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Network or graph theory has become a popular tool to represent and analyze large-scale interaction patterns in the brain. To derive a functional network representation from experimentally recorded neural time series one has to identify the structure of the interactions between these time series. In neuroscience, this is often done by pairwise bivariate analysis because a fully multivariate treatment is typically not possible due to limited data and excessive computational cost. Furthermore, a true multivariate analysis would consist of the analysis of the combined effects, including information theoretic synergies and redundancies, of all possible subsets of network components. Since the number of these subsets is the power set of the network components, this leads to a combinatorial explosion (i.e. a problem that is computationally intractable). In contrast, a pairwise bivariate analysis of interactions is typically feasible but introduces the possibility of false detection of spurious interactions between network components, especially due to cascade and common drive effects. These spurious connections in a network representation may introduce a bias to subsequently computed graph theoretical measures (e.g. clustering coefficient or centrality) as these measures depend on the reliability of the graph representation from which they are computed. Strictly speaking, graph theoretical measures are meaningful only if the underlying graph structure can be guaranteed to consist of one type of connections only, i.e. connections in the graph are guaranteed to be non-spurious. ...
When studying real world complex networks, one rarely has full access to all their components. As an example, the central nervous system of the human consists of 1011 neurons which are each connected to thousands of other neurons. Of these 100 billion neurons, at most a few hundred can be recorded in parallel. Thus observations are hampered by immense subsampling. While subsampling does not affect the observables of single neuron activity, it can heavily distort observables which characterize interactions between pairs or groups of neurons. Without a precise understanding how subsampling affects these observables, inference on neural network dynamics from subsampled neural data remains limited.
We systematically studied subsampling effects in three self-organized critical (SOC) models, since this class of models can reproduce the spatio-temporal activity of spontaneous activity observed in vivo. The models differed in their topology and in their precise interaction rules. The first model consisted of locally connected integrate- and fire units, thereby resembling cortical activity propagation mechanisms. The second model had the same interaction rules but random connectivity. The third model had local connectivity but different activity propagation rules. As a measure of network dynamics, we characterized the spatio-temporal waves of activity, called avalanches. Avalanches are characteristic for SOC models and neural tissue. Avalanche measures A (e.g. size, duration, shape) were calculated for the fully sampled and the subsampled models. To mimic subsampling in the models, we considered the activity of a subset of units only, discarding the activity of all the other units.
Under subsampling the avalanche measures A depended on three main factors: First, A depended on the interaction rules of the model and its topology, thus each model showed its own characteristic subsampling effects on A. Second, A depended on the number of sampled sites n. With small and intermediate n, the true A¬ could not be recovered in any of the models. Third, A depended on the distance d between sampled sites. With small d, A was overestimated, while with large d, A was underestimated.
Since under subsampling, the observables depended on the model's topology and interaction mechanisms, we propose that systematic subsampling can be exploited to compare models with neural data: When changing the number and the distance between electrodes in neural tissue and sampled units in a model analogously, the observables in a correct model should behave the same as in the neural tissue. Thereby, incorrect models can easily be discarded. Thus, systematic subsampling offers a promising and unique approach to model selection, even if brain activity was far from being fully sampled.
Neuronal dynamics differs between wakefulness and sleep stages, so does the cognitive state. In contrast, a single attractor state, called self-organized critical (SOC), has been proposed to govern human brain dynamics for its optimal information coding and processing capabilities. Here we address two open questions: First, does the human brain always operate in this computationally optimal state, even during deep sleep? Second, previous evidence for SOC was based on activity within single brain areas, however, the interaction between brain areas may be organized differently. Here we asked whether the interaction between brain areas is SOC. ...
Meeting Abstract : Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e.V. (GAA). 17. Jahrestagung der Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie. Osnabrück, 25.-26.11.2010.
ntroduction: Several drugs require dose adjustment in patients with impaired renal function, which however, often goes undetected. Serum creatinine may be normal in patients while renal function is already reduced. The estimated GFR (eGFR) allows a more precise evaluation of the renal function. This study was carried out in a group practice for family medicine, in Frankfurt/ Main, Germany. The exploration aimed at investigating if patients with renal insufficiency were recognised and if their prescriptions were appropriate in terms of dose adjustment or contra-indications.
Methods: In patients (>65yrs) with renal insufficiency (creatinine clearance <60 ml/min), their prescribed medication was retrospectively explored (Observation period 1.1.2008 to 1.4.2009). The Cockroft-Gault formula was used as estimate for the eGFR, using a creatinine value from the patient’s charts. In 90 patients, a second eGFR could be estimated from a second creatinine value obtained within 3-6 months. The recommended dose of each prescription in the SmPC (Fachinformation“) was compared to the dose that had been actually prescribed.
Results: Out of 232 consecutively patients >65 yrs, 102 had an eGFR <60 ml/min, 16 of these had an eGFR <30 ml/min. The eGFR was closely correlated (r2=0.81) with an independent second eGFR. Out of these 102 patients, 48 had a serum creatinine level within the normal range. Renal adjustment was required in 263 of a total of 613 prescriptions. 72 prescriptions in a total of 45 patients were not appropriately adjusted (32) or prescribed despite a contraindication (40). For chronic prescriptions, metformin, ramipril, enalapril, HCTZ, and spironolactone accounted for 70% of inappropriate dosing; the magnitude of misdosing was 1.5 to 4 fold (median 2). 9 temporary prescriptions (of a total of 60 prescriptions) in 8 patients were not adjusted (cefuroxim, cefpodoxim, levofloxacin). We could not prove that patients with normal serum creatinine had a higher rate of inappropriate dosing than those with already elevated creatinine.
Discussion and conclusion: In this GP practice, we have demonstrated a considerable prevalence of inappropriate dosing in patients with impaired renal function. It remains to be elucidated whether surveillance of appropriate dosing in renal impairment can be optimized e.g. with CPOE.
Background: Undergoing systemic inflammation, the innate immune system releases excessive proinflammatory mediators, which finally can lead to organ failure. Pattern recognition receptors (PRRs), such as Toll-like receptors (TLRs) and NOD-like receptors (NLRs), form the interface between bacterial and viral toxins and innate immunity. During sepsis, patients with diagnosed adrenal gland insufficiency are at high risk of developing a multiorgan dysfunction syndrome, which dramatically increases the risk of mortality. To date, little is known about the mechanisms leading to adrenal dysfunction under septic conditions. Here, we investigated the sepsis-related activation of the PRRs, cell inflammation, and apoptosis within adrenal glands.
Methods: Two sepsis models were performed: the polymicrobial sepsis model (caecal ligation and puncture (CLP)) and the LTA-induced intoxication model. All experiments received institutional approval by the Regierungspräsidium Darmstadt. CLP was performed as previously described [1], wherein one-third of the caecum was ligated and punctured with a 20-gauge needle. For LTA-induced systemic inflammation, TLR2 knockout (TLR2-/-) and WT mice were injected intraperitoneally with pure LTA (pLTA; 1 mg/kg) or PBS for 2 hours. To detect potential direct adrenal dysfunction, mice were additionally injected with adrenocorticotropic hormone (ACTH; 100 μg/kg) 1 hour after pLTA or PBS. Adrenals and plasma samples were taken. Gene expressions in the adrenals (rt-PCR), cytokine release (multiplex assay), and the apoptosis rate (TUNEL assay) within the adrenals were determined.
Results: In both models, adrenals showed increased mRNA expression of TLR2 and TLR4, various NLRs, cytokines as well as inflammasome components, NADPH oxidase subunits, and nitric oxide synthases (data not shown). In WT mice, ACTH alone had no effect on inflammation, while pLTA or pLTA/ACTH administration showed increased levels of the cytokines IL-1β, IL-6, and TNFα. TLR2-/- mice indicated no response as expected (Figure 1, left). Interestingly, surviving CLP mice showed no inflammatory adrenal response, whereas nonsurvivors had elevated cytokine levels (Figure 1, right). Additionally, we identified a marked increase in apoptosis of both chromaffin and steroid-producing cells in adrenal glands obtained from mice with sepsis as compared with their controls (Figure 2).
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Conclusion: Taken together, sepsis-induced activation of the PRRs may contribute to adrenal impairment by enhancing tissue inflammation, oxidative stress and culminate in cellular apoptosis, while mortality seems to be associated with adrenal inflammation.
Background: Nerve injury induced protein 1 (Ninjurin 1 (Ninj1)) was first identified in Schwann cells and neurons contributing to cell adhesion and nerve regeneration. Recently, the role of Ninj1 has been linked to inflammatory processes in the central nervous system where functional repression reduced leukocyte infiltration and clinical disease activity during experimental autoimmune encephalomyelitis in mice [1]. But Ninj1 is also expressed outside the nervous system in various organs such as the liver and kidney as well as on leukocytes [2,3]. Therefore, we hypothesized that Ninj1 contributes to inflammation in general; that is, also outside the nervous system, with special interest in the pathogenesis of sepsis.
Methods: Ninj1 was repressed by transfecting HMEC-1 cells, a human dermal microvascular endothelial cell line with siRNA targeting Ninj1 (siNinj1) or a negative control (siC). Subsequently, cells were stimulated with 100 ng/ml LPS (TLR4 agonist), 3 μg/ml LTA (TLR2 agonist) or 100 n/ml poly(I:C) (TLR3 agonist) for 3 hours. The inflammatory response was analyzed by real-time PCR. In addition, transmigration of neutrophils across a HMEC-1 monolayer was measured using transwell plates (pore size 3 μm).
Results: Repression of Ninj1 by siRNA reduced Ninj1 mRNA expression in HMEC about 90% (Figure 1A). Reduced Ninj1 expression decreased neutrophil migration to 62.5% (Figure 1B) and TLR signaling. In detail, knockdown of Ninj1 significantly reduced TLR-2 and TLR-4 triggered expression of ICAM-1 and IL-6 (Figure 1C,D) while poly(I:C)-induced expression was only slightly reduced. To analyze a more specific TLR-3 target, we measured IP-10 mRNA expression, which was also significantly reduced in siNinj1-transfected cells (Figure 1E).
Conclusion: Our in vitro data strongly indicated that Ninj1 is involved in regulation of TLR signaling and therewith contributes to inflammation. In vivo experiments will clarify its impact on systemic inflammation.
Einleitung: Es wurden die Leistungen beim Verstehen im Störgeräusch von CI-Patienten mit unterschiedlichen Implantattypen verglichen. Der TEMPO+ Sprachprozessor (MED-EL, Implantat C40+) verwendet ein Mikrophon mit Kugelcharakteristik, während der ESPrit 3G Prozessor (COCHLEAR, Implantat CI24R(CA)) mit einem frontal ausgelegten Richtmikrophon ausgestattet ist.
Methode: Von den zwei untersuchten Patientengruppen (n=20) war eine mit einem C40+ Implantat (MED-EL, Innsbruck), die andere mit dem CI24RCA Implantat (Cochlear, Melbourne) versorgt. Es wurde die S0N180 Lautsprecheranordnung im Freifeld für den HSM-Test (Hochmair, Schulz und Moser, 1997) und die S0N0 Anordnung für den Oldenburger Satztest (Wagener, Kühnel und Kollmeier, 1999) verwendet. Der OLSA wurde mit festem Sprachpegel (65 dB SPL) und adaptivem Störgeräusch durchgeführt. Der HSM-Satztest wurde bei Signal-/ Rauschverhältnissen von 15 dB, 10 dB, 5 dB, 0dB sowie ohne Störgeräusch durchgeführt.
Ergebnisse: Im HSM-Satztest (S0N180) wurden signifikant bessere Leistungen beim Verstehen im Störgeräusch für die Gruppe mit dem Richtmikrophon nachgewiesen. Im Oldenburger Satztest zeigten sich keine signifikanten Unterschiede.
Schlussfolgerungen: Im Vergleich zu einem Mikrophon mit Kugelcharakteristik verbessert ein Richtmikrophon das Sprachverstehen in Situationen, in denen die Sprache frontal und der Störschall von hinten dargeboten werden.
TRENTOOL : an open source toolbox to estimate neural directed interactions with transfer entropy
(2011)
To investigate directed interactions in neural networks we often use Norbert Wiener's famous definition of observational causality. Wiener’s definition states that an improvement of the prediction of the future of a time series X from its own past by the incorporation of information from the past of a second time series Y is seen as an indication of a causal interaction from Y to X. Early implementations of Wiener's principle – such as Granger causality – modelled interacting systems by linear autoregressive processes and the interactions themselves were also assumed to be linear. However, in complex systems – such as the brain – nonlinear behaviour of its parts and nonlinear interactions between them have to be expected. In fact nonlinear power-to-power or phase-to-power interactions between frequencies are reported frequently. To cover all types of non-linear interactions in the brain, and thereby to fully chart the neural networks of interest, it is useful to implement Wiener's principle in a way that is free of a model of the interaction [1]. Indeed, it is possible to reformulate Wiener's principle based on information theoretic quantities to obtain the desired model-freeness. The resulting measure was originally formulated by Schreiber [2] and termed transfer entropy (TE). Shortly after its publication transfer entropy found applications to neurophysiological data. With the introduction of new, data efficient estimators (e.g. [3]) TE has experienced a rapid surge of interest (e.g. [4]). Applications of TE in neuroscience range from recordings in cultured neuronal populations to functional magnetic resonanace imaging (fMRI) signals. Despite widespread interest in TE, no publicly available toolbox exists that guides the user through the difficulties of this powerful technique. TRENTOOL (the TRansfer ENtropy TOOLbox) fills this gap for the neurosciences by bundling data efficient estimation algorithms with the necessary parameter estimation routines and nonparametric statistical testing procedures for comparison to surrogate data or between experimental conditions. TRENTOOL is an open source MATLAB toolbox based on the Fieldtrip data format. ...
The nervous system probably cannot display macroscopic quantum (i.e. classically impossible) behaviours such as quantum entanglement, superposition or tunnelling (Koch and Hepp, Nature 440:611, 2006). However, in contrast to this quantum "mysticism" there is an alternative way in which quantum events might influence the brain activity. The nervous system is a nonlinear system with many feedback loops at every level of its structural hierarchy. A conventional wisdom is that in macroscopic objects the quantum fluctuations are self-averaging and thus not important. Nevertheless this intuition might be misleading in the case of nonlinear complex systems. Because of a high sensitivity to initial conditions, in chaotic systems the microscopic fluctuations may be amplified upward and thereby affect the system’s output. In this way stochastic quantum dynamics might sometimes alter the outcome of neuronal computations, not by generating classically impossible solutions, but by influencing the selection of many possible solutions (Satinover, Quantum Brain, Wiley & Sons, 2001). I am going to discuss recent theoretical proposals and experimental findings in quantum mechanics, complexity theory and computational neuroscience suggesting that biological evolution is able to take advantage of quantum-computational speed-up. I predict that the future research on quantum complex systems will provide us with novel interesting insights that might be relevant also for neurobiology and neurophilosophy.
Market uptake of pegylated interferons for the treatment of hepatitis C in Europe : meeting abstract
(2008)
Introduction and Objectives Hepatitis C virus (HCV) infection is a leading cause of chronic liver disease with life threatening sequelae such as end-stage liver cirrhosis and liver cancer. It is estimated that the infection annually causes about 86,000 deaths, 1.2 million disability adjusted life years (DALYs), and ¼ of the liver transplants in the WHO European region. Presently, only antiviral drugs can prevent the progression to severe liver disease. Pegylated interferons combined with ribavirin are considered as current state-of-the-art treatment. Objective of this investigation was to assess the market uptake of these drugs across Europe in order to find out whether there is unequal access to optimised therapy. Material and Methods We used IMS launch and sales data (April 2000 to December 2005) for peginterferons and ribavirin for 21 countries of the WHO European region. Market uptake was investigated by comparing the development of country-specific sales rates. For market access analysis, we converted sales figures into numbers of treated patients and related those to country-specific hepatitis C prevalence. To convert sales figures into patient figures, the amount of active pharmaceutical ingredients (API) sold was divided by average total patient doses (ATPD), derived by a probability tree-based calculation algorithm accounting for genotype distribution, early stopping rules, body weight, unscheduled treatment stops and dose reductions Ntotal=APIPegIFNalpha-2a/ATPDPegIFNalpha-2a+APIPegIFN&alpha-2b/ATPDPegIFNalpha-2b For more concise result presentation the 21 included countries were aggregated into four categories: 1. EU founding members (1957): Belgium, France, Germany, Italy and Netherlands; 2. Countries joining EU before 2000: Austria (1995), Denmark (1973), Finland (1995), Greece (1981), Republic of Ireland (1973), Spain (1986), Sweden and UK (1973) 3. Countries joining EU after 2000: Czech Republic (2004), Hungary (2004), Poland (2004) and Romania (2007); 4. EU non-member states: Norway, Russia, Switzerland and Turkey. Results Market launch and market uptake of the investigated drugs differed considerably across countries. The earliest, most rapid and highest increases in sales rates were observed in the EU founding member states, followed by countries that joined the EU before 2000, countries that joined the EU after 2000, and EU non-member states. Most new EU member states showed a noticeable increase in sales after joining the EU. Market access analysis yielded that until end of 2005, about 308 000 patients were treated with peginterferon in the 21 countries. Treatment rates differed across Europe. The number of patients ever treated with peginterferon per 100 prevalent cases ranged from 16 in France to less than one in Romania, Poland, Greece and Russia. Discussion Peginterferon market uptake and prevalence adjusted treatment rates were found to vary considerably across 21 countries in the WHO European region suggesting unequal access to optimised therapy. Poor market access was especially common in low-resource countries. Besides budget restrictions, national surveillance and prevention policy should be considered as explanations for market access variation. Although our results allowed for the ranking of countries in order of market access, no final conclusions on over- or undertreatment can be drawn, because the number of patients who really require antiviral treatment is unknown. Further research based on pan-European decision models is recommended to determine the fraction of not yet successfully treated but treatable patients among those ever diagnosed with HCV. ...