Background: Fractions are known to be difficult for children and adults. Behavioral studies suggest that magnitude processing of fractions can be improved via number line estimation (NLE) trainings, but little is known about the neural correlates of fraction learning.

Method: To examine the neuro-cognitive foundations of fraction learning, behavioral performance and neural correlates were measured before and after a five-day NLE training.

Results: In all evaluation tasks behavioral performance increased after training. We observed a fronto-parietal network associated with number magnitude processing to be recruited in all tasks as indicated by a numerical distance effect. For symbolic fractions, the distance effect on intraparietal activation was only observed after training.

Conclusion: The absence of a distance effect of symbolic fractions before the training could indicate an initially less automatic access to their overall magnitude. NLE training facilitates processing of overall fraction magnitude as indicated by the distance effect in neural activation.

Objective: The aim of our study was to investigate how well Particle Image Velocimetry (PIV) measurements could serve Computational Fluid Dynamics (CFD) model validation for nasal airflow.

Material and methods: For the PIV measurements, a silicone model of the nose based on cone beam computed tomography (CBCT) scans of a patient was made. Corresponding CFD calculations were conducted with laminar and two turbulent models (k-ω and k-ω SST).

Results: CFD and PIV results corresponded well in our study. Especially, the correspondence of CFD calculations between the laminar and turbulent models was found to be even stronger. When comparing CFD with PIV, we found that the results were most convergent in the wider parts of the nasal cavities.

Conclusion: PIV measurements in realistically modelled nasal cavities succeed acceptably and CFD calculations produce corresponding results with PIV measurements. Greater model scaling is, however, necessary for better validations with PIV and comparisons of competing CFD models.

Measurement of salivary cortisol is a practical and non-invasive tool for studying stress reactivity to various types of stressors even in young infants. Whereas studies using physical stressors during the first months of life have found robust cortisol responses to painful stimuli, research with older infants using psychological stressors (e.g., parental separation) has produced mixed findings, limiting our understanding of potential developmental changes in cortisol reactivity across infancy. In the present study, we used meta-analysis to systematically investigate whether psychological stressor paradigms are associated with measurable cortisol responses in infants under 18 months of age and whether the magnitude of the responses is moderated by the type of psychological stressor (i.e., separation, frustration, novelty, or disruption of parental interaction), infant age, and other potential moderators. Across 47 studies (N = 4095, age range: 3–18 months), we found that commonly used psychological stressor paradigms are associated with a small (Hedges’ g = .11) increase in salivary cortisol levels in typically developing infants. Stressor type moderated the effect sizes, and when effect sizes in each category were analyzed separately, only the separation studies were associated with a consistent increase in cortisol following the stressor. Age did not moderate the effect sizes either in the full set of studies or within the separate stressor types. These meta-analytic results indicate that the normative cortisol response to psychological stressors across infancy is small and emphasize the need for standardized stressor paradigms to assess cortisol responses systematically across infancy.

Purpose: Myoclonus in progressive myoclonus epilepsy type 1 (EPM1) patients shows marked variability, which presents a substantial challenge in devising treatment and conducting clinical trials. Consequently, fast and objective myoclonus quantification methods are needed. Methods: Ten video-recorded unified myoclonus rating scale (UMRS) myoclonus with action tests were performed on EPM1 patients who were selected for the development and testing of the automatic myoclonus quantification method. Human pose and body movement analyses of the videos were used to identify body keypoints and further analyze movement smoothness and speed. The automatic myoclonus rating scale (ARMS) was developed. It included the jerk count during movement score and the log dimensionless jerk (LDLJ) score to evaluate changes in the smoothness of movement. Results: The scores obtained with the automatic analyses showed moderate to strong significant correlation with the UMRS myoclonus with action scores. The jerk count of the primary keypoints and the LDLJ scores were effective in the evaluation of the myoclonic jerks during hand movements. They also correlated moderately to strongly with the total UMRS test panel scores (r² = 0,77, P = 0,009 for the jerk count score and r² = 0,88, P = 0,001 for the LDLJ score). The automatic analyses was weaker in quantification of the neck, trunk, and leg myoclonus. Conclusion: Automatic quantification of myoclonic jerks using human pose and body movement analysis of patients’ videos is feasible and was found to be quite consistent with the accepted clinical gold standard quantification method. Based on the results of this study, the automatic analytical method should be further developed and validated to improve myoclonus severity follow-up for EPM1 patients.

Recent research in neuroscience indicates the importance of tripartite synapses and gliotransmission mediated by astrocytes in neuronal system modulation. Although the astrocyte and neuronal network functions are interrelated, they are fundamentally different in their signaling patterns and, possibly, the time scales at which they operate. However, the exact nature of gliotransmission and the effect of the tripartite synapse function at the network level are currently elusive. In this paper, we propose a computational model of interactions between an astrocyte network and a neuron network, starting from tripartite synapses and spanning to a joint network level. Our model focuses on a two-dimensional setup emulating a mixed in vitro neuron-astrocyte cell culture. The model depicts astrocyte-released gliotransmitters exerting opposing effects on the neurons: increasing the release probability of the presynaptic neuron while hyperpolarizing the post-synaptic one at a longer time scale. We simulated the joint networks with various levels of astrocyte contributions and neuronal activity levels. Our results indicate that astrocytes prolong the burst duration of neurons, while restricting hyperactivity. Thus, in our model, the effect of astrocytes is homeostatic; the firing rate of the network stabilizes to an intermediate level independently of neuronal base activity. Our computational model highlights the plausible roles of astrocytes in interconnected astrocytic and neuronal networks. Our simulations support recent findings in neurons and astrocytes in vivo and in vitro suggesting that astrocytic networks provide a modulatory role in the bursting of the neuronal network.

Synaptic neurotransmission has recently been proposed to function via either a membrane-independent or a membrane-dependent mechanism, depending on the neurotransmitter type. In the membrane-dependent mechanism, amphipathic neurotransmitters first partition to the lipid headgroup region and then diffuse along the membrane plane to their membrane-buried receptors. However, to date, this mechanism has not been demonstrated for any neurotransmitter-receptor complex. Here, we combined isothermal calorimetry measurements with a diverse set of molecular dynamics simulation methods to investigate the partitioning of an amphipathic neurotransmitter (dopamine) and the mechanism of its entry into the ligand-binding site. Our results show that the binding of dopamine to its receptor is consistent with the membrane-dependent binding and entry mechanism. Both experimental and simulation results showed that dopamine favors binding to lipid membranes especially in the headgroup region. Moreover, our simulations revealed a ligand-entry pathway from the membrane to the binding site. This pathway passes through a lateral gate between transmembrane alpha-helices 5 and 6 on the membrane-facing side of the protein. All in all, our results demonstrate that dopamine binds to its receptor by a membrane-dependent mechanism, and this is complemented by the more traditional binding mechanism directly through the aqueous phase. The results suggest that the membrane-dependent mechanism is common in other synaptic receptors, too.

We focus on electro-/magnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial and deep areas without specific a priori knowledge of the number or location of the activity. To enable source distinguishability for any depth, we introduce a randomized multiresolution scanning (RAMUS) approach in which the MAP estimate of the brain activity is varied during the reconstruction process. RAMUS aims to provide a robust and accurate imaging outcome for the whole brain, while maintaining the computational cost on an appropriate level. The inverse gamma (IG) distribution is applied as the primary hyperprior in order to achieve an optimal performance for the deep part of the brain. In this proof-of-the-concept study, we consider the detection of simultaneous thalamic and somatosensory activity via numerically simulated data modeling the 14-20 ms post-stimulus somatosensory evoked potential and field response to electrical wrist stimulation. Both a spherical and realistic model are utilized to analyze the source reconstruction discrepancies. In the numerically examined case, RAMUS was observed to enhance the visibility of deep components and also marginalizing the random effects of the discretization and optimization without a remarkable computation cost. A robust and accurate MAP estimate for the primary current density was obtained in both superficial and deep parts of the brain.

For the first time, impedance pneumography (IP) enables a continuous analysis of the tidal breathing flow volume (TBFV), overnight. We studied how corticosteroid inhalation treatments, sleep stage, and time from sleep onset modify the nocturnal TBFV profiles of children. Seventy children, 1–5 years old and with recurrent wheezing, underwent three, full-night TBFVs recordings at home, using IP. The first recorded one week before ending a 3-months inhaled corticosteroids treatment, and remaining two, 2 and 4 weeks after treatment. TBFV profiles were grouped by hour from sleep onset and estimated sleep stage. Compared with on-medication, the off-medication profiles showed lower volume at exhalation peak flow, earlier interruption of expiration, and less convex middle expiration. The differences in the first two features were significant during non-rapid eye movement (NREM), and the differences in the third were more prominent during REM after 4 h of sleep. These combinations of TBFV features, sleep phase, and sleep time potentially indicate airflow limitation in young children.

Spontaneous network activity plays a fundamental role in the formation of functional networks during early development. The landmark of this activity is the recurrent emergence of intensive time-limited network bursts (NBs) rapidly spreading across the entire dissociated culture in vitro. The main excitatory mediators of NBs are glutamatergic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) and N-Methyl-D-aspartic-acid receptors (NMDARs) that express fast and slow ion channel kinetics, respectively. The fast inhibition of the activity is mediated through gamma-aminobutyric acid type A receptors (GABA_ARs). Although the AMPAR, NMDAR and GABA_AR kinetics have been biophysically characterized in detail at the monosynaptic level in a variety of brain areas, the unique features of NBs emerging from the kinetics and the complex interplay of these receptors are not well understood. The goal of this study is to analyze the contribution of fast GABA_ARs on AMPAR- and NMDAR- mediated spontaneous NB activity in dissociated neonatal rat cortical cultures at 3 weeks in vitro. The networks were probed by both acute and gradual application of each excitatory receptor antagonist and combinations of acute excitatory and inhibitory receptor antagonists. At the same time, the extracellular network-wide activity was recorded with microelectrode arrays (MEAs). We analyzed the characteristic NB measures extracted from NB rate profiles and the distributions of interspike intervals, interburst intervals, and electrode recruitment time as well as the similarity of spatio-temporal patterns of network activity under different receptor antagonists. We show that NBs were rapidly initiated and recruited as well as diversely propagated by AMPARs and temporally and spatially maintained by NMDARs. GABA_ARs reduced the spiking frequency in AMPAR-mediated networks and dampened the termination of NBs in NMDAR-mediated networks as well as slowed down the recruitment of activity in all networks. Finally, we show characteristic super bursts composed of slow NBs with highly repetitive spatio-temporal patterns in gradually AMPAR blocked networks. To the best of our knowledge, this study is the first to unravel in detail how the three main mediators of synaptic transmission uniquely shape the NB characteristics, such as the initiation, maintenance, recruitment and termination of NBs in cortical cell cultures in vitro.

G protein-coupled receptors (GPCRs) control cellular signaling and responses. Many of these GPCRs are modulated by cholesterol and polyunsaturated fatty acids (PUFAs) which have been shown to co-exist with saturated lipids in ordered membrane domains. However, the lipid compositions of such domains extracted from the brain cortex tissue of individuals suffering from GPCR-associated neurological disorders show drastically lowered levels of PUFAs. Here, using free energy techniques and multiscale simulations of numerous membrane proteins, we show that the presence of the PUFA DHA helps helical multi-pass proteins such as GPCRs partition into ordered membrane domains. The mechanism is based on hybrid lipids, whose PUFA chains coat the rough protein surface, while the saturated chains face the raft environment, thus minimizing perturbations therein. Our findings suggest that the reduction of GPCR partitioning to their native ordered environments due to PUFA depletion might affect the function of these receptors in numerous neurodegenerative diseases, where the membrane PUFA levels in the brain are decreased. We hope that this work inspires experimental studies on the connection between membrane PUFA levels and GPCR signaling.

Purpose: Retinal explant cultures provide simplified systems where the functions of the retina and the effects of ocular therapies can be studied in an isolated environment. The purpose of this study was to provide insight into long-term preservation of retinal tissue in culture conditions, enable a deeper understanding of the interdependence of retinal morphology and function, and ensure the reliability of the explant technique for prolonged experiments. Methods: Retinal explants from adult mice were cultured as organotypic culture at the air-medium interface for 14 days in vitro (DIV). Retinal functionality was assessed by multielectrode array technique and morphology by immunohistochemical methods at several time points during culture. Results: Retinal explants retained viability for 14 DIV, although with diminishing neuronal activity, progressing neuronal loss, and increasing reactive gliosis. We recorded spontaneous retinal ganglion cell (RGC) activity up to 14 DIV with temporally changing distribution of RGC firing rates. Light responsiveness was measurable from RGCs for 7 DIV and from photoreceptors for 2 DIV. Apoptotic cells were detected beginning at 3 DIV with their density peaking at 7 DIV. The number of RGCs gradually decreased by 70% during 14 DIV. The change was accompanied by the loss of RGC functionality, resulting in 84% loss of electrically active RGCs. Conclusions: Retinal explants provide a valuable tool for studies of retinal functions and development of ocular therapies. However, critical for long-term use, retinal functionality was lost before structural loss, emphasizing a need for both functional and morphologic readouts to determine the overall state of the cultured retina.

Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability distribution for the skull conductivity that describes our (lack of) knowledge on its value, and model the effects of this uncertainty on EEG recordings with the help of an additive error term in the observation model. Before the Bayesian inference, the likelihood is marginalized over this error term. Thus, in the inversion we estimate only our primary unknown, the source distribution. We quantified the improvements in the source localization when the proposed Bayesian modelling was used in the presence of different skull conductivity errors and levels of measurement noise. Based on the results, BAE was able to improve the source localization accuracy, particularly when the unknown (true) skull conductivity was much lower than the expected standard conductivity value. The source locations that gained the highest improvements were shallow and originally exhibited the largest localization errors. In our case study, the benefits of BAE became negligible when the signal-to-noise ratio dropped to 20 dB.

Objectives: Diffusion tensor imaging (DTI) is sensitive technique to detect widespread changes in water diffusivity in the normal-appearing white matter (NAWM) that appears unaffected in conventional magnetic resonance imaging. We aimed to investigate the prognostic value and stability of DTI indices in the NAWM of the brain in an assessment of disability progression in patients with a relapsing-onset multiple sclerosis (MS). Methods: Forty-six MS patients were studied for DTI indices (fractional anisotropy (FA), mean diffusivity (MD), radial (RD), and axial (AD) diffusivity) in the NAWM of the corpus callosum (CC) and the internal capsule at baseline and at 1 year after. DTI analysis for 10 healthy controls was also performed at baseline. Simultaneously, focal brain lesion volume and atrophy measurements were done at baseline for MS patients. Associations between DTI indices, volumetric measurements, and disability progression over 4 years were studied by multivariate logistic regression analysis. Results: At baseline, most DTI metrics differed significantly between MS patients and healthy controls. There was tendency for associations between baseline DTI indices in the CC and disability progression (p < 0.05). Changes in DTI indices over 1 year were observed only in the CC (p < 0.008), and those changes were not found to predict clinical worsening over 4 years. Clear-cut association with disability progression was not detected for baseline volumetric measurements. Conclusion: Aberrant diffusivity measures in the NAWM of the CC may provide additional information for individual disability progression over 4 years in MS with the relapsing-onset disease. CC may be a good target for DTI measurements in monitoring disease activity in MS, and more studies are needed to assess the related prognostic potential.

The aim of this paper is to advance electroencephalography (EEG) source analysis using finite element method (FEM) head volume conductor models that go beyond the standard three compartment (skin, skull, brain) approach and take brain tissue inhomogeneity (gray and white matter and cerebrospinal fluid) into account. The new approach should enable accurate EEG forward modeling in the thin human cortical structures and, more specifically, in the especially thin cortices in children brain research or in pathological applications. The source model should thus be focal enough to be usable in the thin cortices, but should on the other side be more realistic than the current standard mathematical point dipole. Furthermore, it should be numerically accurate and computationally fast. We propose to achieve the best balance between these demands with a current preserving (divergence conforming) dipolar source model. We develop and investigate a varying number of current preserving source basis elements n (n=1,…,n=5). For validation, we conducted numerical experiments within a multi-layered spherical domain, where an analytical solution exists. We show that the accuracy increases along with the number of basis elements, while focality decreases. The results suggest that the best balance between accuracy and focality in thin cortices is achieved with n=4 (or in extreme cases even n=3) basis functions, while in thicker cortices n=5 is recommended to obtain the highest accuracy. We also compare the current preserving approach to two further FEM source modeling techniques, namely partial integration and St. Venant, and show that the best current preserving source model outperforms the competing methods with regard to overall balance. For all tested approaches, FEM transfer matrices enable high computational speed. We implemented the new EEG forward modeling approaches into the open source duneuro library for forward modeling in bioelectromagnetism to enable its broader use by the brain research community. This library is build upon the DUNE framework for parallel finite elements simulations and integrates with high-level toolboxes like FieldTrip. Additionally, an inversion test has been implemented using the realistic head model to demonstrate and compare the differences between the aforementioned source models.

Human astrocytes differ dramatically in cell morphology and gene expression from murine astrocytes. The latter are well known to be of major importance in the formation of neuronal networks by promoting synapse maturation. However, whether human astrocyte lineage cells have a similar role in network formation has not been firmly established. Here, we investigated the impact of human astrocyte lineage cells on the functional maturation of neural networks that were derived from human induced pluripotent stem cells (hiPSCs). Initial in vitro differentiation of hiPSC-derived neural progenitor cells and immature neurons (glia+ cultures) resulted in spontaneously active neural networks as indicated by synchronous neuronal Ca²⁺ transients. Depleting proliferating neural progenitors from these cultures by short-term antimitotic treatment resulted in strongly astrocyte lineage cell-depleted neuronal networks (glia− cultures). Strikingly, in contrast to glia+ cultures, glia− cultures did not exhibit spontaneous network activity. Detailed analysis of the morphological and electrophysiological properties of neurons by patch clamp recordings revealed reduced dendritic arborization in glia− cultures. In addition, a reduced action potential frequency upon current injection in pyramidal-like neurons was observed, whereas the electrical excitability of multipolar neurons was unaltered. Furthermore, we found a reduced dendritic density of PSD95-positive excitatory synapses, and more immature properties of AMPA (alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) miniature excitatory postsynaptic currents (mEPSCs) in glia− cultures, suggesting that the maturation of glutamatergic synapses depends on the presence of hiPSC-derived astrocyte lineage cells. Intriguingly, addition of the astrocyte-derived synapse maturation inducer cholesterol increased the dendritic density of PSD95-positive excitatory synapses in glia− cultures.

Atmospheric nanoparticles can be formed either via nucleation in atmosphere or be directly emitted to the atmosphere. In urban areas, several combustion sources (engines, biomass burning, power generation plants) are directly emitting nanoparticles to the atmosphere and, in addition, the gaseous emissions from the same sources can participate to atmospheric nanoparticle formation. This article focuses on the sources and formation of nanoparticles in traffic-influenced environments and reviews current knowledge on composition and characteristics of these nanoparticles. In general, elevated number concentrations of nanoparticles are very typically observed in traffic-influenced environments. Traffic related nanoparticles can originate from combustion process or from non-exhaust related sources such as brake wear. Particles originating from combustion process can be divided to three different sources; 1) primary nanoparticles formed in high temperature, 2) delayed primary particles formed as gaseous compounds nucleate during the cooling and dilution process and 3) secondary nanoparticles formed from gaseous precursors via the atmospheric photochemistry. The nanoparticles observed in roadside environment are a complex mixture of particles from several sources affected by atmospheric processing, local co-pollutants and meteorology.

Recent advances in robotics allow for collaboration between humans and machines in performing tasks at home or in industrial settings without harming the life of the user. While humans can easily adapt to each other and work in team, it is not as trivial for robots. In their case, interaction skills typically come at the cost of extensive programming and teaching. Besides, understanding the semantics of a task is necessary to work efficiently and react to changes in the task execution process. As a result, in order to achieve seamless collaboration, appropriate reasoning, learning skills and interaction capabilities are needed. For us humans, a cornerstone of our communication is language that we use to teach, coordinate and communicate. In this paper we thus propose a system allowing (i) to teach new action semantics based on the already available knowledge and (ii) to use natural language communication to resolve ambiguities that could arise while giving commands to the robot. Reasoning then allows new skills to be performed either autonomously or in collaboration with a human. Teaching occurs through a web application and motions are learned with physical demonstration of the robotic arm. We demonstrate the utility of our system in two scenarios and reflect upon the challenges that it introduces.

This article introduces the Zeffiro interface (ZI) version 2.2 for brain imaging. ZI aims to provide a simple, accessible and multimodal open source platform for finite element method (FEM) based and graphics processing unit (GPU) accelerated forward and inverse computations in the Matlab environment. It allows one to (1) generate a given multi-compartment head model, (2) to evaluate a lead field matrix as well as (3) to invert and analyze a given set of measurements. GPU acceleration is applied in each of the processing stages (1)–(3). In its current configuration, ZI includes forward solvers for electro-/magnetoencephalography (EEG) and linearized electrical impedance tomography (EIT) as well as a set of inverse solvers based on the hierarchical Bayesian model (HBM). We report the results of EEG and EIT inversion tests performed with real and synthetic data, respectively, and demonstrate numerically how the inversion parameters affect the EEG inversion outcome in HBM. The GPU acceleration was found to be essential in the generation of the FE mesh and the LF matrix in order to achieve a reasonable computing time. The code package can be extended in the future based on the directions given in this article.

Spontaneous pupillary fluctuations are indicative of the cognitive load imposed while doing a task involving memory resources. However, the fluctuations are also dependent on other factors like lighting conditions, uncertainty or the level of confidence while performing the task and so on. This paper aims to separate various components of pupillary response in order to assess the cognitive load and the confidence with which the task is performed. Hybrid decomposition models using ensemble empirical mode decomposition followed by independent component analysis is found to effectively reconstruct the original signal. The variational Mode Decomposition has been used in order to overcome the limitations imposed by empirical mode decomposition. Results show that variational mode decomposition outperforms existing state-of-the-art methods. Further, we attempted to identify the hidden components of pupillary response during cognitive tasks like mental addition and the anagram test. We obtained F_score of 0.67 in the detection of cognitive load and F_score of 0.99 for the detection of confidence level from the single channel pupil data.

A mutated KRAS protein is frequently observed in human cancers. Traditionally, the oncogenic properties of KRAS missense mutants at position 12 (G12X) have been considered as equal. Here, by assessing the probabilities of occurrence of all KRAS G12X mutations and KRAS dynamics we show that this assumption does not hold true. Instead, our findings revealed an outstanding mutational bias. We conducted a thorough mutational analysis of KRAS G12X mutations and assessed to what extent the observed mutation frequencies follow a random distribution. Unique tissue-specific frequencies are displayed with specific mutations, especially with G12R, which cannot be explained by random probabilities. To clarify the underlying causes for the nonrandom probabilities, we conducted extensive atomistic molecular dynamics simulations (170 μs) to study the differences of G12X mutations on a molecular level. The simulations revealed an allosteric hydrophobic signaling network in KRAS, and that protein dynamics is altered among the G12X mutants and as such differs from the wild-type and is mutation-specific. The shift in long-timescale conformational dynamics was confirmed with Markov state modeling. A G12X mutation was found to modify KRAS dynamics in an allosteric way, which is especially manifested in the switch regions that are responsible for the effector protein binding. The findings provide a basis to understand better the oncogenic properties of KRAS G12X mutants and the consequences of the observed nonrandom frequencies of specific G12X mutations.

The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of floating point operations. Several works have been developed to compress a pre-trained deep network to reduce memory footprint and, possibly, computation. Instead of compressing a pre-trained network, in this work, we propose a generic neural network layer structure employing multilinear projection as the primary feature extractor. The proposed architecture requires several times less memory as compared to the traditional Convolutional Neural Networks (CNN), while inherits the similar design principles of a CNN. In addition, the proposed architecture is equipped with two computation schemes that enable computation reduction or scalability. Experimental results show the effectiveness of our compact projection that outperforms traditional CNN, while requiring far fewer parameters.

Lipophilic neurotransmitters (NTs) such as dopamine are chemical messengers enabling neurotransmission by adhering onto the extracellular surface of the post-synaptic membrane in a synapse, followed by binding to their receptors. Previous studies have shown that the strength of the NT–membrane association is dependent on the lipid composition of the membrane. Negatively charged lipids such as phosphatidylserine, phosphatidylglycerol, and phosphatidic acid have been indicated to promote NT–membrane binding, however these anionic lipids reside almost exclusively in the intracellular leaflet of the post-synaptic membrane instead of the extracellular leaflet facing the synaptic cleft. Meanwhile, the extracellular leaflet is relatively rich in biologically relevant anionic gangliosides such as monosialotetrahexosylganglioside (GM1), yet the role of gangliosides in NT–membrane association is not clear. Here, we explored the role of GM1 in modulating the binding of dopamine and histamine (as amphipathic/cationic NTs) as well as acetylcholine (as a hydrophilic/cationic NT) with the post-synaptic membrane surface. Atomistic molecular dynamics simulations and free energy calculations indicated that GM1 fosters membrane association of histamine and dopamine. For acetylcholine, this effect was not observed. The in silico results suggest that gangliosides form a charge-based vestibule in front of the post-synaptic membrane, attracting amphipathic NTs to the vicinity of the membrane. The results also stress the importance to understand the significance of the structural details of NTs, as exemplified by the GM1–acetylcholine interaction. In a larger context, the NT–membrane adherence, coupled to lateral diffusion in the membrane plane, is proposed to improve neurotransmission efficiency by advancing NT entry into the membrane-embedded ligand-binding sites.

Objectives: The aim of the study was to compare the EEG findings and haemodynamic parameters of adult male patients while undergoing mask induction with sevoflurane anaesthesia with either controlled hyperventilation (CH) or spontaneous breathing (SB). Methods: Twenty male patients, aged 23–52 (mean 42) years were anaesthetized randomly with either spontaneous breathing or mild controlled hyperventilation via mask. EEG was recorded using a full 10–20 electrode set. Results: Anaesthesia induction with high inhaled concentrations of sevoflurane produced several epileptiform and periodic EEG patterns. CH doubled the amount of these EEG patterns compared to SB. Higher heart rate was recorded in the CH group. Conclusions: We describe a high incidence of paroxysmal EEG activity: epileptiform and generalized periodic discharges (GPDs) during rapid sevoflurane in nitrous oxide-oxygen mask induction in hyperventilated male patients. However these activities have no effect to the heart rate or the mean arterial pressure. Significance: The monitoring of GPDs and burst suppression patterns during rapid anaesthesia induction with sevoflurane provides possibility to study the effects of volatile anaesthetics in the healthy brain. In order to analyse the different sources of EEG patterns a wide-band multichannel EEG recording is necessary.

In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear recurrent neural networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time. At last, various different situations have been considered by setting different coefficient matrices of general time-varying LMEs and a great variety of computer simulations (including the application to robot manipulators) have been conducted to validate the better finite-time convergence of the proposed two nonlinear recurrent neural networks.

This paper aims at solving online equality-constrained quadratic programming problem, which is widely encountered in science and engineering, e.g., computer vision and pattern recognition, digital signal processing, and robotics. Recurrent neural networks such as conventional GradientNet and ZhangNet are considered as powerful solvers for such a problem in light of its high computational efficiency and capability of circuit realisation. In this paper, an improved primal recurrent neural network and its electronic implementation are proposed and analysed. Compared to the existing recurrent networks, i.e. GradientNet and ZhangNet, our network can theoretically guarantee superior global exponential convergence. Robustness performance of our such neural model is also analysed under a large model implementation error, with the upper bound of stead-state solution error estimated. Simulation results demonstrate theoretical analysis on the proposed model, which also verify the effectiveness of the proposed model for online equality-constrained quadratic programming.

The idea that astrocytes may be active partners in synaptic information processing has recently emerged from abundant experimental reports. Because of their spatial proximity to neurons and their bidirectional communication with them, astrocytes are now considered as an important third element of the synapse. Astrocytes integrate and process synaptic information and by doing so generate cytosolic calcium signals that are believed to reflect neuronal transmitter release. Moreover, they regulate neuronal information transmission by releasing gliotransmitters into the synaptic cleft affecting both pre- and postsynaptic receptors. Concurrent with the first experimental reports of the astrocytic impact on neural network dynamics, computational models describing astrocytic functions have been developed. In this review, we give an overview over the published computational models of astrocytic functions, from single-cell dynamics to the tripartite synapse level and network models of astrocytes and neurons.

Niemann-Pick Protein C2 (npc2) is a small soluble protein critical for cholesterol transport within and from the lysosome and the late endosome. Intriguingly, npc2-mediated cholesterol transport has been shown to be modulated by lipids, yet the molecular mechanism of npc2-membrane interactions has remained elusive. Here, based on an extensive set of atomistic simulations and free energy calculations, we clarify the mechanism and energetics of npc2-membrane binding and characterize the roles of physiologically relevant key lipids associated with the binding process. Our results capture in atomistic detail two competitively favorable membrane binding orientations of npc2 with a low interconversion barrier. The first binding mode (Prone) places the cholesterol binding pocket in direct contact with the membrane and is characterized by membrane insertion of a loop (V59-M60-G61-I62-P63-V64-P65). This mode is associated with cholesterol uptake and release. On the other hand, the second mode (Supine) places the cholesterol binding pocket away from the membrane surface, but has overall higher membrane binding affinity. We determined that bis(monoacylglycero)phosphate (bmp) is specifically required for strong membrane binding in Prone mode, and that it cannot be substituted by other anionic lipids. Meanwhile, sphingomyelin counteracts bmp by hindering Prone mode without affecting Supine mode. Our results provide concrete evidence that lipids modulate npc2-mediated cholesterol transport either by favoring or disfavoring Prone mode and that they impose this by modulating the accessibility of bmp for interacting with npc2. Overall, we provide a mechanism by which npc2-mediated cholesterol transport is controlled by the membrane composition and how npc2-lipid interactions can regulate the transport rate.

We propose a novel metric called MetrIntMeas (Metric for the Intelligence Measuring) for an accurate and robust measurement of the difficult problem-solving intelligence of a swarm system. The metric allows the classification if a swarm system belongs to the same class with the systems which have a specific reference intelligence value. For proving the efficiency of the proposed metric we realized a case study on a swarm system specialized in solving a NP-hard problem. As an application of the proposed metric, we present the measurement of the swarm systems’ evolution in intelligence. We gave a new definition to the intelligent evolving systems. The evolution of intelligent systems can be verified using the proposed MetrIntMeas metric.

The self-assembling of the amyloid β (Aβ) peptide into neurotoxic aggregates is considered a central event in the pathogenesis of Alzheimer's disease (AD). Based on the "amyloid hypothesis", many efforts have been devoted to designing molecules able to halt disease progression by inhibiting Aβ self-assembly. Here, we combine biophysical (ThT assays, TEM and AFM imaging), biochemical (WB and ESI-MS), and computational (all-atom molecular dynamics) techniques to investigate the capacity of four optically pure components of the natural product silymarin (silybin A, silybin B, 2,3-dehydrosilybin A, 2,3-dehydrosilybin B) to inhibit Aβ aggregation. Despite TEM analysis demonstrated that all the four investigated flavonoids prevent the formation of mature fibrils, ThT assays, WB and AFM investigations showed that only silybin B was able to halt the growth of small-sized protofibrils thus promoting the formation of large, amorphous aggregates. Molecular dynamics (MD) simulations indicated that silybin B interacts mainly with the C-terminal hydrophobic segment ³⁵MVGGVV⁴⁰ of Aβ40. Consequently to silybin B binding, the peptide conformation remains predominantly unstructured along all the simulations. By contrast, silybin A interacts preferentially with the segments ¹⁷LVFF²⁰ and ²⁷NKGAII³² of Aβ40 which shows a high tendency to form bend, turn, and β-sheet conformation in and around these two domains. Both 2,3-dehydrosilybin enantiomers bind preferentially the segment ¹⁷LVFF²⁰ but lead to the formation of different small-sized, ThT-positive Aβ aggregates. Finally, in vivo studies in a transgenic Caenorhabditis elegans strain expressing human Aβ indicated that silybin B is the most effective of the four compounds in counteracting Aβ proteotoxicity. This study underscores the pivotal role of stereochemistry in determining the neuroprotective potential of silybins and points to silybin B as a promising lead compound for further development in anti-AD therapeutics.

Background Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by adapting to the local firing rate they take into account all the time scales of a given dataset. New method In data containing multiple time scales (e.g. regular spiking and bursts) one is typically less interested in the smallest time scales and a more adaptive approach is needed. Here we propose the A-ISI-distance, the A-SPIKE-distance and A-SPIKE-synchronization, which generalize the original measures by considering the local relative to the global time scales. For the A-SPIKE-distance we also introduce a rate-independent extension called the RIA-SPIKE-distance, which focuses specifically on spike timing. Results The adaptive generalizations A-ISI-distance and A-SPIKE-distance allow to disregard spike time differences that are not relevant on a more global scale. A-SPIKE-synchronization does not any longer demand an unreasonably high accuracy for spike doublets and coinciding bursts. Finally, the RIA-SPIKE-distance proves to be independent of rate ratios between spike trains. Comparison with existing methods We find that compared to the original versions the A-ISI-distance and the A-SPIKE-distance yield improvements for spike trains containing different time scales without exhibiting any unwanted side effects in other examples. A-SPIKE-synchronization matches spikes more efficiently than SPIKE-synchronization. Conclusions With these proposals we have completed the picture, since we now provide adaptive generalized measures that are sensitive to firing rate only (A-ISI-distance), to timing only (ARI-SPIKE-distance), and to both at the same time (A-SPIKE-distance).

Hyaluronan is a polyanionic, megadalton-scale polysaccharide, which initiates cell signaling by interacting with several receptor proteins including CD44 involved in cell-cell interactions and cell adhesion. Previous studies of the CD44 hyaluronan binding domain have identified multiple widespread residues to be responsible for its recognition capacity. In contrast, the X-ray structural characterization of CD44 has revealed a single binding mode associated with interactions that involve just a fraction of these residues. In this study, we show through atomistic molecular dynamics simulations that hyaluronan can bind CD44 with three topographically different binding modes that in unison define an interaction fingerprint, thus providing a plausible explanation for the disagreement between the earlier studies. Our results confirm that the known crystallographic mode is the strongest of the three binding modes. The other two modes represent metastable configurations that are readily available in the initial stages of the binding, and they are also the most frequently observed modes in our unbiased simulations. We further discuss how CD44, fostered by the weaker binding modes, diffuses along HA when attached. This 1D diffusion combined with the constrained relative orientation of the diffusing proteins is likely to influence the aggregation kinetics of CD44. Importantly, CD44 aggregation has been suggested to be a possible mechanism in CD44-mediated signaling.

In this study, the dopamine-lipid bilayer interactions were probed with three physiologically relevant ion compositions using atomistic molecular dynamics simulations and free energy calculations. The in silico results indicate that calcium is able to decrease significantly the binding of dopamine to a neutral (zwitterionic) phosphatidylcholine lipid bilayer model mimicking the inner leaflet of a presynaptic vesicle. We argue that the observed calcium-induced effect is likely in crucial role in the neurotransmitter release from the presynaptic vesicles docked in the active zone of nerve terminals. The inner leaflets of presynaptic vesicles, which are responsible for releasing neurotransmitters into the synaptic cleft, are mainly composed of neutral lipids such as phosphatidylcholine and phosphatidylethanolamine. The neutrality of the lipid head group region, enhanced by a low pH level, should limit membrane aggregation of transmitters. In addition, the simulations suggest that the high calcium levels inside presynaptic vesicles prevent even the most lipophilic transmitters such as dopamine from adhering to the inner leaflet surface, thus rendering unhindered neurotransmitter release feasible.

Recent advances in image-based object recognition have exploited object proposals to speed up the detection process by reducing the search space. In this paper, we present a novel idea that utilizes true objectness and semantic image filtering (retrieved within the convolutional layers of a Convolutional Neural Network) to propose effective region proposals. Information learned in fully convolutional layers is used to reduce the number of proposals and enhance their localization by producing highly accurate bounding boxes. The greatest benefit of our method is that it can be integrated into any existing approach exploiting edge-based objectness to achieve consistently high recall across various intersection over union thresholds. Experiments on PASCAL VOC 2007 and ImageNet datasets demonstrate that our approach improves two existing state-of-the-art models with significantly high margins and pushes the boundaries of object proposal generation.

Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introducedan adaptive burst analysis methodwhich enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive burst detection. The main emphasis is, if network-wide adaptive burst detection can provide new insights into the assessment of network activity. Therefore, we propose a modification to the previously introduced inter-spike interval (ISI) histogram based cumulative moving average (CMA) algorithm to analyze multiple spike trains simultaneously. The network size can be freely defined, e.g., to include all the electrodes in a microelectrode array (MEA) recording. Additionally, the method can be applied on a series of measurements on the same network to pool the data for statistical analysis. Firstly, we apply both the original CMA-algorithm and our proposed network-wide CMA-algorithm on artificial spike trains to investigate how the modification changes the burst detection. Thereafter, we use the algorithms on MEA data of spontaneously active chemically manipulated in vitro rat cortical networks. Moreover, we compare the synchrony of the detected bursts introducing a new burst synchrony measure. Finally, we demonstrate howthe bursting statistics can be used to classify networks by applying k-means clustering to the bursting statistics. The results showthat the proposed network wide adaptive burst detection provides a method to unify the burst definition in the whole network and thus improves the assessment and classification of the neuronal activity, e.g., the effects of different pharmaceuticals. The results indicate that the novel method is adaptive enough to be usable on networks with different dynamics, and it is especially feasible when comparing the behavior of differently spiking networks, for example in developing networks.

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily-life situations. A new exploratory data-analysis approach, functional segmentation inter-subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable. FuSeISC was tested using fMRI data sets collected during traditional block-design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only useful for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but also has other potential applications, such as generation of a functional brain atlases including both lower- and higher-order processing areas. Finally, as a part of FuSeISC, a criterion-based sparsification of the shared nearest-neighbor graph was proposed for detecting clusters in noisy data. In the tests with synthetic data, this technique was superior to well-known clustering methods, such as Ward's method, affinity propagation, and K-means ++. Hum Brain Mapp 38:2643–2665, 2017.

Background Typically, live cell analyses are performed outside an incubator in an ambient air, where the lack of sufficient CO₂ supply results in a fast change of pH and the high evaporation causes concentration drifts in the culture medium. That limits the experiment time for tens of minutes. In many applications, e.g. in neurotoxicity studies, a prolonged measurement of extracellular activity is, however, essential. New method We demonstrate a simple cell culture chamber that enables stable culture conditions during prolonged extracellular recordings on a microelectrode array (MEA) outside an incubator. The proposed chamber consists of a gas permeable silicone structure that enables gas transfer into the chamber. Results We show that the culture chamber supports the growth of the human embryonic stem cell (hESC)-derived neurons both inside and outside an incubator. The structure provides very low evaporation, stable pH and osmolarity, and maintains strong signaling of hESC-derived neuronal networks over three-day MEA experiments. Comparison with existing methods Existing systems are typically complex including continuous perfusion of medium or relatively large amount of gas to supply. The proposed chamber requires only a supply of very low flow rate (1.5 ml/min) of non-humidified 5% CO₂ gas. Utilizing dry gas supply makes the proposed chamber simple to use. Conclusion Using the proposed culture structure on top of MEA, we can maintain hESC-derived neural networks over three days outside an incubator. Technically, the structure requires very low flow rate of dry gas supporting, however, low evaporation and maintaining the pH of the culture.

This paper explores advanced electrode modeling in the context of separate and parallel transcranial electrical stimulation (tES) and electroencephalography (EEG) measurements.We focus on boundary condition based approaches that do not necessitate adding auxiliary elements, e.g. sponges, to the computational domain. In particular, we investigate the complete electrode model (CEM) which incorporates a detailed description of the skin-electrode interface including its contact surface, impedance and normal current distribution. The CEM can be applied for both tES and EEG electrodes which is advantageous when a parallel system is used. In comparison to the CEM, we test two important reduced approaches: the gap model (GAP) and the point electrode model (PEM). We aim to find out the differences of these approaches for a realistic numerical setting based on the stimulation of the auditory cortex. The results obtained suggest, among other things, that GAP and GAP/PEM are sufficiently accurate for the practical application of tES and parallel tES/EEG, respectively. Differences between CEM and GAP were observed mainly in the skin compartment, where only CEM explains the heating effects characteristic to tES.

Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

Traumatic spinal cord injuries (SCIs) lead to axonal damage at the trauma site, as well as disconnections within the central nervous system. While the exact mechanisms of the long-term pathophysiological consequences of SCIs are not fully understood, it is known that neuronal damage and degeneration are not limited to the direct proximity of the trauma. Instead, the effects can be detected even in the cerebrum. We examined SCI-induced chronic brain changes with a case-control design using 32 patients and 70 control subjects. Whole-brain white matter (WM) tracts were assessed with diffusion tensor imaging (DTI). In addition, we analysed associations between DTI metrics and several clinical SCI variables. Whole-brain analyses were executed by tract-based spatial statistics (TBSS), with an additional complementary atlas-based analysis (ABA). We observed widespread, statistically significant (P≤0.01) changes similar to neural degeneration in SCI patients, both in the corticospinal tract (CST) and beyond. In addition, associations between DTI metrics and time since injury were found with TBSS and ABA, implying possible long-term post-injury neural regeneration. Using the ABA approach, we observed a correlation between SCI severity and DTI metrics, indicating a decrease in WM integrity along with patient sensory or motor scores. Our results suggest a widespread neurodegenerative effect of SCI within the cerebrum that is not limited to the motor pathways. Furthermore, DTI-measured WM integrity of chronic SCI patients seemed to improve as time elapsed since injury.

Functional neuroimaging studies have shown age-related differences in brain activation and connectivity patterns for emotional memory. Previous studies with middle-aged and older adults have reported associations between episodic memory and white matter (WM) microstructure obtained from diffusion tensor imaging, but such studies on emotional memory remain few. To our knowledge, this is the first study to explore associations between WM microstructure as measured by fractional anisotropy (FA) and recognition memory for intentionally encoded positive, negative, and emotionally neutral words using tract-based spatial statistics applied to diffusion tensor imaging images in an elderly sample (44 cognitively intact adults aged 50-79 years). The use of tract-based spatial statistics enables the identification of WM tracts important to emotional memory without a priori assumptions required for region-of-interest approaches that have been used in previous work. The behavioral analyses showed a positivity bias, that is, a preference for positive words, in recognition memory. No statistically significant associations emerged between FA and memory for negative or neutral words. Controlling for age and memory performance for negative and neutral words, recognition memory for positive words was negatively associated with FA in several projection, association, and commissural tracts in the left hemisphere. This likely reflects the complex interplay between the mnemonic positivity bias, structural WM integrity, and functional brain compensatory mechanisms in older age. Also, the unexpected directionality of the results indicates that the WM microstructural correlates of emotional memory show unique characteristics in normal older individuals.

Rhodopsin is a prototypical G-protein-coupled receptor (GPCR) that is activated when its 11-cis-retinal moiety is photoisomerized to all-trans retinal. This step initiates a cascade of reactions by which rods signal changes in light intensity. Like other GPCRs, rhodopsin is deactivated through receptor phosphorylation and arrestin binding. Full recovery of receptor sensitivity is then achievedwhenrhodopsin is regenerated through a series of steps that return the receptor to its ground state. Here, we show that dephosphorylation of the opsin moiety of rhodopsin is an extremely slow but requisite step in the restoration of the visual pigment to its ground state. We make use of a novel observation: isolated mouse retinae kept in standard media for routine physiologic recordings display blunted dephosphorylation of rhodopsin. Isoelectric focusing followed by Western blot analysis of bleached isolated retinae showed little dephosphorylation of rhodopsin for up to 4 h in darkness, even under conditions when rhodopsin was completely regenerated. Microspectrophotometeric determinations of rhodopsin spectra show that regenerated phospho-rhodopsin has the same molecular photosensitivity as unphosphorylated rhodopsin and that flash responses measured by trans-retinal electroretinogram or single-cell suction electrode recording displayed dark-adapted kinetics. Single quantal responses displayed normal dark-adapted kinetics, but rods were only half as sensitive as those containing exclusively unphosphorylated rhodopsin. We propose a model in which light-exposed retinae contain a mixed population of phosphorylated and unphosphorylated rhodopsin. Moreover, complete dark adaptation can only occur when all rhodopsin has been dephosphorylated, a process that requires >3 h in complete darkness.

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer’s disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Inter-subject correlation (ISC) is a widely used method for analyzing functional magnetic resonance imaging (fMRI) data acquired during naturalistic stimuli. A challenge in ISC analysis is to define the required sample size in the way that the results are reliable. We studied the effect of the sample size on the reliability of ISC analysis and additionally addressed the following question: How many subjects are needed for the ISC statistics to converge to the ISC statistics obtained using a large sample? The study was realized using a large block design data set of 130 subjects. We performed a split-half resampling based analysis repeatedly sampling two nonoverlapping subsets of 10-65 subjects and comparing the ISC maps between the independent subject sets. Our findings suggested that with 20 subjects, on average, the ISC statistics had converged close to a large sample ISC statistic with 130 subjects. However, the split-half reliability of unthresholded and thresholded ISC maps improved notably when the number of subjects was increased from 20 to 30 or more.

Human SH-SY5Y neuroblastoma cells maintain their potential for differentiation and regression in culture conditions. The induction of differentiation could serve as a strategy to inhibit cell proliferation and tumor growth. Previous studies have shown that differentiation of SH-SY5Y cells can be induced by all-trans-retinoic-acid (RA) and cholesterol (CHOL). However, signaling pathways that lead to terminal differentiation of SH-SY5Y cells are still largely unknown. The goal of this study was to examine in the RA and CHOL treated SH-SY5Y cells the additive impacts of estradiol (E₂) and brain-derived neurotrophic factor (BDNF) on cell morphology, cell population growth, synaptic vesicle recycling and presence of neurofilaments. The above features indicate a higher level of neuronal differentiation. Our data show that treatment for 10 days in vitro (DIV) with RA alone or when combined with E₂ (RE) or CHOL (RC), but not when combined with BDNF (RB), significantly (p < 0.01) inhibited the cell population growth. Synaptic vesicle recycling, induced by high-K⁺ depolarization, was significantly increased in all treatments where RA was included (RE, RC, RB, RCB), and when all agents were added together (RCBE). Specifically, our results show for the first time that E₂ treatment can alone increase synaptic vesicle recycling in SH-SY5Y cells. This work contributes to the understanding of the ways to improve suppression of neuroblastoma cells’ population growth by inducing maturation and differentiation.

Background: The most common cause for Eustachian tube dilatory dysfunction is mucosal inflammation. The aim of this study was to validate a scale for Eustachian tube mucosal inflammation, based on digital video clips obtained during diagnostic rigid endoscopy. Methods: A previously described four-step scale for grading the degree of inflammation of the mucosa of the Eustachian tube lumen was used for this validation study. A tutorial for use of the scale, including static images and 10 second video clips, was presented to 26 clinicians with various levels of experience. Each clinician then reviewed 35 short digital video samples of Eustachian tubes from patients and rated the degree of inflammation. A subset of the clinicians performed a second rating of the same video clips at a subsequent time. Statistical analysis of the ratings provided inter- and intrarater reliability scores. Results: Twenty-six clinicians with various levels of experience rated a total of 35 videos. Thirteen clinicians rated the videos twice. The overall correlation coefficient for the rating of inflammation severity was relatively good (0.74, 95% confidence interval, 0.72-0.76). The intralevel correlation coefficient for intrarater reliability was high (0.86). For those who rated videos twice, the intralevel correlation coefficient improved after the first rating (0.73, to 0.76), but improvement was not statistically significant. Conclusion: The inflammation scale used for Eustachian tube mucosal inflammation is reliable and this scale can be used with a high level of consistency by clinicians with various levels of experience.

Background: Cerebral white matter lesions are one imaging surrogate for cerebral small vessel disease. These white matter lesions are associated with increased morbidity and mortality in both the general population and ischemic stroke patients. Aims: To investigate whether severe white matter lesions in a cohort of ischemic stroke patients are associated with fewer days spent at home and earlier permanent institutionalization.Methods: We included 391 consecutive patients aged 55-85 years with ischemic stroke admitted to the Helsinki University Central Hospital (the Stroke Aging Memory cohort) with a 21-year follow-up. Hospitalization and nursing home admissions were reviewed from national registers.white matter lesions were rated using magnetic resonance imaging performed three-months poststroke, dichotomized as none-to-moderate and severe. Kaplan-Meier plots log-rank and binary logistic regression (odds ratio) and Cox multivariable proportional hazards model were used to study the association of white matter lesions with days spent at home and the time of permanent institutionalization. Hazards and odds ratio with their 95% confidence intervals are reported.Results: Severe white matter lesions were associated with fewer days spent at home, and more frequent, and earlier permanent institutionalization (1487 vs. 2354 days; log-rank P <0·001). After adjusting for significant covariates from univariable analyses, severe white matter lesions were associated with fewer days spent at home (odds ratio 1·62; confidence interval 1·16-2·25), permanent institutionalization within five-years (odds ratio 2·29; confidence interval 1·23-4·29), and increased hazards ratio of permanent institutionalization during 21 years of follow-up (1·64; confidence interval 1·119-2·26).Conclusions: After ischemic stroke, patients with severe white matter lesions spend fewer days at home and become permanently institutionalized earlier, especially within the first five-years.

Objective The activation of autoreactive T cells is a major event in the initiation of autoimmune responses in multiple sclerosis (MS). In addition to the T cell receptor stimulation, optimal activation of T cells requires various costimulatory molecules, such as CD26 and CD30, which has not been extensively studied in MS. Our aim was to explore whether the circulating levels of CD26 and CD30 in sera are associated with MS subtypes, inflammatory disease activity and disability in MS patients. Methods The study included 195 participants: 39 relapsing-remitting MS patients, 19 secondary-progressive MS patients, 19 clinically isolated syndrome patients, 58 controls for sCD26 analysis and 60 for sCD30 analysis. The levels of sCD26 and sCD30 in sera were analyzed using enzyme-linked immunosorbent assay, and the levels of interleukin-10, tumor necrosis factor-α and interferon-γ were analyzed with the Luminex assay. Results We observed increased levels of sCD26 and sCD30 in relapsing-remitting MS, secondary-progressive MS, and clinically isolated syndrome patients compared with the controls (P <0.05). Furthermore, elevated levels of sCD30 were noticed in treated relapsing-remitting MS patients than in untreated patients (P = 0.016), and also in converted CIS patients than in unconverted patients (P = 0.009). Although sCD26 and sCD30 could not associate with clinical measures, such as the disability score or disease activity, the levels of sCD30 correlated positively with interleukin-10 levels (r = 0.583, P <0.0001) and sCD26 levels (r = 0.262, P = 0.046) in MS patients. Conclusion The present results suggest that the elevated levels of sCD30 are associated with the regulatory immune responses predisposing to clinically stable phase of MS.

Patients treated with deep brain stimulation (DBS) provide an opportunity to study affective processes in humans with "lesion on demand" at key nodes in the limbic circuitries, such as at the anterior thalamic nuclei (ANT). ANT has been suggested to play a role in emotional control with its connection to the orbitofrontal cortex and the anterior cingulate cortex. However, direct evidence for its role in emotional function in human subjects is lacking. Reported side effects of ANT-DBS in the treatment of refractory epilepsy include depression related symptoms. In line with these mood-related clinical side effects, we have previously reported that stimulating the anterior thalamus increased emotional interference in a visual attention task as indicated by prolonged reaction times due to threat-related emotional distractors. We used event-related potentials to investigate potential attentional mechanism behind this behavioural observation. We hypothesized that ANT-DBS leads to greater attention capture by threat-related distractors. We tested this hypothesis using centro-parietal N2-P3 peak-to-peak amplitude as a measure of allocated attentional resources. Six epileptic patients treated with deep brain stimulation at ANT participated in the study. Electroencephalography was recorded while the patients performed a computer based Executive-Reaction Time test with threat-related emotional distractors. During the task, either ANT or a thalamic control location was stimulated, or the stimulation was turned off. Stimulation of ANT was associated with increased centro-parietal N2-P3 amplitude and increased reaction time in the context of threat-related emotional distractors. We conclude that high frequency electric stimulation of ANT leads to greater attentional capture by emotional stimuli. This is the first study to provide direct evidence from human subjects with on-line electric manipulation of ANT for its role in emotion-attention interaction.

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static “snapshots” of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing “gold standard” measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a “knowledge commons,” which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

In this paper, we propose an extension of the Extreme Learning Machine algorithm for Single-hidden Layer Feedforward Neural network training that incorporates Dropout and DropConnect regularization in its optimization process. We show that both types of regularization lead to the same solution for the network output weights calculation, which is adopted by the proposed DropELM network. The proposed algorithm is able to exploit Dropout and DropConnect regularization, without computationally intensive iterative weight tuning. We show that the adoption of such a regularization approach can lead to better solutions for the network output weights. We incorporate the proposed regularization approach in several recently proposed ELM algorithms and show that their performance can be enhanced without requiring much additional computational cost.

We study distance-based classification of human actions and introduce a new metric learning approach based on logistic discrimination for the determination of a low-dimensional feature space of increased discrimination power. We argue that for effective distance-based classification, both the optimal projection space and the optimal class representation should be determined. We qualitatively and quantitatively illustrate the superiority of the proposed approach to metric learning approaches employing the class mean for class representation. We also introduce extensions of the proposed metric learning approach to allow for richer class representations and to operate in arbitrary-dimensional Hilbert spaces for non-linear feature extraction and classification. Experimental results denote that the performance of the proposed distance-based classification schemes is comparable (or even better) to that of Support Vector Machine classifier (in both the linear and kernel cases) which is currently the standard choice for human action recognition.

Cone-beam computed tomography (CBCT) plays a key role in cochlear implantation in both planning implantation before surgery and quality control during surgery due to the high spatial resolution and convenience of application in the operation theater. We recently designed a novel, highresolution cone-beam acquisition system that has been tested in temporal bones with cochlear implantation to identify the scalar localization of the electrode arrays. The current study aimed to verify the reliability of the experimental CBCT set-up using high-resolution invitro X-ray microtomography (μCT) imaging as a reference. Nine human temporal bones were studied by inserting a straight electrode of a cochlear implant using the round window approach followed by sequential imaging using experimental CBCT and μCT with and without 1% iodine as the contrast agent. In the CBCT images, the electrodes were located in the scala tympani and near the lateral wall in all temporal bones. In the μCT images, the cochlear fine structures, including Reissner's membrane, stria vascularis, spiral ligament, basilar membrane, spiral limbus, osseous spiral lamina, and Rosenthal's canal that hosts the spiral ganglion cells, were clearly delineated; the electrode array avoided the lateral wall of the scala tympani in the hook region and then ran along the lateral wall of the scala tympani without any exception, a feature that was also detected in a temporal bone with ruptures in the basilar and Reissner's membranes. In conclusion, the current invitro μCT imaging system produced high-quality images that could demonstrate the fine cochlear structures faithfully and verify the reliability of a novel experimental CBCT set-up aimed for clinical application in identifying the scalar localization of the electrode array. The straight electrode is safe for cochlear structures with low risk of translocation and is suitable for atraumatic implantation, although a large gap between the contacts and the modiolus exists.

Objective In relapsing-remitting MS (RRMS) patients treated with natalizumab, the low level of L-selectin-expressing CD4+ T cells has been associated with the risk of progressive multifocal leukoencephalopathy (PML). In this study, our aim was to correlate the levels of soluble L-selectin and the anti-JCV antibody index in the sera of RRMS patients treated with natalizumab. Methods This study included 99 subjects, including 44 RRMS patients treated with natalizumab, 30 with interferon beta (IFN-β) and 25 healthy controls. The levels of soluble l-selectin (sL-selectin) in sera were measured by ELISA, and the anti-JC Virus (JCV) antibody index was determined by the second-generation ELISA (STRATIFY JCV^TM DxSelect^TM) assay. Results A significant correlation was found between the levels of sL-selectin and anti-JCV antibody indices in sera in the natalizumab-treated patients (r=0.402; p=0.007; n=44), but not in those treated with IFN-β. This correlation became even stronger in JCV seropositive patients treated with natalizumab for longer than 18 months (r=0.529; p=0.043; n=15). Conclusion The results support the hypothesis of sL-selectin being connected to the anti-JCV antibody index values and possibly cellular L-selectin. Measurement of serum sL-selectin should be evaluated further as a potential biomarker for predicting the risk of developing PML.

We developed a two-level statistical model that addresses the question of how properties of neurite morphology shape the large-scale network connectivity. We adopted a low-dimensional statistical description of neurites. From the neurite model description we derived the expected number of synapses, node degree, and the effective radius, the maximal distance between two neurons expected to form at least one synapse. We related these quantities to the network connectivity described using standard measures from graph theory, such as motif counts, clustering coefficient, minimal path length, and small-world coefficient. These measures are used in a neuroscience context to study phenomena from synaptic connectivity in the small neuronal networks to large scale functional connectivity in the cortex. For these measures we provide analytical solutions that clearly relate different model properties. Neurites that sparsely cover space lead to a small effective radius. If the effective radius is small compared to the overall neuron size the obtained networks share similarities with the uniform random networks as each neuron connects to a small number of distant neurons. Large neurites with densely packed branches lead to a large effective radius. If this effective radius is large compared to the neuron size, the obtained networks have many local connections. In between these extremes, the networks maximize the variability of connection repertoires. The presented approach connects the properties of neuron morphology with large scale network properties without requiring heavy simulations with many model parameters. The two-steps procedure provides an easier interpretation of the role of each modeled parameter. The model is flexible and each of its components can be further expanded. We identified a range of model parameters that maximizes variability in network connectivity, the property that might affect network capacity to exhibit different dynamical regimes.

High serum lipopolysaccharide (LPS) activity in normoalbuminuric patients with type 1 diabetes (T1D) predicts the progression of diabetic nephropathy (DN), but the mechanisms behind this remain unclear. We observed that treatment of cultured human podocytes with sera from normoalbuminuric T1D patients with high LPS activity downregulated 3-phosphoinositide-dependent kinase-1 (PDK1), an activator of the Akt cell survival pathway, and induced apoptosis. Knockdown of PDK1 in cultured human podocytes inhibited antiapoptotic Akt pathway, stimulated proapoptotic p38 MAPK pathway, and increased apoptosis demonstrating an antiapoptotic role for PDK1 in podocytes. Interestingly, PDK1 was downregulated in the glomeruli of diabetic rats and patients with type 2 diabetes before the onset of proteinuria, further suggesting that reduced expression of PDK1 associates with podocyte injury and development of DN. Treatment of podocytes in vitro and mice in vivo with LPS reduced PDK1 expression and induced apoptosis, which were prevented by inhibiting the Toll-like receptor (TLR) signaling pathway with the immunomodulatory agent GIT27. Our data show that LPS downregulates the cell survival factor PDK1 and induces podocyte apoptosis, and that blocking the TLR pathway with GIT27 may provide a non-nephrotoxic means to prevent the progression of DN.

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org. (C) 2015 Elsevier Inc. All rights reserved.

To analyse whether the expression of apoptotic transcripts is associated with the conversion from clinically isolated syndrome (CIS) to multiple sclerosis (MS). Eleven candidate transcripts belonging to the death receptor pathway, BCL-2, the inflammasome complex and NF-ΚB family were studied in the nonconverting and converting CIS patients during the four-year follow-up period. Conversion to MS was associated with marked variability in the expression of proapoptotic genes that were linked to TGF-B1 gene levels. The predominant expression of proapoptotic genes in patients with CIS suggests an increased potential to undergo apoptosis with the goal of terminating immune responses and regulating immune system homeostasis.

Background Deep brain stimulation (DBS) is a minimally invasive and reversible method to treat an increasing number of neurological and psychiatric disorders, including epilepsy. Targeting poorly defined deep structures is based in large degree on stereotactic atlas information, which may be a major source of inconsistent treatment effects. Aim of the study In the present study, we aimed to study whether a recently approved target for epilepsy (anterior nucleus of thalamus, ANT) is visualized in clinically established 3 T MRI and whether ANT is delineated using intraoperative microelectrode recording (MER). We have especially focused on individual variation in the location of ANT in stereotactic space. We also aimed to demonstrate the role of individual variation in interpretation of MER data by projecting samples onto AC-PC (anterior and posterior commissure) and ANT-normalized coordinate systems. Methods Detailed analysis of ANT delineations in 3 T MRI short tau inversion recovery (STIR) images from eight patients undergoing DBS for refractory epilepsy was performed. Coronal and sagittal cross-sectional models of ANT were plotted in the AC-PC coordinate system to study individual variation. A total of 186 MER samples collected from 10 DBS trajectories and 5 patients were analyzed, and the location of each sample was calculated and corrected accordingly to the location of the final DBS electrode and projected to the AC-PC or coordinate system normalized to ANT. Results Most of the key structures in the anatomic atlas around ANT (mammillothalamic tract and external medullary lamina) were identified in STIR images allowing visual delineation of ANT. We observed a high degree of anatomical variation in the location of ANT, and the cross-sectional areas overlapped by study patients decreased in a linear fashion with an increasing number of patients. MER information from 10 individual trajectories correlated with STIR signal characteristics by demonstrating a spike-negative zone, presumably white matter layer, at the lateral aspect of ANT in ANT-normalized coordinate system as predicted by STIR images. However, MER information projected to the AC-PC coordinate system was not able to delineate ANT. Conclusions ANT is delineated in 3 T MRI by visualization of a thin white matter lamina between ANT and other nuclear groups that lack spiking activity. Direct targeting in the anterior thalamic area is superior to indirect targeting due to extensive individual variation in the location of ANT. Without detailed imaging information, however, a single trajectory MER has little localizing value.