Factors influencing the dissolved iron input by river water to the open ocean

International audienceThe influence of natural metal chelators on the bio-available iron input to the ocean by river water was studied. Ferrous and ferric ions present as suspended colloidal particles maintaining the semblance of a dissolved load are coagulated and settled as their freshwater carrier is mixed with seawater at the continental boundary. However, we might argue that different iron-binding colloids become sequentially destabilized in meeting progressively increasing salinities. By use of a 59Fe tracer method, the partitioning of the iron load from the suspended and dissolved mobile fraction to storage in the sediments was measured with high accuracy in mixtures of natural river water with artificial sea water. The results show a characteristic sequence of sedimentation. Various colloids of different stability are removed from a water of increasing salinity, such as it is the case in the transition from a river water to the open sea. However, the iron transport capacities of the investigated river waters differed greatly. A mountainous river in the Austrian Alps would add only about 5% of its dissolved Fe load, that is about 2.0 µg L-1 Fe, to coastal waters. A small tributary draining a sphagnum peat-bog, which acts as a source of refractory low-molecular-weight fulvic acids to the river water, would add approximately 20% of its original Fe load, that is up to 480 µg L-1 Fe to the ocean's bio-available iron pool. This points to a natural mechanism of ocean iron fertilization by terrigenous fulvic-iron complexes originating from weathering processes occurring in the soils upstream

Editorial: State-dependent brain computation

International audienceThe brain is a self-organizing system, which has evolved such that neuronal responses and related behavior are continuously adapted with respect to the external and internal context. This powerful capability is achieved through the modulation of neuronal interactions depending on the history of previously processed information. In particular, the brain updates its connections as it learns successful versus unsuccessful strategies. The resulting connectivity changes, together with stochastic processes (i.e., noise) influence ongoing neuronal dynamics. The role of such state-dependent fluctuations may be one of the fundamental computational properties of the brain, being pervasively present in human behavior and leaving a distinctive fingerprint in neuroscience data. This development is captured by the present Frontiers Research Topic, " State-Dependent Brain Computation

Selective activation of resting state networks following focal stimulation in a connectome- based network model of the human brain

Imaging studies suggest that the functional connectivity patterns of resting state networks (RS-networks) reflect underlying structural connectivity (SC). If the connectome constrains how brain areas are functionally connected, the stimulation of specific brain areas should produce a characteristic wave of activity ultimately resolving into RS-networks. To systematically test this hypothesis, we use a connectome-based network model of the human brain with detailed realistic SC. We systematically activate all possible thalamic and cortical areas with focal stimulation patterns and confirm that the stimulation of specific areas evokes network patterns that closely resemble RS-networks. For some sites, one or no RS-network is engaged, whereas for other sites more than one RS-network may evolve. Our results confirm that the brain is operating at the edge of criticality, wherein stimulation produces a cascade of functional network recruitments, collapsing onto a smaller subspace that is constrained in part by the anatomical local and long-range SCs. We suggest that information flow, and subsequent cognitive processing, follows specific routes imposed by connectome features, and that these routes explain the emergence of RS-networks. Since brain stimulation can be used to diagnose/treat neurological disorders, we provide a look-up table showing which areas need to be stimulated to activate specific RS-networks.Comment: 25 pages (in total), 7 figures, 2 table

Detection of fixed points in spatiotemporal signals by clustering method

We present a method to determine fixed points in spatiotemporal signals. A 144-dimensioanl simulated signal, similar to a Kueppers-Lortz instability, is analyzed and its fixed points are reconstructed.Comment: 3 pages, 3 figure

Spherical harmonic decomposition applied to spatial-temporal analysis of human high-density EEG

We demonstrate an application of spherical harmonic decomposition to analysis of the human electroencephalogram (EEG). We implement two methods and discuss issues specific to analysis of hemispherical, irregularly sampled data. Performance of the methods and spatial sampling requirements are quantified using simulated data. The analysis is applied to experimental EEG data, confirming earlier reports of an approximate frequency-wavenumber relationship in some bands.Comment: 12 pages, 8 figures, submitted to Phys. Rev. E, uses APS RevTeX style

On the sample size dependence of the critical current density in MgB2_2 superconductors

Sample size dependent critical current density has been observed in magnesium diboride superconductors. At high fields, larger samples provide higher critical current densities, while at low fields, larger samples give rise to lower critical current densities. The explanation for this surprising result is proposed in this study based on the electric field generated in the superconductors. The dependence of the current density on the sample size has been derived as a power law $j\propto R^{1/n}$ ($n$ is the $n$ factor characterizing $E-j$ curve $E=E_c(j/j_c)^n$). This dependence provides one with a new method to derive the $n$ factor and can also be used to determine the dependence of the activation energy on the current density.Comment: Revtex, 4 pages, 5 figure

The structural connectome constrains fast brain dynamics

Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics

Clinical connectome fingerprints of cognitive decline

Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that “clinical fingerprints” can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks

Central peak position in magnetization loops of high-TcT_c superconductors

Exact analytical results are obtained for the magnetization of a superconducting thin strip with a general behavior J_c(B) of the critical current density. We show that within the critical-state model the magnetization as function of applied field, B_a, has an extremum located exactly at B_a=0. This result is in excellent agreement with presented experimental data for a YBCO thin film. After introducing granularity by patterning the film, the central peak becomes shifted to positive fields on the descending field branch of the loop. Our results show that a positive peak position is a definite signature of granularity in superconductors.Comment: $ pages, 6 figure

TVB-EduPack: An interactive learning and scripting platform for The Virtual Brain

The Virtual Brain (TVB; thevirtualbrain.org) is a neuroinformatics platform for full brain network simulation based on individual anatomical connectivity data. The framework addresses clinical and neuroscientific questions by simulating multi-scale neural dynamics that range from local population activity to large-scale brain function and related macroscopic signals like electroencephalography and functional magnetic resonance imaging. TVB is equipped with a graphical and a command-line interface to create models that capture the characteristic biological variability to predict the brain activity of individual subjects. To enable researchers from various backgrounds a quick start into TVB and brain network modeling in general, we developed an educational module: TVB-EduPack. EduPack offers two educational functionalities that seamlessly integrate into TVB's graphical user interface (GUI): (i) interactive tutorials introduce GUI elements, guide through the basic mechanics of software usage and develop complex use-case scenarios; animations, videos and textual descriptions transport essential principles of computational neuroscience and brain modeling; (ii) an automatic script generator records model parameters and produces input files for TVB's Python programming interface; thereby, simulation configurations can be exported as scripts that allow flexible customization of the modeling process and self-defined batch- and post-processing applications while benefitting from the full power of the Python language and its toolboxes. This article covers the implementation of TVB-EduPack and its integration into TVB architecture. Like TVB, EduPack is an open source community project that lives from the participation and contribution of its users. TVB-EduPack can be obtained as part of TVB from thevirtualbrain.org