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Frequency Domain Connectivity Identification: An Application of Partial Directed Coherence in fMRI

SATO, Joao R.; TAKAHASHI, Daniel Y.; ARCURI, Silvia M.; Sameshima, Koichi; MORETTIN, Pedro A.; Baccala, Luiz Antonio
Fonte: WILEY-LISS Publicador: WILEY-LISS
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
65.91%
Functional magnetic resonance imaging (fMRI) has become an important tool in Neuroscience due to its noninvasive and high spatial resolution properties compared to other methods like PET or EEG. Characterization of the neural connectivity has been the aim of several cognitive researches, as the interactions among cortical areas lie at the heart of many brain dysfunctions and mental disorders. Several methods like correlation analysis, structural equation modeling, and dynamic causal models have been proposed to quantify connectivity strength. An important concept related to connectivity modeling is Granger causality, which is one of the most popular definitions for the measure of directional dependence between time series. In this article, we propose the application of the partial directed coherence (PDC) for the connectivity analysis of multisubject fMRI data using multivariate bootstrap. PDC is a frequency domain counterpart of Granger causality and has become a very prominent tool in EEG studies. The achieved frequency decomposition of connectivity is useful in separating interactions from neural modules from those originating in scanner noise, breath, and heart beating. Real fMRI dataset of six subjects executing a language processing protocol was used for the analysis of connectivity. Hum Brain Mapp 30:452-461...

Coerência parcial e aplicações; Partial Coherence and Its Applications

Lopes, Kim Samejima Mascarenhas
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 24/04/2009 Português
Relevância na Pesquisa
25.7%
Neste trabalho foram estudadas algumas formas de relação entre séries temporais multivariadas. Discutiu-se, inicialmente, a função de coerência, uma função análoga a função de correlação(que é dada no domínio do tempo) calculada no domínio da freqüência. Foram estudadas também as funções de coerência parcial e coerência parcial direcionada. A função de coerência parcial mede a relação entre duas componentes de uma série multivariada, isolados os efeitos de outra série. Em linhas gerais, a Coerência Parcial Direcionada pode ser interpredata como a decomposição da coerência parcial a partir de modelos autoregressivos multivariados. Esse conceito pode ser interpretado como uma representação do conceito de causalidade de Granger no domínio da freqüência. Finalmente, foram aplicadas as funções acima em dois conjuntos de dados: um modelo VAR(1) trivariado simulado e dados de medições de eletroencefalograma.; In this work we studied relationships between multivariate time series. We discussed the coherence function, a function similar to the correlation function(calculated in time domain) in frequency domain. Next, we discussed partial coherence and partial directed coherence. The partial coherence measures the relationship between two components of a multivariate time series...

The frequency domain causality analysis between energy consumption and income in the United States

Tiwari,Aviral Kumar
Fonte: Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto da Universidade de São Paulo Publicador: Faculdade de Economia, Administração e Contabilidade de Ribeirão Preto da Universidade de São Paulo
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/03/2014 Português
Relevância na Pesquisa
155.89%
We investigated Granger-causality in the frequency domain between primary energy consumption/electricity consumption and GDP for the US by employing approach of Lemmens et al. (2008) and covering the period of January, 1973 to December, 2008. We found that causal and reverse causal relations between primary energy consumption and GDP and electricity consumption and GDP vary across frequencies. Our unique contribution in the existing literature lies in decomposing the causality on the basis of time horizons and demonstrating bidirectional the short-run, the medium-run and the long-run causality between GDP and primary energy consumption/electricity consumption and thus providing evidence for the feedback hypothesis. These results have important implications for the US for planning of the short, the medium and the long run energy and economic growth related policies.

Uncovering Interactions in the Frequency Domain

Guo, Shuixia; Wu, Jianhua; Ding, Mingzhou; Feng, Jianfeng
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
85.6%
Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multi-electrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey.

Investigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls

Demirci, Oguz; Stevens, Michael C.; Andreasen, Nancy C.; Michael, Andrew; Liu, Jingyu; White, Tonya; Pearlson, Godfrey D.; Clark, Vincent P.; Calhoun, Vince D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.71%
Functional network connectivity (FNC) is an approach that examines the relationships between brain networks (as opposed to functional connectivity (FC) that focuses upon the relationships between single voxels). FNC may help explain the complex relationships between distributed cerebral sites in the brain and possibly provide new understanding of neurological and psychiatric disorders such as schizophrenia. In this paper, we use independent component analysis (ICA) to extract the time courses of spatially independent components and then use these in Granger causality test (GCT) to investigate causal relationships between brain activation networks. We present results using both simulations and fMRI data of 155 subjects obtained during two different tasks. Unlike previous research, causal relationships are presented over different portions of the frequency spectrum in order to differentiate high and low frequency effects and not merged in a scalar. The results obtained using Sternberg item recognition paradigm (SIRP) and auditory oddball (AOD) tasks showed FNC differentiations between schizophrenia and control groups, and explained how the two groups differed during these tasks. During the SIRP task, secondary visual and cerebellum activation networks served as hubs and included most complex relationships between the activated regions. Secondary visual and temporal lobe activations replaced these components during the AOD task.

More discussions for granger causality and new causality measures

Hu, Sanqing; Cao, Yu; Zhang, Jianhai; Kong, Wanzeng; Yang, Kun; Zhang, Yanbin; Li, Xun
Fonte: Springer Netherlands Publicador: Springer Netherlands
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
45.95%
Granger causality (GC) has been widely applied in economics and neuroscience to reveal causality influence of time series. In our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829–844, 2011), we proposed new causalities in time and frequency domains and particularly focused on new causality in frequency domain by pointing out the shortcomings/limitations of GC or Granger-alike causality metrics and the advantages of new causality. In this paper we continue our previous discussions and focus on new causality and GC or Granger-alike causality metrics in time domain. Although one strong motivation was introduced in our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829–844, 2011) we here present additional motivation for the proposed new causality metric and restate the previous motivation for completeness. We point out one property of conditional GC in time domain and the shortcomings/limitations of conditional GC which cannot reveal the real strength of the directional causality among three time series. We also show the shortcomings/limitations of directed causality (DC) or normalize DC for multivariate time series and demonstrate it cannot reveal real causality at all. By calculating GC and new causality values for an example we demonstrate the influence of one of the time series on the other is linearly increased as the coupling strength is linearly increased. This fact further supports reasonability of new causality metric. We point out that larger instantaneous correlation does not necessarily mean larger true causality (e.g....

Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods

Hu, Sanqing; Dai, Guojun; Worrell, Gregory A.; Dai, Qionghai; Liang, Hualou
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
56.03%
Granger causality (GC) is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. Especially, its counterpart in frequency domain, spectral GC, as well as other Granger-like causality measures have recently been applied to study causal interactions between brain areas in different frequency ranges during cognitive and perceptual tasks. In this paper, we show that: 1) GC in time domain cannot correctly determine how strongly one time series influences the other when there is directional causality between two time series, and 2) spectral GC and other Granger-like causality measures have inherent shortcomings and/or limitations because of the use of the transfer function (or its inverse matrix) and partial information of the linear regression model. On the other hand, we propose two novel causality measures (in time and frequency domains) for the linear regression model, called new causality and new spectral causality, respectively, which are more reasonable and understandable than GC or Granger-like measures. Especially, from one simple example, we point out that, in time domain, both new causality and GC adopt the concept of proportion, but they are defined on two different equations where one equation (for GC) is only part of the other (for new causality)...

Coherence analysis overestimates the role of baroreflex in governing the interactions between heart period and systolic arterial pressure variabilities during general anesthesia

Bassani, Tito; Bari, Vlasta; Marchi, Andrea; Wu, Maddalena Alessandra; Baselli, Giuseppe; Citerio, Giuseppe; Beda, Alessandro; de Abreu, Marcelo Gama; Güldner, Andreas; Guzzetti, Stefano; Porta, Alberto
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Publicado em /11/2013 Português
Relevância na Pesquisa
35.63%
During general anesthesia positive pressure mechanical ventilation (MV) profoundly affects intrathoracic pressure and venous return, thus soliciting cardiopulmonary reflexes and modifying stroke volume. As a consequence heart period, approximated as the temporal distance between two consecutive R peaks on the ECG (RR), and systolic arterial pressure (SAP) variability series are usually highly correlated at the MV frequency (MVF) and this significant correlation is commonly taken as an indication of an active baroreflex. In this study the involvement of baroreflex was tested according to a time-domain linear Granger causality approach accounting explicitly for MV in two experimental protocols. In the first protocol volatile (VA) or intravenous (IA) anesthetic was administered in humans during pressure controlled MV (PCMV). In the second protocol IA was administered in pigs during PCMV or pressure support MV (PSMV). Causality analysis was contrasted with RR-SAP squared coherence. Significant coherence values at MVF were always found in both protocols. On the contrary, a significant causal link from SAP to RR was less frequently found in humans independently of the anesthesiological strategy and in animals during PCMV. PSMV was superior to PCMV in animals because it was able to better preserve a link from SAP to RR. During general anesthesia the involvement of baroreflex in governing RR-SAP variability interactions is largely overestimated by RR-SAP squared coherence and causality analysis can be exploited to rank anesthesiological strategies and MV modes according to the ability of preserving a working baroreflex.

Combining independent component analysis and Granger causality to investigate brain network dynamics with fNIRS measurements

Yuan, Zhen
Fonte: Optical Society of America Publicador: Optical Society of America
Tipo: Artigo de Revista Científica
Publicado em 25/10/2013 Português
Relevância na Pesquisa
55.75%
In this study a new strategy that combines Granger causality mapping (GCM) and independent component analysis (ICA) is proposed to reveal complex neural network dynamics underlying cognitive processes using functional near infrared spectroscopy (fNIRS) measurements. The GCM-ICA algorithm implements the following two procedures: (i) extraction of the region of interests (ROIs) of cortical activations by ICA, and (ii) estimation of the direct causal influences in local brain networks using Granger causality among voxels of ROIs. Our results show that the use of GCM in conjunction with ICA is able to effectively identify the directional brain network dynamics in time-frequency domain based on fNIRS recordings.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
85.91%
Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover...

Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

Kong, Wanzeng; Lin, Weicheng; Babiloni, Fabio; Hu, Sanqing; Borghini, Gianluca
Fonte: MDPI Publicador: MDPI
Tipo: Artigo de Revista Científica
Publicado em 05/08/2015 Português
Relevância na Pesquisa
65.78%
Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG) signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC) in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies.

Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data

Chen, Yonghong; Bressler, Steven L.; Ding, Mingzhou
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 23/08/2006 Português
Relevância na Pesquisa
65.87%
It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.; Comment: 18 pages, 6 figures, Journal published

HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity

Niso, Guiomar; Bruña, Ricardo; Pereda, Ernesto; Gutiérrez, Ricardo; Bajo, Ricardo; Maestú, Fernando; del-Pozo, Francisco
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
25.53%
The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the 'traditional' set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab environment (The Mathworks...

State Space Methods for Granger-Geweke Causality Measures

Solo, Victor
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/01/2015 Português
Relevância na Pesquisa
35.67%
At least two recent developments have put the spotlight on some significant gaps in the theory of multivariate time series. The recent interest in the dynamics of networks; and the advent, across a range of applications, of measuring modalities that operate on different temporal scales. Fundamental to the description of network dynamics is the direction of interaction between nodes, accompanied by a measure of the strength of such interactions. Granger causality (GC) and its associated frequency domain strength measures (GEMs) (due to Geweke) provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. Firstly computing GEMs involves computing submodels of vector time series mod- els, for which reliable methods do not exist; Secondly the impact of filtering on GEMs has never been definitively established. Thirdly the impact of downsampling on GEMs has never been established. In this work, using state space methods, we resolve all these issues and illustrate the results with some simulations. Our discussion is motivated by some problems in (fMRI) brain imaging but is of general applicability.; Comment: These results have been presented in a number of invited talks. HBM Conference...

The frequency domain causality analysis between energy consumption and income in the United States; The frequency domain causality analysis between energy consumption and income in the United States

Tiwari, Aviral Kumar
Fonte: Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de RP Publicador: Universidade de São Paulo. Faculdade de Economia, Administração e Contabilidade de RP
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; ; Formato: application/pdf
Publicado em 01/03/2014 Português
Relevância na Pesquisa
95.85%
Através do teste de casualidade de Granger, nós investigamos o domínio de frequência entre o consumo primário de energia/eletricidade e o produto interno bruto (PIB) dos Estados Unidos; aplicando a abordagem de Lemmens et al. (2008) e cobrindo o período entre Janeiro de 1973 a Dezembro de 2008. Nós achamos relações causal e causal reversa entre o consumo primário de energia e PIB, e o consumo de eletricidade e PIB variam através das frequências. Nossa contribuição única na literatura existente reside na decomposição da causalidade com base em horizontes de tempo e demonstração bi-direcional de causalidade de curto prazo, médio-prazo e longo-prazo entre PIB e consumo primário de energia/eletricidade e assim provendo evidência para a "feedback hypothesis". Estes resultados têm importantes implicações para o planejamento energértico de curto, médio e longo prazo dos Estados Unidos e políticas relacionadas ao crescimento econômico.; We investigated Granger-causality in the frequency domain between primary energy consumption/electricity consumption and GDP for the US by employing approach of Lemmens et al. (2008) and covering the period of January, 1973 to December, 2008. We found that causal and reverse causal relations between primary energy consumption and GDP and electricity consumption and GDP vary across frequencies. Our unique contribution in the existing literature lies in decomposing the causality on the basis of time horizons and demonstrating bidirectional the short-run...