Neural complexity and structural connectivity

Barnett, Lionel; Buckley, Christopher L.; Bullock, Seth

doi:10.1103/physreve.79.051914

Cited by 65 publications

(130 citation statements)

References 39 publications

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“…In neuroanatomical terms, this means that a complex neural system must be highly interconnected" (Tononi, et al 1994). Recently, Barnett et al (2009) confirmed these results.…”

Section: Introductionsupporting

confidence: 75%

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

Fernández¹,

Ríos-Lago²,

Abásolo³

et al. 2011

NeuroImage

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The advent of new signal processing methods, such as non-linear analysis techniques, represents a new perspective which adds further value to brain signals' analysis. Particularly, Lempel-Ziv's Complexity (LZC) has proven to be useful in exploring the complexity of the brain electromagnetic activity. However, an important problem is the lack of knowledge about the physiological determinants of these measures. Although a correlation between complexity and connectivity has been proposed, this hypothesis was never tested in vivo.Thus, the correlation between the microstructure of the anatomic connectivity and the functional complexity of the brain needs to be inspected. In this study we analysed the correlation between LZC and fractional anisotropy (FA), a scalar quantity derived from diffusion tensors that is particularly useful as an estimate of the functional integrity of myelinated axonal fibers, in a group of sixteen healthy adults (all female, mean age 65.56 ± 6.06 years, interval 58-82). Our results showed a positive correlation between FA and LZC scores in regions including clusters in the splenium of the corpus callosum, cingulum, parahipocampal regions and the sagittal stratum. This study supports the notion of a positive correlation between the functional complexity of the brain and the microstructure of its anatomical connectivity. Our investigation proved that a combination of neuroanatomical and neurophysiological techniques may shed some light on the underlying physiological determinants of brain's oscillations.

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“…In neuroanatomical terms, this means that a complex neural system must be highly interconnected" (Tononi, et al 1994). Recently, Barnett et al (2009) confirmed these results.…”

Section: Introductionsupporting

confidence: 75%

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

Fernández¹,

Ríos-Lago²,

Abásolo³

et al. 2011

NeuroImage

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show abstract

“…Since there is no a priori 'best' normalization for a network considered an abstraction of a neural system (e.g. [26]), we decided to compare two plausible alternatives. Further work is needed to establish the general relations among normalization, network structure and dynamical measures.…”

Section: (A) Comparison Among Measuresmentioning

confidence: 99%

Causal density and integrated information as measures of conscious level

Seth

Barrett

Barnett

2011

Phil. Trans. R. Soc. A.

113

117

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An outstanding challenge in neuroscience is to develop theoretically grounded and practically applicable quantitative measures that are sensitive to conscious level. Such measures should be high for vivid alert conscious wakefulness, and low for unconscious states such as dreamless sleep, coma and general anaesthesia. Here, we describe recent progress in the development of measures of dynamical complexity, in particular causal density and integrated information. These and similar measures capture in different ways the extent to which a system's dynamics are simultaneously differentiated and integrated. Because conscious scenes are distinguished by the same dynamical features, these measures are therefore good candidates for reflecting conscious level. After reviewing the theoretical background, we present new simulation results demonstrating similarities and differences between the measures, and we discuss remaining challenges in the practical application of the measures to empirically obtained data.

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“…This enables the interactions between network components (and mutual information itself) to be expressed via a covariance matrix. Recently we have highlighted and then amended an error in this analytical model by moving to a continuous time analogue of the original apparently discrete time formulation [12]. Consequently it is necessary to revisit the analytical work of De Lucia et al in light of this correction.…”

Section: Introductionmentioning

confidence: 99%

Neural complexity: A graph theoretic interpretation

2011

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One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end Tononi et al. [Proc. Nat. Acad. Sci. USA 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system's dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns et al. [Cereb. Cortex 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. WhileDe Lucia et al. [Phys. Rev. E 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work.In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular we explicitly establish a dependency of neural complexity on cyclic graph motifs.

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Neural complexity and structural connectivity

Cited by 65 publications

References 39 publications

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

Causal density and integrated information as measures of conscious level

Neural complexity: A graph theoretic interpretation

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