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

doi:10.1007/s12021-013-9186-1

Cited by 254 publications

(188 citation statements)

References 92 publications

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“…The main advantage of MI is that, being based on nonlinear probability distributions, it detects higher order correlations. Therefore, its result is not dependent on any specific model of the data (47).…”

Section: Meg Data Analysismentioning

confidence: 94%

Musical expertise is related to altered functional connectivity during audiovisual integration

Paraskevopoulos

Kraneburg

Herholz

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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The present study investigated the cortical large-scale functional network underpinning audiovisual integration via magnetoencephalographic recordings. The reorganization of this network related to long-term musical training was investigated by comparing musicians to nonmusicians. Connectivity was calculated on the basis of the estimated mutual information of the sources' activity, and the corresponding networks were statistically compared. Nonmusicians' results indicated that the cortical network associated with audiovisual integration supports visuospatial processing and attentional shifting, whereas a sparser network, related to spatial awareness supports the identification of audiovisual incongruences. In contrast, musicians' results showed enhanced connectivity in regions related to the identification of auditory pattern violations. Hence, nonmusicians rely on the processing of visual clues for the integration of audiovisual information, whereas musicians rely mostly on the corresponding auditory information. The large-scale cortical network underpinning multisensory integration is reorganized due to expertise in a cognitive domain that largely involves audiovisual integration, indicating long-term training-related neuroplasticity.functional connectivity | MEG | multisensory integration | cortical plasticity | musical training M ultisensory integration is of such importance for our understanding of the surrounding world that its cortical correlates interact with most of the neocortical regions, including the ones traditionally considered as unisensory (1). The cortical areas that are usually referred to as multisensory include the superior temporal sulcus, the intraparietal sulcus, and the frontal cortex (2). Nevertheless, recent evidence suggests that multisensory perception engages more widespread areas than what the classic modular approaches have so far assumed (1). Moreover, audiovisual integration emerges from a dynamic interplay of distributed regions operating in large-scale networks (3).The amount of shared information within these large-scale networks of distributed neuronal groups conceptualizes the notion of functional connectivity (4). Recent methodological advances allow the identification of networks that emerge from whole-brain analyses of neuroimaging data such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) (5) and provide fertile ground for studying the functional connectivity patterns of higher cognitive processes. These kinds of networks are modeled as graphs composed of nodes, which represent the cortical areas contributing to the network, and by edges, which represent the connections between the nodes. Each network has specific attributes that quantify connectivity organization (6). These graphs depict the functional connectome of the respective cognitive processes. Recent evidence suggests that functional connectivity networks may be reorganized by factors mediating neuroplasticity such as learning and development (7), altering information processing ...

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Section: Meg Data Analysismentioning

confidence: 94%

Musical expertise is related to altered functional connectivity during audiovisual integration

Paraskevopoulos

Kraneburg

Herholz

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…Results were reported in a descriptive sense, since they were not corrected for multiple comparisons. Signal processing and statistical analyses were performed using the software packages Matlab (version 7.14 Mathworks, Natick, MA) and the toolbox HERMES [5].…”

Section: Resultsmentioning

confidence: 99%

“…This idea was later reformulated using linear autoregressive models by Granger in 1969 [4]. An extensive description of this measure could be found in [4] and [5].…”

Section: Granger Causalitymentioning

confidence: 99%

Assessment of Effective Connectivity in Alzheimer’s Disease Using Granger Causality

Juan‐Cruz

Gómez

Poza

et al. 2016

Biosystems &Amp; Biorobotics

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Abstract-Alzheimer's disease (AD) is a neurological disorder accompanied by cognitive impairment. A complete understanding of the neurological processes involved in AD is a leading challenge in brain research. In this study, resting-state magnetoencephalography (MEG) activity from 36 AD patients and 26 healthy controls was evaluated by means of Granger Causality (GC), an effective connectivity measure that provides an estimation of the information flow between brain regions. Our results showed widespread increments in connectivity in delta (, 1-4 Hz) band. On the other hand, decrements in connectivity patterns were found for theta (, 4-8 Hz), beta (, 13-30 Hz), and gamma (, 30-65 Hz) bands. These findings strength the disconnection hypothesis in AD, and reveal GC as a useful parameter for AD identification.

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“…An important step towards deeper insights has, therefore, been achieved by methods that are capable of inferring a statistical notion of directionality or even causal interactions which have been applied to the climate system [5][6][7][8][9][10] and the human brain [11][12][13] and to disentangle cardiovascular processes [14][15][16], among others. Causal associations between subprocesses can be visualized as links in a complex interaction network.…”

Section: Introductionmentioning

confidence: 99%

Quantifying information transfer and mediation along causal pathways in complex systems

Runge

2015

Phys. Rev. E

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Measures of information transfer have become a popular approach to analyze interactions in complex systems such as the Earth or the human brain from measured time series. Recent work has focused on causal definitions of information transfer aimed at decompositions of predictive information about a target variable, while excluding effects of common drivers and indirect influences. While common drivers clearly constitute a spurious causality, the aim of the present article is to develop measures quantifying different notions of the strength of information transfer along indirect causal paths, based on first reconstructing the multivariate causal network. Another class of novel measures quantifies to what extent different intermediate processes on causal paths contribute to an interaction mechanism to determine pathways of causal information transfer. The proposed framework complements predictive decomposition schemes by focusing more on the interaction mechanism between multiple processes. A rigorous mathematical framework allows for a clear information-theoretic interpretation that can also be related to the underlying dynamics as proven for certain classes of processes. Generally, however, estimates of information transfer remain hard to interpret for nonlinearly intertwined complex systems. But if experiments or mathematical models are not available, then measuring pathways of information transfer within the causal dependency structure allows at least for an abstraction of the dynamics. The measures are illustrated on a climatological example to disentangle pathways of atmospheric flow over Europe.

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HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

Cited by 254 publications

References 92 publications

Musical expertise is related to altered functional connectivity during audiovisual integration

Musical expertise is related to altered functional connectivity during audiovisual integration

Assessment of Effective Connectivity in Alzheimer’s Disease Using Granger Causality

Quantifying information transfer and mediation along causal pathways in complex systems

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