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Too hungry to sleep

Nature on Neuroscience - Wed, 12/19/2018 - 23:00

Too hungry to sleep

Too hungry to sleep, Published online: 20 December 2018; doi:10.1038/s41583-018-0116-y

Too hungry to sleep

Exploring phylogeny to find the function of sleep

Nature on Neuroscience - Wed, 12/19/2018 - 23:00

Exploring phylogeny to find the function of sleep

Exploring phylogeny to find the function of sleep, Published online: 20 December 2018; doi:10.1038/s41583-018-0098-9

The core function of sleep remains unclear. In this Opinion, Anafi, Kayser and Raizen give an overview of sleep states in various phyla and propose that the original function of sleep was likely metabolic.

Circadian blueprint of metabolic pathways in the brain

Nature on Neuroscience - Sun, 12/16/2018 - 23:00

Circadian blueprint of metabolic pathways in the brain

Circadian blueprint of metabolic pathways in the brain, Published online: 17 December 2018; doi:10.1038/s41583-018-0096-y

In addition to the central pacemaker, the mammalian brain contains additional circadian clocks. In this Review, Greco and Sassone–Corsi discuss how systemic homeostasis relies on the coordinated communication between these clocks.

Portraits of communication in neuronal networks

Nature on Neuroscience - Thu, 12/13/2018 - 23:00

Portraits of communication in neuronal networks

Portraits of communication in neuronal networks, Published online: 14 December 2018; doi:10.1038/s41583-018-0094-0

In this Opinion article, Hahn, Kumar and colleagues propose that synchrony- and oscillation-based communication between brain networks can be described by a common theoretical framework. They also suggest a mechanism for control of the flow of information in the brain through nesting of slow and fast oscillations.

Disarming the guards of change

Nature on Neuroscience - Thu, 12/13/2018 - 23:00

Disarming the guards of change

Disarming the guards of change, Published online: 14 December 2018; doi:10.1038/s41583-018-0114-0

Repetitive sensory input induces long-term potentiation of pyramidal cell synapses in mouse somatosensory cortex by activation of higher-order thalamic projections and disinhibition of local interneurons.

Author Correction: Epigenetic regulation in psychiatric disorders

Nature on Neuroscience - Wed, 12/12/2018 - 23:00

Author Correction: Epigenetic regulation in psychiatric disorders

Author Correction: Epigenetic regulation in psychiatric disorders, Published online: 13 December 2018; doi:10.1038/s41583-018-0089-x

Author Correction: Epigenetic regulation in psychiatric disorders

The neurobiological basis of narcolepsy

Nature on Neuroscience - Wed, 12/12/2018 - 23:00

The neurobiological basis of narcolepsy

The neurobiological basis of narcolepsy, Published online: 13 December 2018; doi:10.1038/s41583-018-0097-x

Narcolepsy is a sleep disorder caused by selective loss of orexin-producing neurons. Scammell and colleagues describe the functions of orexin neurons and the effects of their loss and review evidence implicating the immune system in the pathogenesis of the disorder.

When remembering is rewarding

Nature on Neuroscience - Wed, 12/05/2018 - 23:00

When remembering is rewarding

When remembering is rewarding, Published online: 06 December 2018; doi:10.1038/s41583-018-0110-4

In mice, long-term potentiation of hippocampal–nucleus accumbens synapses is required for the formation of reward-related memories.

Front cover

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IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Table of contents

Presents the table of contents for this issue of the publication.

Sleep Quality Estimation by Cardiopulmonary Coupling Analysis

The gold standard for assessment of sleep quality is the polysomnography, where physiological signals are used to generate both quantitative and qualitative measurements. Despite the production of highly accurate results, polysomnography is a complex, uncomfortable, and expensive process, inaccessible to a large group of the population. Home monitoring devices were developed to address these issues, fitting the growing perspective of health care and focusing on prevention and wellness. The objective of this paper was to develop an algorithm capable of estimating the quality of sleep, by analyzing the cyclic alternating pattern rate. The algorithm uses a single-lead electrocardiogram to produce a spectrographic measure of the cardiopulmonary coupling that in turn was fed to a classifier to estimate the non-rapid eye movement sleep and the presence of the cyclic alternating pattern. Two classifiers were tested, a feedforward neural network and a deeply stacked autoencoder, with the second achieving better results, correctly classifying 77% of the subjects sleep quality (either good or bad). The developed method can be implemented in a home monitoring device to estimate the sleep quality in a non-invasive way and improve the detection of pathologies.

Single-Finger Neural Basis Information-Based Neural Decoder for Multi-Finger Movements

In this paper, we investigate the relationship between single and multi-finger movements. By exploiting the neural correlation between the temporal firing patterns between movements, we show that the Pearson’s correlation coefficient for the physically related movement pairs are greater than those of others; the firing rates of the neurons that are tuned to a single-finger movements also increases when the corresponding multi-finger movements are instructed. We also use a hierarchical cluster analysis to verify not only the relationship between the single and multi-finger movements, but also the relationship between the flexion and extension movements. Furthermore, we propose a novel decoding method of modeling neural firing patterns while omitting the training process of the multi-finger movements. For the decoding, the Skellam and Gaussian probability distributions are used as mathematical models. The probabilistic distribution model of the multi-finger movements was estimated using the neural activity that was acquired during single-finger movements. As a result, the proposed neural decoding accuracy comparable with that of the supervised neural decoding accuracy when all of the neurons were used for the multi-finger movements. These results suggest that only the neural activities of single-finger movements can be exploited for the control of dexterous multi-finger neuroprosthetics.

Bayesian Optimized Spectral Filters Coupled With Ternary ECOC for Single-Trial EEG Classification

Motivated by the promising emergence of brain–computer interfaces (BCIs) within assistive/rehabilitative systems for therapeutic applications, this paper proposes a novel Bayesian framework that simultaneously optimizes a number of subject-specific filter banks and spatial filters. Optimized double-band spectro-spatial filters are derived based on common spatial patterns coupled with the error-correcting output coding (ECOC) classifiers. The proposed framework constructs optimized subject-specific spectral filters in an intuitive fashion resulting in creation of significantly discriminant features, which is a crucial requirement for any EEG-based BCI system. Through incorporation of the ECOC approach, the classification problem is then modeled as communication over a noisy channel where the misclassification error is corrected by error correction techniques borrowed from an information theory. This paper also proposes a modified version of the ECOC adopted to EEG classification problems by deploying ternary class codewords to increase the Hamming distance between the codewords and introduce more robustness to misclassification error. The proposed framework is evaluated over two different datasets from the BCI Competition (i.e., BCIC- $textit {IV}_{textsf {2}{a}}$ and BCIC- $textit {IV}_{textsf {2}{b}}$ ). The results indicate that the proposed approach outperforms its counterparts and validate the essential role of optimized spectral filters on the overall classification accuracy.

Predicting Microsleep States Using EEG Inter-Channel Relationships

A microsleep is a brief and an involuntary sleep-related loss of consciousness of up to 15 s. We investigated the performances of seven pairwise inter-channel relationships–covariance, Pearson’s correlation coefficient, wavelet cross-spectral power, wavelet coherence, joint entropy, mutual information, and phase synchronization index–in continuous prediction of microsleep states from EEG. These relationships were used as the feature sets of a linear discriminant analysis (LDA) and a linear support vector machine classifiers. Priors for both classifiers were incorporated to address the class imbalance in the training data sets. Each feature set was extracted from a 5-s window of EEG with the step of 0.25 s and was demeaned with respect to the mean of first 2 min. The sequential forward selection (SFS) method, based on a serial combination of the correlation coefficient, Fisher score-based filter, and an LDA-based wrapper, was used to select features from each training set. The comparison was based on 16-channel EEG data from eight subjects who had performed a 1-D visuomotor task for two 1-h sessions. The prediction performances were evaluated using leave-one-subject-out cross-validation. For both classifiers, non-normalized feature sets were found to perform better than normalized feature sets. Furthermore, demeaning the non-normalized features considerably improved the prediction performance. Overall, the LDA classifier with joint entropy features resulted in the best average prediction performances (phi, AUCPR, and AUCROC) of (0.47, 0.50, and 0.95). Joint entropy between O1 and O2 from theta frequency band was the most informative feature.

Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients

Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures. In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode de-composition (NA-MEMD). Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1–600 Hz), ripple (80–250 Hz), and fast-ripple (250–600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. However, the network phase-synchrony started to increase toward seizure end and achieved its maximum level at seizure offset for both types of epilepsy. This result suggests that hyper-synchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.

A New Unsupervised Detector of High-Frequency Oscillations in Accurate Localization of Epileptic Seizure Onset Zones

This paper presents a new unsupervised detector for automatically detecting high-frequency oscillations (HFOs) using intracranial electroencephalogram (iEEG) signals. This detector does not presuppose a specific number of clusters and has a good performance. First, the HFO candidates are detected by an initial detection method which distinguishes HFOs from background activities. Then, as significant features, fuzzy entropy, short-time energy, power ratio, and spectral centroid of the HFO candidates are investigated and constructed as a feature vector. Finally, the feature vector is used as the input of the fuzzy- ${c}$ -means-quantization-error-modeling-based expectation–maximization-Gaussian mixture model clustering algorithm. This algorithm has the advantages of detecting HFOs and avoiding false detection caused by artifacts. The concentrations of detected HFOs are used to localize epileptic seizure onset zones in epileptic iEEG signal analysis. A comparison shows that our detector provides better localization performance in terms of sensitivity and specificity than five existing detectors.

A Brain–Computer Interface-Based Action Observation Game That Enhances Mu Suppression

Action observation training based on the theory of activation of the mirror-neuron system has been used for the rehabilitation of patients with stroke. In this paper, we sought to assess whether a brain–computer interface (BCI)-based action observation rehabilitation game, using a flickering action video, could preferentially activate the mirror-neuron system. Feedback of stimulus observation, evoked by the flickering action video, was provided using steady state visually evoked potential and event-related desynchronization. Fifteen healthy subjects have experienced the game with BCI interaction (game and interaction), without BCI interaction (game without interaction), observed non-flickering stimuli, and flickering stimuli without the game background (stimuli only) in a counter-balanced order. The game and interface condition was resulted in significantly stronger activation of the mirror-neuron system than did the other three conditions. In addition, the amount of mirror-neuron system activation is gradually decreased in the game without interface, non-flickering stimuli, and stimuli only conditions in a time-dependent manner; however, in the game and interface condition, the amount of mirror-neuron system activation was maintained until the end of the training. Taken together, these data suggest that the proposed game paradigm, which integrates the action observation paradigm with BCI technology, could provide interactive responses for whether watching video clips can engage patients and enhance rehabilitation.

Progressive Thresholding: Shaping and Specificity in Automated Neurofeedback Training

Neurofeedback has long been proposed as a promising form of adjunctive non-pharmaceutical treatment for a variety of neuropsychological disorders. However, there is much debate over its efficacy and specificity. Many suggest that specificity can only be achieved when a specially trained clinician manually updates reward thresholds that indicate to the trainee when they are modulating their brain activity correctly, during training. We present a novel fully automated reward thresholding algorithm called progressive thresholding and test it with a frontal alpha asymmetry neurofeedback protocol. Progressive thresholding uses dynamic difficulty tuning and individual-specific progress models to simulate the shaping a clinician might perform when setting reward thresholds manually. We demonstrate in a double-blind comparison that progressive thresholding leads to significantly better learning outcomes compared with current automatic reward thresholding algorithms.

Assessment of the Complex Refractive Indices of Xenopus Laevis Sciatic Nerve for the Optimization of Optical (NIR) Neurostimulation

Despite an increasing interest in the use of light for neural stimulation, there is little information on how it interacts with neural tissue. The choice of wavelength in most of the optical stimulation literature is based on already available light sources designed for other applications. This paper is the first one to report the complex refractive index of the sciatic nerve of Xenopus laevis, which is a crucial parameter for identifying the optimal wavelength of optical stimuli. The Xenopus laevis neural tissue is the most widely used tissue type in peripheral neurostimulation studies. In this paper, the reflectance ( ${R}$ ) and the transmittance ( ${T}$ ) of the sciatic nerve were measured over a wavelength range of 860–2250 nm, and the corresponding real ( ${n}$ ) and the imaginary ( ${k}$ ) refractive indices were calculated using appropriate formulae in a novel way. The reported ${n}$ values were between 1.3–1.44 and the ${k}$ values are of the order of $textsf {10}^{-textsf {5}}$ over the full wavelength range. The absorption coefficient $alpha $ was found to be 100–500 cm $^{-1}$ . Several localized wavelength ranges were identified that can offer a maximized power coupling between potential opti- al stimuli and the neural tissue (1150–1200 nm, 1500–1700 nm, and 1900–2050 nm). The narrower regions of 1400–1600 nm and 1850–2150 nm were found to exhibit maximized absorbance. Separately, three regions were identified, where the penetration depths are the greatest (950–1000 nm, 1050–1350 nm, and 1600–1900 nm). This paper provides, for the first time, the fundamental specifications for optimizing the parameters of optical neurostimulation systems.

HIVE is supported by the European Commission under the Future and Emerging Technologies program.

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