Multi-modal cryo-EM reveals trimers of protein A10 to form the palisade layer in poxvirus cores.

Poxviruses are among the largest double-stranded DNA viruses, with members such as variola virus, monkeypox virus and the vaccination strain vaccinia virus (VACV). Knowledge about the structural proteins that form the viral core has remained sparse. While major core proteins have been annotated via indirect experimental evidence, their structures have remained elusive and they could not be assigned to individual core features.

How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex-dentate gyrus-CA3 network.

Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)-dentate gyrus (DG)-CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC-DG-CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV-INs) in the DG, is an efficient pattern separator.

MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo.

Background: To understand information coding in single neurons, it is necessary to analyze subthreshold synaptic events, action potentials (APs), and their interrelation in different behavioral states. However, detecting excitatory postsynaptic potentials (EPSPs) or currents (EPSCs) in behaving animals remains challenging, because of unfavorable signal-to-noise ratio, high frequency, fluctuating amplitude, and variable time course of synaptic events.

New Method: We developed a method for synaptic event detection, termed MOD (Machine-learning Optimal-filtering Detection-procedure), which combines concepts of supervised machine learning and optimal Wiener filtering.

Selective Routing of Spatial Information Flow from Input to Output in Hippocampal Granule Cells.

Dentate gyrus granule cells (GCs) connect the entorhinal cortex to the hippocampal CA3 region, but how they process spatial information remains enigmatic. To examine the role of GCs in spatial coding, we measured excitatory postsynaptic potentials (EPSPs) and action potentials (APs) in head-fixed mice running on a linear belt. Intracellular recording from morphologically identified GCs revealed that most cells were active, but activity level varied over a wide range.

Synaptic mechanisms of pattern completion in the hippocampal CA3 network.

The hippocampal CA3 region plays a key role in learning and memory. Recurrent CA3-CA3 synapses are thought to be the subcellular substrate of pattern completion. However, the synaptic mechanisms of this network computation remain enigmatic.

Stimfit: quantifying electrophysiological data with Python.

Intracellular electrophysiological recordings provide crucial insights into elementary neuronal signals such as action potentials and synaptic currents. Analyzing and interpreting these signals is essential for a quantitative understanding of neuronal information processing, and requires both fast data visualization and ready access to complex analysis routines. To achieve this goal, we have developed Stimfit, a free software package for cellular neurophysiology with a Python scripting interface and a built-in Python shell.

Sequential effects in continued visual search: using fixation-related potentials to compare distractor processing before and after target detection.

To search for a target in a complex environment is an everyday behavior that ends with finding the target. When we search for two identical targets, however, we must continue the search after finding the first target and memorize its location. We used fixation-related potentials to investigate the neural correlates of different stages of the search, that is, before and after finding the first target.

A deconvolution-based method with high sensitivity and temporal resolution for detection of spontaneous synaptic currents in vitro and in vivo.

Spontaneous postsynaptic currents (PSCs) provide key information about the mechanisms of synaptic transmission and the activity modes of neuronal networks. However, detecting spontaneous PSCs in vitro and in vivo has been challenging, because of the small amplitude, the variable kinetics, and the undefined time of generation of these events. Here, we describe a, to our knowledge, new method for detecting spontaneous synaptic events by deconvolution, using a template that approximates the average time course of spontaneous PSCs.

Review of the BCI Competition IV.

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students.

BioSig: the free and open source software library for biomedical signal processing.

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research.

Recent advances in modeling and analysis of bioelectric and biomagnetic sources.

Determining the centers of electrical activity in the human body and the connectivity between different centers of activity in the brain is an active area of research. To understand brain function and the nature of cardiovascular diseases requires sophisticated methods applicable to non-invasively measured bioelectric and biomagnetic data. As it is difficult to solve for all unknown parameters at once, several strains of data analysis have been developed, each trying to solve a different part of the problem and each requiring a different set of assumptions.

Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces.

Several feature types have been used with EEG-based Brain-Computer Interfaces. Among the most popular are logarithmic band power estimates with more or less subject-specific optimization of the frequency bands. In this paper we introduce a feature called Time Domain Parameter that is defined by the generalization of the Hjorth parameters.

Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces.

Many Brain-computer Interfaces (BCI) use band-power estimates with more or less subject-specific optimization of the frequency bands. However, a number of alternative EEG features do not need to select the frequency bands; estimators for these features have been modified for an adaptive use. The popular band power estimates were compared with Adaptive AutoRegressive parameters, Hjorth, Barlow, Wackermann, Brain-Rate and a new feature type called Time Domain Parameter.

Robustly estimating the flow direction of information in complex physical systems.

We propose a new measure (phase-slope index) to estimate the direction of information flux in multivariate time series. This measure (a) is insensitive to mixtures of independent sources, (b) gives meaningful results even if the phase spectrum is not linear, and (c) properly weights contributions from different frequencies. These properties are shown in extended simulations and contrasted to Granger causality which yields highly significant false detections for mixtures of independent sources.

Toward self-paced brain-computer communication: navigation through virtual worlds.

The self-paced control paradigm enables users to operate brain-computer interfaces (BCI) in a more natural way: no longer is the machine in control of the timing and speed of communication, but rather the user is. This is important to enhance the usability, flexibility, and response time of a BCI. In this work, we show how subjects, after performing cue-based feedback training (smiley paradigm), learned to navigate self-paced through the "freeSpace" virtual environment (VE).

Directed cortical information flow during human object recognition: analyzing induced EEG gamma-band responses in brain's source space.

The increase of induced gamma-band responses (iGBRs; oscillations >30 Hz) elicited by familiar (meaningful) objects is well established in electroencephalogram (EEG) research. This frequency-specific change at distinct locations is thought to indicate the dynamic formation of local neuronal assemblies during the activation of cortical object representations. As analytically power increase is just a property of a single location, phase-synchrony was introduced to investigate the formation of large-scale networks between spatially distant brain sites.

Study of discriminant analysis applied to motor imagery bipolar data.

We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research.

Analyzing event-related EEG data with multivariate autoregressive parameters.

Methods of spatio-temporal analysis provide important tools for characterizing several dynamic aspects of brain oscillations that are reflected in the human scalp-detected electroencephalogram (EEG). The search to identify the dynamic connectivity of brain signals within different frequency bands, in order to uncover the transient cooperation between different brain sites, converges at the potential of multivariate autoregressive (MVAR) models and their derived parameters. In fact, MVAR parameters provide a whole battery of so-called coupling measures including classic coherence (COH), partial coherence (pCOH), imaginary part of coherence (iCOH), partial-directed coherence (PDC), directed transfer function (DTF), and full frequency directed transfer function (ffDTF).

The BCI competition. III: Validating alternative approaches to actual BCI problems.

A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands.

BCI Meeting 2005--workshop on BCI signal processing: feature extraction and translation.

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.

Characterization of four-class motor imagery EEG data for the BCI-competition 2005.

To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering.

Hemodynamic and autonomic changes induced by Ironman: prediction of competition time by blood pressure variability.

We hypothesized that the extreme endurance exercise of an Ironman competition would lead to long-standing hemodynamic and autonomic changes. We investigated also the possibility of predicting competition performance from baseline hemodynamic and autonomic parameters. We have investigated 27 male athletes before competition, 1 h after, and then for the following week after the competition.

An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24 x 7 utilizing the Siesta database.

To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel.