Organization, development and function of complex brain networks

Sporns, Olaf; Chialvo, Dante R.; Kaiser, Marcus; Hilgetag, Claus C.

doi:10.1016/j.tics.2004.07.008

Cited by 1,868 publications

(1,423 citation statements)

References 75 publications

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“…The higher local efficiency observed suggests elevated clustering of functionally related regions (Sporns et al 2004). This means that information processing within certain neural networks is stronger, with less cross‐talk between distinct functional processes.…”

Section: Discussionmentioning

confidence: 99%

“…This means that information processing within certain neural networks is stronger, with less cross‐talk between distinct functional processes. This clustering of connections suggests hyperactive intermediate signalling (rather than long‐range signalling) in the network, perturbing the capacity for more global neural cooperation or wider cortical interactions (Sporns et al 2004). Higher local efficiency may also suggest higher likelihood of closed feedback loops and enhanced reciprocity of connections, meaning that information is not dispersed and processed across networks but remains closed off in a specific network model (Sporns et al 2004).…”

Section: Discussionmentioning

confidence: 99%

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Naltrexone ameliorates functional network abnormalities in alcohol‐dependent individuals

et al. 2017

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Naltrexone, an opioid receptor antagonist, is commonly used as a relapse prevention medication in alcohol and opiate addiction, but its efficacy and the mechanisms underpinning its clinical usefulness are not well characterized. In the current study, we examined the effects of 50‐mg naltrexone compared with placebo on neural network changes associated with substance dependence in 21 alcohol and 36 poly‐drug‐dependent individuals compared with 36 healthy volunteers. Graph theoretic and network‐based statistical analysis of resting‐state functional magnetic resonance imaging (MRI) data revealed that alcohol‐dependent subjects had reduced functional connectivity of a dispersed network compared with both poly‐drug‐dependent and healthy subjects. Higher local efficiency was observed in both patient groups, indicating clustered and segregated network topology and information processing. Naltrexone normalized heightened local efficiency of the neural network in alcohol‐dependent individuals, to the same levels as healthy volunteers. Naltrexone failed to have an effect on the local efficiency in abstinent poly‐substance‐dependent individuals. Across groups, local efficiency was associated with substance, but no alcohol exposure implicating local efficiency as a potential premorbid risk factor in alcohol use disorders that can be ameliorated by naltrexone. These findings suggest one possible mechanism for the clinical effects of naltrexone, namely, the amelioration of disrupted network topology.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Naltrexone ameliorates functional network abnormalities in alcohol‐dependent individuals

et al. 2017

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“…However, this approach conveys a rather static idea of brain processes. By contrast, it is now believed that processing of a functional task by the brain can only be performed through interaction of segregated regions within a complex network (Hebb, 1949;Tononi et al, 1998;Frackowiak et al, 2004;Sporns et al, 2004). Consequently, various methods have been proposed in fMRI data analysis to extract information of interaction from datasets, most of which rely on either functional or effective connectivity (for reviews and discussions, see, e.g., Stone and Kö tter, 2002;Horwitz, 2003;Lee et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Partial correlation for functional brain interactivity investigation in functional MRI

et al. 2006

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“…The role of networks in brain function has been increasingly recognized over the past decade [Friston et al, 1993;Sporns et al, 2004]. In functional neuroimaging, brain networks are primarily studied in terms of functional connectivity (defined as temporal correlations between remote neurophysiologic events) and effective connectivity (defined as the causal influence one neuronal system exerts over another) [Friston, 1995].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Granger causality analysis of fMRI data

Deshpande

LaConte

James

et al. 2008

Human Brain Mapping

216

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This article describes the combination of multivariate Ganger causality analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network.

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Organization, development and function of complex brain networks

Cited by 1,868 publications

References 75 publications

Naltrexone ameliorates functional network abnormalities in alcohol‐dependent individuals

Naltrexone ameliorates functional network abnormalities in alcohol‐dependent individuals

Partial correlation for functional brain interactivity investigation in functional MRI

Multivariate Granger causality analysis of fMRI data

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