ayahuasca. We next analyzed the statistical and topological properties of;
these networks. Comparing the networks generated from the data before and after Ayahuasca;
ingestion, we find some significant changes which we highlight: an increase in the Shannon;
entropy, a increase in the mean geodesic distance and changes in network efficiencies. The;
increase in mean distance indicates a global expansion of the brain networks. This suggests;
a decrease in global integration of brain regions. Moreover, the increase in the entropy of the;
degree distribution suggests an increase in the range of possibilities of functional patterns.;
The change in the network efficiencies goes beyond what can be accounted for by the changes;
in degree distribution. We discuss and present potential interpretations of our results in the;
context of neuroscience.
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