White matter brain structure predicts language performance and learning success.

Individual differences in the ability to process language have long been discussed. Much of the neural basis of these, however, is yet unknown. Here we investigated the relationship between long-range white matter connectivity of the brain, as revealed by diffusion tractography, and the ability to process syntactically complex sentences in the participants' native language as well as the improvement thereof by multiday training. We identified specific network motifs by singular value decomposition that indeed related white matter structural connectivity to individual language processing performance. First, for two such motifs, one in the left and one in the right hemisphere, their individual prevalence significantly predicted the individual language performance, suggesting an anatomical predisposition for the individual ability to process syntactically complex sentences. Both motifs comprise a number of cortical regions, but seem to be dominated by areas known for the involvement in working memory rather than the classical language network itself. Second, we identified another left hemispheric network motif, whose change of prevalence over the training period significantly correlated with the individual change in performance, thus reflecting training induced white matter plasticity. This motif comprises diverse cortical areas including regions known for their involvement in language processing, working memory and motor functions. The present findings suggest that individual differences in language processing and learning can be explained, in part, by individual differences in the brain's white matter structure. Brain structure may be a crucial factor to be considered when discussing variations in human cognitive performance, more generally.

On the Role of Arkypallidal and Prototypical Neurons for Phase Transitions in the External Pallidum.

The external pallidum (globus pallidus pars externa [GPe]) plays a central role for basal ganglia functions and dynamics and, consequently, has been included in most computational studies of the basal ganglia. These studies considered the GPe as a homogeneous neural population. However, experimental studies have shown that the GPe contains at least two distinct cell types (prototypical and arkypallidal cells). In this work, we provide in silico insight into how pallidal heterogeneity modulates dynamic regimes inside the GPe and how they affect the GPe response to oscillatory input. We derive a mean-field model of the GPe system from a microscopic spiking neural network of recurrently coupled prototypical and arkypallidal neurons. Using bifurcation analysis, we examine the influence of dopamine-dependent changes of intrapallidal connectivity on the GPe dynamics. We find that increased self-inhibition of prototypical cells can induce oscillations, whereas increased inhibition of prototypical cells by arkypallidal cells leads to the emergence of a bistable regime. Furthermore, we show that oscillatory input to the GPe, arriving from striatum, leads to characteristic patterns of cross-frequency coupling observed at the GPe. Based on these findings, we propose two different hypotheses of how dopamine depletion at the GPe may lead to phase-amplitude coupling between the parkinsonian beta rhythm and a GPe-intrinsic γ rhythm. Finally, we show that these findings generalize to realistic spiking neural networks of sparsely coupled Type I excitable GPe neurons.SIGNIFICANCE STATEMENT Our work provides (1) insight into the theoretical implications of a dichotomous globus pallidus pars externa (GPe) organization, and (2) an exact mean-field model that allows for future investigations of the relationship between GPe spiking activity and local field potential fluctuations. We identify the major phase transitions that the GPe can undergo when subject to static or periodic input and link these phase transitions to the emergence of synchronized oscillations and cross-frequency coupling in the basal ganglia. Because of the close links between our model and experimental findings on the structure and dynamics of prototypical and arkypallidal cells, our results can be used to guide both experimental and computational studies on the role of the GPe for basal ganglia dynamics in health and disease.

Ephaptic coupling in white matter fibre bundles modulates axonal transmission delays.

Axonal connections are widely regarded as faithful transmitters of neuronal signals with fixed delays. The reasoning behind this is that extracellular potentials caused by spikes travelling along axons are too small to have an effect on other axons. Here we devise a computational framework that allows us to study the effect of extracellular potentials generated by spike volleys in axonal fibre bundles on axonal transmission delays. We demonstrate that, although the extracellular potentials generated by single spikes are of the order of microvolts, the collective extracellular potential generated by spike volleys can reach several millivolts. As a consequence, the resulting depolarisation of the axonal membranes increases the velocity of spikes, and therefore reduces axonal delays between brain areas. Driving a neural mass model with such spike volleys, we further demonstrate that only ephaptic coupling can explain the reduction of stimulus latencies with increased stimulus intensities, as observed in many psychological experiments.

Modelling the effect of ephaptic coupling on spike propagation in peripheral nerve fibres.

Experimental and theoretical studies have shown that ephaptic coupling leads to the synchronisation and slowing down of spikes propagating along the axons within peripheral nerve bundles. However, the main focus thus far has been on a small number of identical axons, whereas realistic peripheral nerve bundles contain numerous axons with different diameters. Here, we present a computationally efficient spike propagation model, which captures the essential features of propagating spikes and their ephaptic interaction, and facilitates the theoretical investigation of spike volleys in large, heterogeneous fibre bundles. We first lay out the theoretical basis to describe how the spike in an active axon changes the membrane potential of a passive axon. These insights are then incorporated into the spike propagation model, which is calibrated with a biophysically realistic model based on Hodgkin-Huxley dynamics. The fully calibrated model is then applied to fibre bundles with a large number of axons and different types of axon diameter distributions. One key insight of this study is that the heterogeneity of the axonal diameters has a dispersive effect, and that a higher level of heterogeneity requires stronger ephaptic coupling to achieve full synchronisation between spikes.

A Mean-Field Description of Bursting Dynamics in Spiking Neural Networks with Short-Term Adaptation.

Bursting plays an important role in neural communication. At the population level, macroscopic bursting has been identified in populations of neurons that do not express intrinsic bursting mechanisms. For the analysis of phase transitions between bursting and non-bursting states, mean-field descriptions of macroscopic bursting behavior are a valuable tool. In this article, we derive mean-field descriptions of populations of spiking neurons and examine whether states of collective bursting behavior can arise from short-term adaptation mechanisms. Specifically, we consider synaptic depression and spike-frequency adaptation in networks of quadratic integrate-and-fire neurons. Analyzing the mean-field model via bifurcation analysis, we find that bursting behavior emerges for both types of short-term adaptation. This bursting behavior can coexist with steady-state behavior, providing a bistable regime that allows for transient switches between synchronized and nonsynchronized states of population dynamics. For all of these findings, we demonstrate a close correspondence between the spiking neural network and the mean-field model. Although the mean-field model has been derived under the assumptions of an infinite population size and all-to-all coupling inside the population, we show that this correspondence holds even for small, sparsely coupled networks. In summary, we provide mechanistic descriptions of phase transitions between bursting and steady-state population dynamics, which play important roles in both healthy neural communication and neurological disorders.

Action potential propagation and synchronisation in myelinated axons.

With the advent of advanced MRI techniques it has become possible to study axonal white matter non-invasively and in great detail. Measuring the various parameters of the long-range connections of the brain opens up the possibility to build and refine detailed models of large-scale neuronal activity. One particular challenge is to find a mathematical description of action potential propagation that is sufficiently simple, yet still biologically plausible to model signal transmission across entire axonal fibre bundles. We develop a mathematical framework in which we replace the Hodgkin-Huxley dynamics by a spike-diffuse-spike model with passive sub-threshold dynamics and explicit, threshold-activated ion channel currents. This allows us to study in detail the influence of the various model parameters on the action potential velocity and on the entrainment of action potentials between ephaptically coupled fibres without having to recur to numerical simulations. Specifically, we recover known results regarding the influence of axon diameter, node of Ranvier length and internode length on the velocity of action potentials. Additionally, we find that the velocity depends more strongly on the thickness of the myelin sheath than was suggested by previous theoretical studies. We further explain the slowing down and synchronisation of action potentials in ephaptically coupled fibres by their dynamic interaction. In summary, this study presents a solution to incorporate detailed axonal parameters into a whole-brain modelling framework.

Bumps and oscillons in networks of spiking neurons.

We study localized patterns in an exact mean-field description of a spatially extended network of quadratic integrate-and-fire neurons. We investigate conditions for the existence and stability of localized solutions, so-called bumps, and give an analytic estimate for the parameter range, where these solutions exist in parameter space, when one or more microscopic network parameters are varied. We develop Galerkin methods for the model equations, which enable numerical bifurcation analysis of stationary and time-periodic spatially extended solutions. We study the emergence of patterns composed of multiple bumps, which are arranged in a snake-and-ladder bifurcation structure if a homogeneous or heterogeneous synaptic kernel is suitably chosen. Furthermore, we examine time-periodic, spatially localized solutions (oscillons) in the presence of external forcing, and in autonomous, recurrently coupled excitatory and inhibitory networks. In both cases, we observe period-doubling cascades leading to chaotic oscillations.

Network mechanisms underlying the role of oscillations in cognitive tasks.

Oscillatory activity robustly correlates with task demands during many cognitive tasks. However, not only are the network mechanisms underlying the generation of these rhythms poorly understood, but it is also still unknown to what extent they may play a functional role, as opposed to being a mere epiphenomenon. Here we study the mechanisms underlying the influence of oscillatory drive on network dynamics related to cognitive processing in simple working memory (WM), and memory recall tasks. Specifically, we investigate how the frequency of oscillatory input interacts with the intrinsic dynamics in networks of recurrently coupled spiking neurons to cause changes of state: the neuronal correlates of the corresponding cognitive process. We find that slow oscillations, in the delta and theta band, are effective in activating network states associated with memory recall. On the other hand, faster oscillations, in the beta range, can serve to clear memory states by resonantly driving transient bouts of spike synchrony which destabilize the activity. We leverage a recently derived set of exact mean-field equations for networks of quadratic integrate-and-fire neurons to systematically study the bifurcation structure in the periodically forced spiking network. Interestingly, we find that the oscillatory signals which are most effective in allowing flexible switching between network states are not smooth, pure sinusoids, but rather burst-like, with a sharp onset. We show that such periodic bursts themselves readily arise spontaneously in networks of excitatory and inhibitory neurons, and that the burst frequency can be tuned via changes in tonic drive. Finally, we show that oscillations in the gamma range can actually stabilize WM states which otherwise would not persist.

A computational biomarker of idiopathic generalized epilepsy from resting state EEG.

Epilepsy is one of the most common serious neurologic conditions. It is characterized by the tendency to have recurrent seizures, which arise against a backdrop of apparently normal brain activity. At present, clinical diagnosis relies on the following: (1) case history, which can be unreliable; (2) observation of transient abnormal activity during electroencephalography (EEG), which may not be present during clinical evaluation; and (3) if diagnostic uncertainty occurs, undertaking prolonged monitoring in an attempt to observe EEG abnormalities, which is costly. Herein, we describe the discovery and validation of an epilepsy biomarker based on computational analysis of a short segment of resting-state (interictal) EEG. Our method utilizes a computer model of dynamic networks, where the network is inferred from the extent of synchrony between EEG channels (functional networks) and the normalized power spectrum of the clinical data. We optimize model parameters using a leave-one-out classification on a dataset comprising 30 people with idiopathic generalized epilepsy (IGE) and 38 normal controls. Applying this scheme to all 68 subjects we find 100% specificity at 56.7% sensitivity, and 100% sensitivity at 65.8% specificity. We believe this biomarker could readily provide additional support to the diagnostic process.

Subcellular distribution of FTY720 and FTY720-phosphate in immune cells - another aspect of Fingolimod action relevant for therapeutic application.

FTY720 (Fingolimod; Gilenya®) is an immune-modulatory prodrug which, after intracellular phosphorylation by sphingosine kinase 2 (SphK2) and export, mimics effects of the endogenous lipid mediator sphingosine-1-phosphate. Fingolimod has been introduced to treat relapsing-remitting multiple sclerosis. However, little has been published about the immune cell membrane penetration and subcellular distribution of FTY720 and FTY720-P. Thus, we applied a newly established LC-MS/MS method to analyze the subcellular distribution of FTY720 and FTY720-P in subcellular compartments of spleen cells of wild type, SphK1- and SphK2-deficient mice. These studies demonstrated that, when normalized to the original cell volume and calculated on molar basis, FTY720 and FTY720-P dramatically accumulated several hundredfold within immune cells reaching micromolar concentrations. The amount and distribution of FTY720 was differentially affected by SphK1- and SphK2-deficiency. On the background of recently described relevant intracellular FTY720 effects in the nanomolar range and the prolonged application in multiple sclerosis, this data showing a substantial intracellular accumulation of FTY720, has to be considered for benefit/risk ratio estimates.

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.

Lack of microsomal prostaglandin E(2) synthase-1 in bone marrow-derived myeloid cells impairs left ventricular function and increases mortality after acute myocardial infarction.

BACKGROUND: Microsomal prostaglandin E(2) synthase-1 (mPGES-1), encoded by the Ptges gene, catalyzes prostaglandin E(2) biosynthesis and is expressed by leukocytes, cardiac myocytes, and cardiac fibroblasts. Ptges(-/-) mice develop more left ventricle (LV) dilation, worse LV contractile function, and higher LV end-diastolic pressure than Ptges(+/+) mice after myocardial infarction. In this study, we define the role of mPGES-1 in bone marrow-derived leukocytes in the recovery of LV function after coronary ligation. METHODS AND RESULTS: Cardiac structure and function in Ptges(+/+) mice with Ptges(+/+) bone marrow (BM(+/+)) and Ptges(+/+) mice with Ptges(-/-) BM (BM(-/-)) were assessed by morphometric analysis, echocardiography, and invasive hemodynamics before and 7 and 28 days after myocardial infarction. Prostaglandin levels and prostaglandin biosynthetic enzyme gene expression were measured by liquid chromatography-tandem mass spectrometry and real-time polymerase chain reaction, immunoblotting, immunohistochemistry, and immunofluorescence microscopy, respectively. After myocardial infarction, BM(-/-) mice had more LV dilation, worse LV systolic and diastolic function, higher LV end-diastolic pressure, more cardiomyocyte hypertrophy, and higher mortality but similar infarct size and pulmonary edema compared with BM(+/+) mice. BM(-/-) mice also had higher levels of COX-1 protein and more leukocytes in the infarct, but not the viable LV, than BM(+/+) mice. Levels of prostaglandin E(2) were higher in the infarct and viable myocardium of BM(-/-) mice than in BM(+/+) mice. CONCLUSIONS: Lack of mPGES-1 in bone marrow-derived leukocytes negatively regulates COX-1 expression, prostaglandin E(2) biosynthesis, and inflammation in the infarct and leads to impaired LV function, adverse LV remodeling, and decreased survival after acute myocardial infarction.

GTP cyclohydrolase and tetrahydrobiopterin regulate pain sensitivity and persistence.

We report that GTP cyclohydrolase (GCH1), the rate-limiting enzyme for tetrahydrobiopterin (BH4) synthesis, is a key modulator of peripheral neuropathic and inflammatory pain. BH4 is an essential cofactor for catecholamine, serotonin and nitric oxide production. After axonal injury, concentrations of BH4 rose in primary sensory neurons, owing to upregulation of GCH1. After peripheral inflammation, BH4 also increased in dorsal root ganglia (DRGs), owing to enhanced GCH1 enzyme activity. Inhibiting this de novo BH4 synthesis in rats attenuated neuropathic and inflammatory pain and prevented nerve injury-evoked excess nitric oxide production in the DRG, whereas administering BH4 intrathecally exacerbated pain. In humans, a haplotype of the GCH1 gene (population frequency 15.4%) was significantly associated with less pain following diskectomy for persistent radicular low back pain. Healthy individuals homozygous for this haplotype exhibited reduced experimental pain sensitivity, and forskolin-stimulated immortalized leukocytes from haplotype carriers upregulated GCH1 less than did controls. BH4 is therefore an intrinsic regulator of pain sensitivity and chronicity, and the GTP cyclohydrolase haplotype is a marker for these traits.

Anti-inflammatory role of microsomal prostaglandin E synthase-1 in a model of neuroinflammation.

A major immunological response during neuroinflammation is the activation of microglia, which subsequently release proinflammatory mediators such as prostaglandin E(2) (PGE(2)). Besides its proinflammatory properties, cyclooxygenase-2 (COX-2)-derived PGE(2) has been shown to exhibit anti-inflammatory effects on innate immune responses. Here, we investigated the role of microsomal PGE(2) synthase-1 (mPGES-1), which is functionally coupled to COX-2, in immune responses using a model of lipopolysaccharide (LPS)-induced spinal neuroinflammation. Interestingly, we found that activation of E-prostanoid (EP)2 and EP4 receptors, but not EP1, EP3, PGI(2) receptor (IP), thromboxane A(2) receptor (TP), PGD(2) receptor (DP), and PGF(2) receptor (FP), efficiently blocked LPS-induced tumor necrosis factor α (TNFα) synthesis and COX-2 and mPGES-1 induction as well as prostaglandin synthesis in spinal cultures. In vivo, spinal EP2 receptors were up-regulated in microglia in response to intrathecally injected LPS. Accordingly, LPS priming reduced spinal synthesis of TNFα, interleukin 1ß (IL-1ß), and prostaglandins in response to a second intrathecal LPS injection. Importantly, this reduction was only seen in wild-type but not in mPGES-1-deficient mice. Furthermore, intrathecal application of EP2 and EP4 agonists as well as genetic deletion of EP2 significantly reduced spinal TNFα and IL-1ß synthesis in mPGES-1 knock-out mice after LPS priming. These data suggest that initial inflammation prepares the spinal cord for a negative feedback regulation by mPGES-1-derived PGE(2) followed by EP2 activation, which limits the synthesis of inflammatory mediators during chronic inflammation. Thus, our data suggest a role of mPGES-1-derived PGE(2) in resolution of neuroinflammation.

Group V phospholipase A2 in bone marrow-derived myeloid cells and bronchial epithelial cells promotes bacterial clearance after Escherichia coli pneumonia.

Group V-secreted phospholipase A(2) (GV sPLA(2)) hydrolyzes bacterial phospholipids and initiates eicosanoid biosynthesis. Here, we elucidate the role of GV sPLA(2) in the pathophysiology of Escherichia coli pneumonia. Inflammatory cells and bronchial epithelial cells both express GV sPLA(2) after pulmonary E. coli infection. GV(-/-) mice accumulate fewer polymorphonuclear leukocytes in alveoli, have higher levels of E. coli in bronchoalveolar lavage fluid and lung, and develop respiratory acidosis, more severe hypothermia, and higher IL-6, IL-10, and TNF-α levels than GV(+/+) mice after pulmonary E. coli infection. Eicosanoid levels in bronchoalveolar lavage are similar in GV(+/+) and GV(-/-) mice after lung E. coli infection. In contrast, GV(+/+) mice have higher levels of prostaglandin D(2) (PGD(2)), PGF(2α), and 15-keto-PGE(2) in lung and express higher levels of ICAM-1 and PECAM-1 on pulmonary endothelial cells than GV(-/-) mice after lung infection with E. coli. Selective deletion of GV sPLA(2) in non-myeloid cells impairs leukocyte accumulation after pulmonary E. coli infection, and lack of GV sPLA(2) in either bone marrow-derived myeloid cells or non-myeloid cells attenuates E. coli clearance from the alveolar space and the lung parenchyma. These observations show that GV sPLA(2) in bone marrow-derived myeloid cells as well as non-myeloid cells, which are likely bronchial epithelial cells, participate in the regulation of the innate immune response to pulmonary infection with E. coli.

Methadone inhibits CYP2D6 and UGT2B7/2B4 in vivo: a study using codeine in methadone- and buprenorphine-maintained subjects.

AIMS: To compare the O-demethylation (CYP2D6-mediated), N-demethylation (CYP3A4-mediated) and 6-glucuronidation (UGT2B4/7-mediated) metabolism of codeine between methadone- and buprenorphine-maintained CYP2D6 extensive metabolizer subjects. METHODS: Ten methadone- and eight buprenorphine-maintained subjects received a single 60 mg dose of codeine phosphate. Blood was collected at 3 h and urine over 6 h and assayed for codeine, norcodeine, morphine, morphine-3- and -6-glucuronides and codeine-6-glucuronide. RESULTS: The urinary metabolic ratio for O-demethylation was significantly higher (P= 0.0044) in the subjects taking methadone (mean ± SD, 2.8 ± 3.1) compared with those taking buprenorphine (0.60 ± 0.43), likewise for 6-glucuronide formation (0.31 ± 0.24 vs. 0.053 ± 0.027; P < 0.0002), but there was no significant difference (P= 0.36) in N-demethylation. Similar changes in plasma metabolic ratios were also found. In plasma, compared with those maintained on buprenorphine, the methadone-maintained subjects had increased codeine and norcodeine concentrations (P < 0.004), similar morphine (P= 0.72) and lower morphine-3- and -6- and codeine-6-glucuronide concentrations (P < 0.008). CONCLUSION: Methadone is associated with inhibition of CYP2D6 and UGTs 2B4 and 2B7 reactions in vivo, even though it is not a substrate for these enzymes. Plasma morphine was not altered, owing to the opposing effects of inhibition of both formation and elimination; however, morphine-6-glucuronide (analgesically active) concentrations were substantially reduced. Drug interactions with methadone are likely to include drugs metabolized by various UGTs and CYP2D6.

Rate and oscillatory switching dynamics of a multilayer visual microcircuit model.

The neocortex is organized around layered microcircuits consisting of a variety of excitatory and inhibitory neuronal types which perform rate- and oscillation-based computations. Using modeling, we show that both superficial and deep layers of the primary mouse visual cortex implement two ultrasensitive and bistable switches built on mutual inhibitory connectivity motives between somatostatin, parvalbumin, and vasoactive intestinal polypeptide cells. The switches toggle pyramidal neurons between high and low firing rate states that are synchronized across layers through translaminar connectivity. Moreover, inhibited and disinhibited states are characterized by low- and high-frequency oscillations, respectively, with layer-specific differences in frequency and power which show asymmetric changes during state transitions. These findings are consistent with a number of experimental observations and embed firing rate together with oscillatory changes within a switch interpretation of the microcircuit.

Alterations in the hippocampal endocannabinoid system in diet-induced obese mice.

The endocannabinoid (eCB) system plays central roles in the regulation of food intake and energy expenditure. Its alteration in activity contributes to the development and maintenance of obesity. Stimulation of the cannabinoid receptor type 1 (CB(1) receptor) increases feeding, enhances reward aspects of eating, and promotes lipogenesis, whereas its blockade decreases appetite, sustains weight loss, increases insulin sensitivity, and alleviates dysregulation of lipid metabolism. The hypothesis has been put forward that the eCB system is overactive in obesity. Hippocampal circuits are not directly involved in the neuronal control of food intake and appetite, but they play important roles in hedonic aspects of eating. We investigated the possibility whether or not diet-induced obesity (DIO) alters the functioning of the hippocampal eCB system. We found that levels of the two eCBs, 2-arachidonoyl glycerol (2-AG) and anandamide, were increased in the hippocampus from DIO mice, with a concomitant increase of the 2-AG synthesizing enzyme diacylglycerol lipase-alpha and increased CB(1) receptor immunoreactivity in CA1 and CA3 regions, whereas CB(1) receptor agonist-induced [(35)S]GTPgammaS binding was unchanged. eCB-mediated synaptic plasticity was changed in the CA1 region, as depolarization-induced suppression of inhibition and long-term depression of inhibitory synapses were enhanced. Functionality of CB(1) receptors in GABAergic neurons was furthermore revealed, as mice specifically lacking CB(1) receptors on this neuronal population were partly resistant to DIO. Our results show that DIO-induced changes in the eCB system affect not only tissues directly involved in the metabolic regulation but also brain regions mediating hedonic aspects of eating and influencing cognitive processes.

Mean-field approximations of networks of spiking neurons with short-term synaptic plasticity.

Low-dimensional descriptions of spiking neural network dynamics are an effective tool for bridging different scales of organization of brain structure and function. Recent advances in deriving mean-field descriptions for networks of coupled oscillators have sparked the development of a new generation of neural mass models. Of notable interest are mean-field descriptions of all-to-all coupled quadratic integrate-and-fire (QIF) neurons, which have already seen numerous extensions and applications. These extensions include different forms of short-term adaptation considered to play an important role in generating and sustaining dynamic regimes of interest in the brain. It is an open question, however, whether the incorporation of presynaptic forms of synaptic plasticity driven by single neuron activity would still permit the derivation of mean-field equations using the same method. Here we discuss this problem using an established model of short-term synaptic plasticity at the single neuron level, for which we present two different approaches for the derivation of the mean-field equations. We compare these models with a recently proposed mean-field approximation that assumes stochastic spike timings. In general, the latter fails to accurately reproduce the macroscopic activity in networks of deterministic QIF neurons with distributed parameters. We show that the mean-field models we propose provide a more accurate description of the network dynamics, although they are mathematically more involved. Using bifurcation analysis, we find that QIF networks with presynaptic short-term plasticity can express regimes of periodic bursting activity as well as bistable regimes. Together, we provide novel insight into the macroscopic effects of short-term synaptic plasticity in spiking neural networks, as well as two different mean-field descriptions for future investigations of such networks.

Office work and stretch training (OST) study: effects on the prevalence of musculoskeletal diseases and gender differences: a non-randomised control study.

OBJECTIVES: For the prevention of musculoskeletal diseases (MSDs), stretch training can be a measure of the workplace health promotion (WHP) for office workers. This can lead to an increase in mobility and, ultimately, reduce or prevent MSD. The aim of the study was to examine a standardised and individualised stretch training on a device, specifically 'five Business', for the prevalence of MSD. DESIGN: This study is a non-randomised control study. SETTING: WHP programme with clerical employees of a German car manufacturer. PARTICIPANTS: 252 (110 women; 142 men) subjects (median age of 44 ([Formula: see text] 21 years) finished the study successfully. Inclusion criteria included a full-time employment in the office workplace and subjective health. INTERVENTION: The intervention group completed 22-24 training units of 10 min each on the 'five-Business' device two times a week for 12 weeks. PRIMARY AND SECONDARY OUTCOME MEASURES: Data were collected in the form of a pre-post study Nordic Questionnaire. RESULTS: After the intervention, significantly fewer subjects reported pain in the area of the neck (-17.79), shoulder (-11.28%), upper back (-14.7%), lower back (-12.78%) and feet (-8.51%). The gender analysis revealed that women are, in general, more often affected by musculoskeletal complaints than men, especially in the neck (+29.5%) and feet (+15.03%). Both sexes had significant reductions of MSD in the most commonly affected regions. Thus, 27.12% less women reported having neck pain, while 13.14% less men reported having low back pain. CONCLUSIONS: The results suggest that a stretching programme performed for 3 months can reduce musculoskeletal complaints in the most commonly affected areas in office workers. Both men and women benefited from the stretch training to a similar extent, suggesting that this would be a promising measure for therapy and prevention as part of WHP.