This page has only limited features, please log in for full access.
The brain is a biophysical system subject to information flows that may be thought of as a many-body architecture with a spatiotemporal dynamics described by its neuronal structures. The oscillatory nature of brain activity allows these structures (nodes) to be described as a set of coupled oscillators forming a network where the node dynamics and that of the network topology can be studied. Quantifying its dynamics at various scales is an issue that claims to be explored for several brain activities, e.g., activity at rest. The resting-state (RS) associates the underlying brain dynamics of healthy subjects that are not actively compromised with sensory or cognitive processes. Studying its dynamics is highly non-trivial but opens the door to understand the general principles of brain functioning, as well as to contrast a passive null condition vs the dynamics of pathologies or non-resting activities. Here, we hypothesize about how the spatiotemporal dynamics of cortical fluctuations could be for healthy subjects at RS. To do that, we retrieve the alphabet that reconstructs the dynamics (entropy–complexity) of magnetoencephalography (MEG) signals. We assemble the cortical connectivity to elicit the dynamics in the network topology. We depict an order relation between entropy and complexity for frequency bands that is ubiquitous for different temporal scales. We unveiled that the posterior cortex conglomerates nodes with both stronger dynamics and high clustering for α band. The existence of an order relation between dynamic properties suggests an emergent phenomenon characteristic of each band. Interestingly, we find the posterior cortex as a domain of dual character that plays a cardinal role in both the dynamics and structure regarding the activity at rest. To the best of our knowledge, this is the first study with MEG involving information theory and network science to better understand the dynamics and structure of brain activity at rest for different bands and scales.
J. Mendoza-Ruiz; C. E. Alonso-Malaver; M. Valderrama; O. A. Rosso; J. H. Martinez. Dynamics in cortical activity revealed by resting-state MEG rhythms. Chaos: An Interdisciplinary Journal of Nonlinear Science 2020, 30, 123138 .
AMA StyleJ. Mendoza-Ruiz, C. E. Alonso-Malaver, M. Valderrama, O. A. Rosso, J. H. Martinez. Dynamics in cortical activity revealed by resting-state MEG rhythms. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2020; 30 (12):123138.
Chicago/Turabian StyleJ. Mendoza-Ruiz; C. E. Alonso-Malaver; M. Valderrama; O. A. Rosso; J. H. Martinez. 2020. "Dynamics in cortical activity revealed by resting-state MEG rhythms." Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 12: 123138.
We investigated the particular organization of Guardiola’s F.C. Barcelona during season 2009/2010, using datasets from the Spanish National League La Liga. Specifically, we constructed the corresponding pitch networks, obtained from all passes successfully performed by a team during a football match. Pitch networks are composed of nodes consisting of particular subdivisions of the field, which are connected through links whose weight ωi,j corresponds to the number of passes made from region i to region j. We performed a multi-scale analysis focused on evaluating the properties of pitch networks at different scales, from a partition of the pitch into 2 × 2 to 10 × 10 areas. For each scale, we calculated a diversity of network parameters of F.C. Barcelona and its opponents during the whole season. Next, we compared the properties of F.C. Barcelona pitch networks with the networks of its rivals. Our results show how, depending on the spatial scale, there are statistically significant differences between F.C. Barcelona and the rest of the teams of the Spanish league. These differences are particularly significant at the clustering coefficient, the network average shortest-path, and the number of nodes occupied by a team for partitions with a high number of subdivisions.
J.L. Herrera-Diestra; I. Echegoyen; J.H. Martínez; D. Garrido; J. Busquets; F.Seirul. Io; J.M. Buldú. Pitch networks reveal organizational and spatial patterns of Guardiola’s F.C. Barcelona. Chaos, Solitons & Fractals 2020, 138, 109934 .
AMA StyleJ.L. Herrera-Diestra, I. Echegoyen, J.H. Martínez, D. Garrido, J. Busquets, F.Seirul. Io, J.M. Buldú. Pitch networks reveal organizational and spatial patterns of Guardiola’s F.C. Barcelona. Chaos, Solitons & Fractals. 2020; 138 ():109934.
Chicago/Turabian StyleJ.L. Herrera-Diestra; I. Echegoyen; J.H. Martínez; D. Garrido; J. Busquets; F.Seirul. Io; J.M. Buldú. 2020. "Pitch networks reveal organizational and spatial patterns of Guardiola’s F.C. Barcelona." Chaos, Solitons & Fractals 138, no. : 109934.
We introduce a new methodology, which is based on the construction of epidemic networks, to analyse the evolution of epidemic time series. First, we translate the time series into ordinal patterns containing information about local fluctuations in disease prevalence. Each pattern is associated with a node of a network, whose (directed) connections arise from consecutive appearances in the series. The analysis of the network structure and the role of each pattern allows them to be classified according to the enhancement of entropy/complexity along the series, giving a different point of view about the evolution of a given disease.
José L. Herrera-Diestra; Javier M. Buldú; Mario Chavez; Johann H. Martínez. Using symbolic networks to analyse dynamical properties of disease outbreaks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 2020, 476, 20190777 .
AMA StyleJosé L. Herrera-Diestra, Javier M. Buldú, Mario Chavez, Johann H. Martínez. Using symbolic networks to analyse dynamical properties of disease outbreaks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2020; 476 (2236):20190777.
Chicago/Turabian StyleJosé L. Herrera-Diestra; Javier M. Buldú; Mario Chavez; Johann H. Martínez. 2020. "Using symbolic networks to analyse dynamical properties of disease outbreaks." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2236: 20190777.
We quantified the spatial and temporal entropy related to football teams and their players by means of a pass-based interaction. First, we calculated the spatial entropy associated to the positions of all passes made by a football team during a match, obtaining a spatial entropy ranking of Spanish teams during the 2017/2018 season. Second, we investigated how the player’s average location in the field is related to the amount of entropy of his passes. Next, we constructed the temporal passing networks of each team and computed the deviation of their network parameters along the match. For each network parameter, we obtained the permutation entropy and the statistical complexity of its temporal fluctuations. Finally, we investigated how the permutation entropy (and statistical complexity) of the network parameters was related to the total number of passes made by a football team. Our results show that (i) spatial entropy changes according to the position of players in the field, and (ii) the organization of passing networks change during a match and its evolution can be captured measuring the permutation entropy and statistical complexity of the network parameters, allowing to identify what parameters evolve more randomly.
Johann H. Martínez; David Garrido; José L. Herrera-Diestra; Javier Busquets; Ricardo Sevilla-Escoboza; Javier M. Buldú. Spatial and Temporal Entropies in the Spanish Football League: A Network Science Perspective. Entropy 2020, 22, 172 .
AMA StyleJohann H. Martínez, David Garrido, José L. Herrera-Diestra, Javier Busquets, Ricardo Sevilla-Escoboza, Javier M. Buldú. Spatial and Temporal Entropies in the Spanish Football League: A Network Science Perspective. Entropy. 2020; 22 (2):172.
Chicago/Turabian StyleJohann H. Martínez; David Garrido; José L. Herrera-Diestra; Javier Busquets; Ricardo Sevilla-Escoboza; Javier M. Buldú. 2020. "Spatial and Temporal Entropies in the Spanish Football League: A Network Science Perspective." Entropy 22, no. 2: 172.
We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer’s Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.
Ignacio Echegoyen; David López-Sanz; Johann H. Martínez; Fernando Maestú; Javier M. Buldú. Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands. Entropy 2020, 22, 116 .
AMA StyleIgnacio Echegoyen, David López-Sanz, Johann H. Martínez, Fernando Maestú, Javier M. Buldú. Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands. Entropy. 2020; 22 (1):116.
Chicago/Turabian StyleIgnacio Echegoyen; David López-Sanz; Johann H. Martínez; Fernando Maestú; Javier M. Buldú. 2020. "Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer’s Disease: An Analysis Based on Frequency Bands." Entropy 22, no. 1: 116.
To improve our understanding of connected systems, different tools derived from statistics, signal processing, information theory and statistical physics have been developed in the last decade. Here, we will focus on the graph comparison problem. Although different estimates exist to quantify how different two networks are, an appropriate metric has not been proposed. Within this framework we compare the performances of two networks distances (a topological descriptor and a kernel-based approach as representative methods of the main classes considered) with the simple Euclidean metric. We study the performance of metrics as the efficiency of distinguish two network's groups and the computing time. We evaluate these methods on synthetic and real-world networks (brain connectomes and social networks), and we show that the Euclidean distance efficiently captures networks differences in comparison to other proposals. We conclude that the operational use of complicated methods can be justified only by showing that they outperform well-understood traditional statistics, such as Euclidean metrics.
Johann H. Martínez; Mario Chavez. Comparing complex networks: in defence of the simple. New Journal of Physics 2019, 21, 013033 .
AMA StyleJohann H. Martínez, Mario Chavez. Comparing complex networks: in defence of the simple. New Journal of Physics. 2019; 21 (1):013033.
Chicago/Turabian StyleJohann H. Martínez; Mario Chavez. 2019. "Comparing complex networks: in defence of the simple." New Journal of Physics 21, no. 1: 013033.
We introduce Ordinal Synchronization (OS) as a new measure to quantify synchronization between dynamical systems. OS is calculated from the extraction of the ordinal patterns related to two time series, their transformation into D-dimensional ordinal vectors and the adequate quantification of their alignment. OS provides a fast and robust-to noise tool to assess synchronization without any implicit assumption about the distribution of data sets nor their dynamical properties, capturing in-phase and anti-phase synchronization. Furthermore, varying the length of the ordinal vectors required to compute OS it is possible to detect synchronization at different time scales. We test the performance of OS with data sets coming from unidirectionally coupled electronic Lorenz oscillators and brain imaging datasets obtained from magnetoencephalographic recordings, comparing the performance of OS with other classical metrics that quantify synchronization between dynamical systems.
I. Echegoyen; V. Vera-Ávila; R. Sevilla-Escoboza; J.H. Martínez; J.M. Buldú. Ordinal synchronization: Using ordinal patterns to capture interdependencies between time series. Chaos, Solitons & Fractals 2018, 119, 8 -18.
AMA StyleI. Echegoyen, V. Vera-Ávila, R. Sevilla-Escoboza, J.H. Martínez, J.M. Buldú. Ordinal synchronization: Using ordinal patterns to capture interdependencies between time series. Chaos, Solitons & Fractals. 2018; 119 ():8-18.
Chicago/Turabian StyleI. Echegoyen; V. Vera-Ávila; R. Sevilla-Escoboza; J.H. Martínez; J.M. Buldú. 2018. "Ordinal synchronization: Using ordinal patterns to capture interdependencies between time series." Chaos, Solitons & Fractals 119, no. : 8-18.
Time irreversibility is a common signature of nonlinear processes and a fundamental property of non-equilibrium systems driven by non-conservative forces. A time series is said to be reversible if its statistical properties are invariant regardless of the direction of time. Here, we propose the Time Reversibility from Ordinal Patterns method (TiROP) to assess time-reversibility from an observed finite time series. TiROP captures the information of scalar observations in time forward as well as its time-reversed counterpart by means of ordinal patterns. The method compares both underlying information contents by quantifying its (dis)-similarity via the Jensen-Shannon divergence. The statistic is contrasted with a population of divergences coming from a set of surrogates to unveil the temporal nature and its involved time scales. We tested TiROP in different synthetic and real, linear, and non-linear time series, juxtaposed with results from the classical Ramsey’s time reversibility test. Our results depict a novel, fast-computation, and fully data-driven methodology to assess time-reversibility with no further assumptions over data. This approach adds new insights into the current non-linear analysis techniques and also could shed light on determining new physiological biomarkers of high reliability and computational efficiency.
J. H. Martínez; J. L. Herrera-Diestra; Mario Chavez. Detection of time reversibility in time series by ordinal patterns analysis. Chaos: An Interdisciplinary Journal of Nonlinear Science 2018, 28, 123111 .
AMA StyleJ. H. Martínez, J. L. Herrera-Diestra, Mario Chavez. Detection of time reversibility in time series by ordinal patterns analysis. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2018; 28 (12):123111.
Chicago/Turabian StyleJ. H. Martínez; J. L. Herrera-Diestra; Mario Chavez. 2018. "Detection of time reversibility in time series by ordinal patterns analysis." Chaos: An Interdisciplinary Journal of Nonlinear Science 28, no. 12: 123111.
Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game
Javier M. Buldú; Javier Busquets; Johann H. Martínez; Jose Herrera Diestra; Ignacio Echegoyen; Javier Galeano; Jordi Luque. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Frontiers in Psychology 2018, 9, 1900 .
AMA StyleJavier M. Buldú, Javier Busquets, Johann H. Martínez, Jose Herrera Diestra, Ignacio Echegoyen, Javier Galeano, Jordi Luque. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Frontiers in Psychology. 2018; 9 ():1900.
Chicago/Turabian StyleJavier M. Buldú; Javier Busquets; Johann H. Martínez; Jose Herrera Diestra; Ignacio Echegoyen; Javier Galeano; Jordi Luque. 2018. "Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game." Frontiers in Psychology 9, no. : 1900.
Ignacio Echegoyen; Victor Vera-Ávila; Ricardo Sevilla-Escoboza; Johann H. Martínez; Javier M. Buldú. Ordinal Synchronization: Using ordinal patterns to capture interdependencies between time series. 2018, 1 .
AMA StyleIgnacio Echegoyen, Victor Vera-Ávila, Ricardo Sevilla-Escoboza, Johann H. Martínez, Javier M. Buldú. Ordinal Synchronization: Using ordinal patterns to capture interdependencies between time series. . 2018; ():1.
Chicago/Turabian StyleIgnacio Echegoyen; Victor Vera-Ávila; Ricardo Sevilla-Escoboza; Johann H. Martínez; Javier M. Buldú. 2018. "Ordinal Synchronization: Using ordinal patterns to capture interdependencies between time series." , no. : 1.
Johann H. Martínez; José L. Herrera-Diestra; Mario Chavez. Detection of time reversibility in time series by ordinal patterns analysis. 2018, 1 .
AMA StyleJohann H. Martínez, José L. Herrera-Diestra, Mario Chavez. Detection of time reversibility in time series by ordinal patterns analysis. . 2018; ():1.
Chicago/Turabian StyleJohann H. Martínez; José L. Herrera-Diestra; Mario Chavez. 2018. "Detection of time reversibility in time series by ordinal patterns analysis." , no. : 1.
The way the topological structure goes from a decoupled state into a coupled one in multiplex networks has been widely studied by means of analytical and numerical studies, involving models of artificial networks. In general, these experiments assume uniform interconnections between layers offering, on the one hand, an analytical treatment of the structural properties of multiplex networks but, on the other hand, loosing applicability to real networks where heterogeneity of the links’ weights is an intrinsic feature. In this paper, we study 2-layer multiplex networks of musicians whose layers correspond to empirical datasets containing, and linking the information of: (i) collaboration between them and (ii) musical similarities. In our model, connections between the collaboration and similarity layers exist, but they are not ubiquitous for all nodes. Specifically, inter-layer links are created (and weighted) based on structural resemblances between the neighborhood of an artist, taking into account the level of interaction at each layer. Next, we evaluate the effect that the heterogeneity of the weights of the inter-layer links has on the structural properties of the whole network, namely the second smallest eigenvalue of the Laplacian matrix (algebraic connectivity). Our results show a transition in the value of the algebraic connectivity that is far from classical theoretical predictions where the weight of the inter-layer links is considered to be homogeneous.
Johann H. Martínez; Stefano Boccaletti; Vladimir V. Makarov; Javier M. Buldú. Multiplex networks of musical artists: The effect of heterogeneous inter-layer links. Physica A: Statistical Mechanics and its Applications 2018, 510, 671 -677.
AMA StyleJohann H. Martínez, Stefano Boccaletti, Vladimir V. Makarov, Javier M. Buldú. Multiplex networks of musical artists: The effect of heterogeneous inter-layer links. Physica A: Statistical Mechanics and its Applications. 2018; 510 ():671-677.
Chicago/Turabian StyleJohann H. Martínez; Stefano Boccaletti; Vladimir V. Makarov; Javier M. Buldú. 2018. "Multiplex networks of musical artists: The effect of heterogeneous inter-layer links." Physica A: Statistical Mechanics and its Applications 510, no. : 671-677.
We investigated how the organization of functional brain networks was related to cognitive reserve (CR) during a memory task in healthy aging. We obtained the magnetoencephalographic functional networks of 20 elders with a high or low CR level to analyse the differences at network features. We reported a negative correlation between synchronization of the whole network and CR, and observed differences both at the node and at the network level in: the average shortest path and the network outreach. Individuals with high CR required functional networks with lower links to successfully carry out the memory task. These results may indicate that those individuals with low CR level exhibited a dual pattern of compensation and network impairment, since their functioning was more energetically costly to perform the task as the high CR group. Additionally, we evaluated how the dynamical properties of the different brain regions were correlated to the network parameters obtaining that entropy was positively correlated with the strength and clustering coefficient, while complexity behaved conversely. Consequently, highly connected nodes of the functional networks showed a more stochastic and less complex signal. We consider that network approach may be a relevant tool to better understand brain functioning in aging.
Johann H. Martínez; María Eugenia López; Pedro Ariza; Mario Chavez; José A. Pineda-Pardo; David López-Sanz; Pedro Gil; Fernando Maestu; Javier M. Buldú. Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics. Scientific Reports 2018, 8, 10525 .
AMA StyleJohann H. Martínez, María Eugenia López, Pedro Ariza, Mario Chavez, José A. Pineda-Pardo, David López-Sanz, Pedro Gil, Fernando Maestu, Javier M. Buldú. Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics. Scientific Reports. 2018; 8 (1):10525.
Chicago/Turabian StyleJohann H. Martínez; María Eugenia López; Pedro Ariza; Mario Chavez; José A. Pineda-Pardo; David López-Sanz; Pedro Gil; Fernando Maestu; Javier M. Buldú. 2018. "Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics." Scientific Reports 8, no. 1: 10525.
Today the human brain can be modeled as a graph where nodes represent different regions and links stand for statistical interactions between their activities as recorded by different neuroimaging techniques. Empirical studies have lead to the hypothesis that brain functions rely on the coordination of a scattered mosaic of functionally specialized brain regions (modules or sub-networks), forming a web-like structure of coordinated assemblies (a network of networks. NoN). The study of brain dynamics would therefore benefit from an inspection of how functional sub-networks interact between them. In this paper, we model the brain as an interconnected system composed of two specific sub-networks, the left (L) and right (R) hemispheres, which compete with each other for centrality, a topological measure of importance in a networked system. Specifically, we considered functional scalp EEG networks (SEN) derived from high-density electroencephalographic (EEG) recordings and investigated how node centrality is shaped by interhemispheric connections. Our results show that the distribution of centrality strongly depends on the number of functional connections between hemispheres and the way these connections are distributed. Additionally, we investigated the consequences of node failure on hemispherical centrality, and showed how the abundance of inter-hemispheric links favors the functional balance of centrality distribution between the hemispheres.
J. H. Martínez; J. M. Buldú; D. Papo; F. De Vico Fallani; Mario Chavez. Role of inter-hemispheric connections in functional brain networks. Scientific Reports 2018, 8, 1 -10.
AMA StyleJ. H. Martínez, J. M. Buldú, D. Papo, F. De Vico Fallani, Mario Chavez. Role of inter-hemispheric connections in functional brain networks. Scientific Reports. 2018; 8 (1):1-10.
Chicago/Turabian StyleJ. H. Martínez; J. M. Buldú; D. Papo; F. De Vico Fallani; Mario Chavez. 2018. "Role of inter-hemispheric connections in functional brain networks." Scientific Reports 8, no. 1: 1-10.
The way the topological structure goes from a decoupled state into a coupled one in multiplex networks has been widely studied by means of analytical and numerical studies, involving models of artificial networks. In general, these experiments assume uniform interconnections between layers offering, on the one hand, an analytical treatment of the structural properties of multiplex networks but, on the other hand, loosing applicability to real networks where heterogeneity of the links' weights is an intrinsic feature. In this paper, we study 2-layer multiplex networks of musicians whose layers correspond to empirical datasets containing, and linking the information of: (i) collaboration between them and (ii) musical similarities. In our model, connections between the collaboration and similarity layers exist, but they are not ubiquitous for all nodes. Specifically, inter-layer links are created (and weighted) based on structural resemblances between the neighborhood of an artist, taking into account the level of interaction at each layer. Next, we evaluate the effect that the heterogeneity of the weights of the inter-layer links has on the structural properties of the whole network, namely the second smallest eigenvalue of the Laplacian matrix (algebraic connectivity). Our results show a transition in the value of the algebraic connectivity that is far from classical theoretical predictions where the weight of the inter-layer links is considered to be homogeneous.
Johann H. Martinez; Stefano Boccaletti; Vladimir V. Makarov; Javier M. Buldú. Multiplex networks of musical artists: the effect of heterogeneous inter-layer links. 2018, 1 .
AMA StyleJohann H. Martinez, Stefano Boccaletti, Vladimir V. Makarov, Javier M. Buldú. Multiplex networks of musical artists: the effect of heterogeneous inter-layer links. . 2018; ():1.
Chicago/Turabian StyleJohann H. Martinez; Stefano Boccaletti; Vladimir V. Makarov; Javier M. Buldú. 2018. "Multiplex networks of musical artists: the effect of heterogeneous inter-layer links." , no. : 1.
Beware of the Small-World Neuroscientist!
David Epapo; Massimiliano Zanin; Johann H. Martínez; Javier Buldú. Beware of the Small-World Neuroscientist! Frontiers in Human Neuroscience 2016, 10, 96 .
AMA StyleDavid Epapo, Massimiliano Zanin, Johann H. Martínez, Javier Buldú. Beware of the Small-World Neuroscientist! Frontiers in Human Neuroscience. 2016; 10 ():96.
Chicago/Turabian StyleDavid Epapo; Massimiliano Zanin; Johann H. Martínez; Javier Buldú. 2016. "Beware of the Small-World Neuroscientist!" Frontiers in Human Neuroscience 10, no. : 96.
Disorder of consciousness (DOC) is a consequence of severe brain injuries. Diagnosis of DOC is very challenging because it requires the patient collaboration. Research in hemodynamic brain activity in resting state conditions suggests that healthy brain is organized into large-scale resting state networks (RSNs) of sensory/cognitive relevance. Recently, relationships among these RSNs have been explored as a possible biomarker of loss of consciousness. The RSN functional connectivity is computed as the temporal relationship between pairs of RSNs time-courses. It results in the so called functional network of brain connectivity (FNC). The properties of this network in the DOC conditions remains poorly understood. In this work, we investigated some local complex network properties of the brain FNC,, during altered states of consciousness. For this, we characterized a population of 49 DOC patients and 27 healthy controls. fMRI data was acquired and processed for each subject to built a FNC for each one. Network characterization was performed by computing the strength and the clustering coefficient measurements at individual level on the corresponding FNC. These nodal measurements allows to understand brain alterations of single RSN in the FNC. Our results show that strength and clustering variations may reflect brain network reconfiguration, and they may be associated to loss of consciousness states in patients with DOCs.
Darwin E. Martinez; Johann H. Martinez; Jorge E. Rudas; Athena Demertzi; Lizette Heine; Luaba Tshibanda; Andrea Soddu; Steven Laureys; Francisco Gomez. A graph based characterization of functional resting state networks for patients with disorders of consciousness. 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA) 2015, 1 -8.
AMA StyleDarwin E. Martinez, Johann H. Martinez, Jorge E. Rudas, Athena Demertzi, Lizette Heine, Luaba Tshibanda, Andrea Soddu, Steven Laureys, Francisco Gomez. A graph based characterization of functional resting state networks for patients with disorders of consciousness. 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA). 2015; ():1-8.
Chicago/Turabian StyleDarwin E. Martinez; Johann H. Martinez; Jorge E. Rudas; Athena Demertzi; Lizette Heine; Luaba Tshibanda; Andrea Soddu; Steven Laureys; Francisco Gomez. 2015. "A graph based characterization of functional resting state networks for patients with disorders of consciousness." 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA) , no. : 1-8.
Disorders of consciousness (DOC) is a consequence of severe brain injuries. DOC diagnosis is quite challenging because it may require patient collaboration. Investigations of brain activity in resting conditions propose that healthy brain is organized into large-scale resting state networks (RSNs) of sensory/cognitive relevance. The complete set of RSN together with their corresponding interaction induce a functional network of brain connectivity (FNC). Recently, the connectivity pattern between pairs of RSNs have been explored as biomarker of loss of consciousness. The role of this FNC in the DOC conditions remains poorly understood. In this work, we propose to use a network analysis method to explore complex properties of the functional brain network induced by the connectivity among RSNs. In particular, we aim to characterize the communication quality among network nodes, which have been suggested to be linked to altered states of consciousness. The proposed approach was evaluated on a population of 27 healthy controls and 49 subjects with DOC conditions. fMRI data was obtained and processed for each subject to built a FNC at individual level. The communication quality among network nodes was quantified by using global efficiency, average characteristic path, diameter, radius, average strength and average clustering coefficient. Our results suggests that the information efficiency transfer at the global level decrease with the level the severity of the loss of consciousness condition. These results highlight the importance of graph based features to characterize brain complexity, and in particular, complex phenomena as consciousness emergence. In addition, our results can be potentially used in the development of novel methods to support diagnosis of patients with DOC conditions.
Darwin E. Martinez; Johann H. Martínez; Jorge Rudas; Athena Demertzi; Lizette Heine; Luaba Tshibanda; Andrea Soddu; Steven Laureys; Francisco Gomez. Functional resting state networks characterization through global network measurements for patients with disorders of consciousness. 2015 10th Computing Colombian Conference (10CCC) 2015, 286 -293.
AMA StyleDarwin E. Martinez, Johann H. Martínez, Jorge Rudas, Athena Demertzi, Lizette Heine, Luaba Tshibanda, Andrea Soddu, Steven Laureys, Francisco Gomez. Functional resting state networks characterization through global network measurements for patients with disorders of consciousness. 2015 10th Computing Colombian Conference (10CCC). 2015; ():286-293.
Chicago/Turabian StyleDarwin E. Martinez; Johann H. Martínez; Jorge Rudas; Athena Demertzi; Lizette Heine; Luaba Tshibanda; Andrea Soddu; Steven Laureys; Francisco Gomez. 2015. "Functional resting state networks characterization through global network measurements for patients with disorders of consciousness." 2015 10th Computing Colombian Conference (10CCC) , no. : 286-293.
In this study we used graph theory analysis to investigate age-related reorganization of functional networks during the active maintenance of information that is interrupted by external interference. Additionally, we sought to investigate network differences before and after averaging network parameters between both maintenance and interference windows. We compared young and older adults by measuring their magnetoencephalographic recordings during an interference-based working memory task restricted to successful recognitions. Data analysis focused on the topology/temporal evolution of functional networks during both the maintenance and interference windows. We observed that: (a) Older adults require higher synchronization between cortical brain sites in order to achieve a successful recognition, (b) The main differences between age groups arise during the interference window, (c) Older adults show reduced ability to reorganize network topology when interference is introduced, and (d) Averaging network parameters leads to a loss of sensitivity to detect age differences.
Pedro Ariza; Elena Solesio-Jofre; Johann H. Martãnez; Josã© A. Pineda-Pardo; Guiomar Niso; Fernando Maestãº; Javier M. Buldãº; Johann H. Martínez; José A. Pineda-Pardo; Fernando Maestú; Javier M. Buldú. Evaluating the effect of aging on interference resolution with time-varying complex networks analysis. Frontiers in Human Neuroscience 2015, 9, 1 .
AMA StylePedro Ariza, Elena Solesio-Jofre, Johann H. Martãnez, Josã© A. Pineda-Pardo, Guiomar Niso, Fernando Maestãº, Javier M. Buldãº, Johann H. Martínez, José A. Pineda-Pardo, Fernando Maestú, Javier M. Buldú. Evaluating the effect of aging on interference resolution with time-varying complex networks analysis. Frontiers in Human Neuroscience. 2015; 9 ():1.
Chicago/Turabian StylePedro Ariza; Elena Solesio-Jofre; Johann H. Martãnez; Josã© A. Pineda-Pardo; Guiomar Niso; Fernando Maestãº; Javier M. Buldãº; Johann H. Martínez; José A. Pineda-Pardo; Fernando Maestú; Javier M. Buldú. 2015. "Evaluating the effect of aging on interference resolution with time-varying complex networks analysis." Frontiers in Human Neuroscience 9, no. : 1.
Johann H. Martínez; P. Ariza; M. Zanin; David Papo; Fernando Maestu; J.M. Pastor; R. Bajo; Stefano Boccaletti; J.M. Buldú. Corrigendum to “Anomalous Consistency in Mild Cognitive Impairment: A complex networks approach” [Chaos Solitons Fract. J. 70 (2014) 144–155]. Chaos, Solitons & Fractals 2015, 73, 202 .
AMA StyleJohann H. Martínez, P. Ariza, M. Zanin, David Papo, Fernando Maestu, J.M. Pastor, R. Bajo, Stefano Boccaletti, J.M. Buldú. Corrigendum to “Anomalous Consistency in Mild Cognitive Impairment: A complex networks approach” [Chaos Solitons Fract. J. 70 (2014) 144–155]. Chaos, Solitons & Fractals. 2015; 73 ():202.
Chicago/Turabian StyleJohann H. Martínez; P. Ariza; M. Zanin; David Papo; Fernando Maestu; J.M. Pastor; R. Bajo; Stefano Boccaletti; J.M. Buldú. 2015. "Corrigendum to “Anomalous Consistency in Mild Cognitive Impairment: A complex networks approach” [Chaos Solitons Fract. J. 70 (2014) 144–155]." Chaos, Solitons & Fractals 73, no. : 202.