Avalanches in self-organized critical neural networks: A minimal model for the neural SOC universality class

The brain keeps its overall dynamics in a corridor of intermediate activity and it has been a long standing question what possible mechanism could achieve this task. Mechanisms from the field of statistical physics have long been suggesting that this homeostasis of brain activity could occur even without a central regulator, via self-organization on the level of neurons and their interactions, alone. Such physical mechanisms from the class of self-organized criticality exhibit characteristic dynamical signatures, similar to seismic activity related to earthquakes. Measurements of cortex rest activity showed first signs of dynamical signatures potentially pointing to self-organized critical dynamics in the brain. Indeed, recent more accurate measurements allowed for a detailed comparison with scaling theory of non-equilibrium critical phenomena, proving the existence of criticality in cortex dynamics. We here compare this new evaluation of cortex activity data to the predictions of the earliest physics spin model of self-organized critical neural networks. We find that the model matches with the recent experimental data and its interpretation in terms of dynamical signatures for criticality in the brain. The combination of signatures for criticality, power law distributions of avalanche sizes and durations, as well as a specific scaling relationship between anomalous exponents, defines a universality class characteristic of the particular critical phenomenon observed in the neural experiments. The spin model is a candidate for a minimal model of a self-organized critical adaptive network for the universality class of neural criticality. As a prototype model, it provides the background for models that include more biological details, yet share the same universality class characteristic of the homeostasis of activity in the brain.Comment: 17 pages, 5 figure

Internal Jugular Vein Cross-Sectional Area Enlargement Is Associated with Aging in Healthy Individuals.

Internal jugular vein (IJV) narrowing has been implicated in central nervous system pathologies, however normal physiological age- and gender-related IJV variance in healthy individuals (HIs) has not been adequately assessed.We assessed the relationship between IJV cross-sectional area (CSA) and aging.This study involved 193 HIs (63 males and 130 females) who received 2-dimensional magnetic resonance venography at 3T. The minimum CSA of the IJVs at cervical levels C2/C3, C4, C5/C6, and C7/T1 was obtained using a semi-automated contouring-thresholding technique. Subjects were grouped by decade. Pearson and partial correlation (controlled for cardiovascular risk factors, including hypertension, heart disease, smoking and body mass index) and analysis of variance analyses were used, with paired t-tests comparing side differences.Mean right IJV CSA ranges were: in males, 41.6 mm2 (C2/C3) to 82.0 mm2 (C7/T1); in females, 38.0 mm2 (C2/C3) to 62.3 mm2 (C7/T1), while the equivalent left side ranges were: in males, 28.0 mm2 (C2/C3) to 52.2 mm2 (C7/T1); in females, 27.2 mm2 (C2/C3) to 47.8 mm2 (C7/T1). The CSA of the right IJVs was significantly larger (p<0.001) than the left at all cervical levels. Controlling for cardiovascular risk factors, the correlation between age and IJV CSA was more robust in males than in the females for all cervical levels.In HIs age, gender, hand side and cervical location all affect IJV CSA. These findings suggest that any definition of IJV stenosis needs to account for these factors

Loss of Expression and Promoter Methylation of SLIT2 Are Associated with Sessile Serrated Adenoma Formation.

Serrated adenomas form a distinct subtype of colorectal pre-malignant lesions that may progress to malignancy along a different molecular pathway than the conventional adenoma-carcinoma pathway. Previous studies have hypothesised that BRAF mutation and promoter hypermethylation plays a role, but the evidence for this is not robust. We aimed to carry out a whole-genome loss of heterozygosity analysis, followed by targeted promoter methylation and expression analysis to identify potential pathways in serrated adenomas. An initial panel of 9 sessile serrated adenomas (SSA) and one TSA were analysed using Illumina Goldengate HumanLinkage panel arrays to ascertain regions of loss of heterozygosity. This was verified via molecular inversion probe analysis and microsatellite analysis of a further 32 samples. Methylation analysis of genes of interest was carried out using methylation specific PCR (verified by pyrosequencing) and immunohistochemistry used to correlate loss of expression of genes of interest. All experiments used adenoma samples and normal tissue samples as control. SSA samples were found on whole-genome analysis to have consistent loss of heterozygosity at 4p15.1–4p15.31, which was not found in the sole TSA, adenomas, or normal tissues. Genes of interest in this region were PDCH7 and SLIT2, and combined MSP/IHC analysis of these genes revealed significant loss of SLIT2 expression associated with promoter methylation of SLIT2. Loss of expression of SLIT2 by promoter hypermethylation and loss of heterozygosity events is significantly associated with serrated adenoma development, and SLIT2 may represent a epimutated tumour suppressor gene according to the Knudson “two hit” hypothesis

Efficient Network Reconstruction from Dynamical Cascades Identifies Small-World Topology of Neuronal Avalanches

Cascading activity is commonly found in complex systems with directed interactions such as metabolic networks, neuronal networks, or disease spreading in social networks. Substantial insight into a system's organization can be obtained by reconstructing the underlying functional network architecture from the observed activity cascades. Here we focus on Bayesian approaches and reduce their computational demands by introducing the Iterative Bayesian (IB) and Posterior Weighted Averaging (PWA) methods. We introduce a special case of PWA, cast in nonparametric form, which we call the normalized count (NC) algorithm. NC efficiently reconstructs random and small-world functional network topologies and architectures from subcritical, critical, and supercritical cascading dynamics and yields significant improvements over commonly used correlation methods. With experimental data, NC identified a functional and structural small-world topology and its corresponding traffic in cortical networks with neuronal avalanche dynamics

Causal Measures of Structure and Plasticity in Simulated and Living Neural Networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time

Down-Regulation of Serum/Glucocorticoid Regulated Kinase 1 in Colorectal Tumours Is Largely Independent of Promoter Hypermethylation

Background: We have previously shown that serum/glucocorticoid regulated kinase 1 (SGK1) is down-regulated in colorectal cancers (CRC) with respect to normal tissue. As hyper-methylation of promoter regions is a well-known mechanism of gene silencing in cancer, we tested whether the SGK1 promoter region was methylated in colonic tumour samples. Methodology/Principal Findings: We investigated the methylation profile of the two CpG islands present in the promoter region of SGK1 in a panel of 5 colorectal cancer cell lines by sequencing clones of bisulphite-treated DNA samples. We further confirmed our findings in a panel of 10 normal and 10 tumour colonic tissue samples of human origin. We observed CpG methylation only in the smaller and more distal CpG island in the promoter region of SGK1 in both normal and tumour samples of colonic origin. We further identified a single nucleotide polymorphism (SNP, rs1743963) which affects methylation of the corresponding CpG. Conclusions/Significance: Our results show that even though partial methylation of the promoter region of SGK1 is present

A new methodological contribution for the geodiversity assessment: applicability to Ceará State (Brazil)

The concept of geodiversity aggregates the abiotic elements of nature and promotes the geoconservation. The main objective of this work is to contribute to the upgrade of the method for the assessment and quantification of geodiversity proposed by Pereira et al. (2013). The method is based on the superposition of a regular grid of 12 × 12 km on different maps (lithology, geomorphology, soil, paleonthology, mineral and geological energy resources) at scales of 1:250,000 to 1:600,000. In addition to other up- grades, the water resources are regarded here as a new com- ponent to consider when quantifying geodiversity. The sum of these maps generated the quantitative Map of Geodiversity Indices and the Map of Geodiversity Assessment, ranging from very low to very high geodiversity. The analysis of the geodiversity map of the State of Ceará (Brazil) shows the applicability and advantage of this method, highlighting two regions with higher levels of geodiversity (Northwest and South) and another region with the lowest levels (Sertões Cearenses). The results also allowed the characterization of the State of Ceará concerning the individual components of the geodiversity, especially the water resources. Geodiversity indices and maps are comprehensive and user-friendly data in the territorial planning, considering the geodiversity either as a whole, or each of its components, especially the more sensi- tive such as fossil conservation, and water, mineral, and non- renewable energy resources management.The authors express their gratitude to the Brazilian research fostering institution "Coordenação de Aperfeiçoamento de Pessoal de Nível Superior" (CAPES) for awarding the Ciência Sem Fronteiras (CsF) PhD scholarship that enabled this work. This work was partially co-funded by the European Union through the European Regional Development Fund, based on COMPETE 2020 (Programa Operacional da Competitividade e Internacionalização), project ICT (UID/GEO/04683/ 2013) with reference POCI-01-0145-FEDER-007690 and national funds provided by Fundação para a Ciência e Tecnologia

From Retinal Waves to Activity-Dependent Retinogeniculate Map Development

A neural model is described of how spontaneous retinal waves are formed in infant mammals, and how these waves organize activity-dependent development of a topographic map in the lateral geniculate nucleus, with connections from each eye segregated into separate anatomical layers. The model simulates the spontaneous behavior of starburst amacrine cells and retinal ganglion cells during the production of retinal waves during the first few weeks of mammalian postnatal development. It proposes how excitatory and inhibitory mechanisms within individual cells, such as Ca2+-activated K+ channels, and cAMP currents and signaling cascades, can modulate the spatiotemporal dynamics of waves, notably by controlling the after-hyperpolarization currents of starburst amacrine cells. Given the critical role of the geniculate map in the development of visual cortex, these results provide a foundation for analyzing the temporal dynamics whereby the visual cortex itself develops

Network-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI