Chemical activation of pre-Bötzinger complex in vivo reduces respiratory network complexity

In the in vivo anesthetized adult cat model, multiple patterns of inspiratory motor discharge have been recorded in response to chemical stimulation and focal hypoxia of the pre-B?tzinger complex (pre-B?tC), suggesting that this region may participate in the generation of complex respiratory dynamics. The complexity of a signal can be quantified using approximate entropy (ApEn) and multiscale entropy (MSEn) methods, both of which measure the regularity (orderliness) in a time series, with the latter method taking into consideration temporal fluctuations in the underlying dynamics. The current investigation was undertaken to examine the effects of pre-B?tC-induced excitation of phasic phrenic nerve discharge, which is characterized by high-amplitude, rapid-rate-of-rise, short-duration bursts, on the complexity of the central inspiratory neural controller in the vagotomized, chloralose-anesthetized adult cat model. To assess inspiratory neural network complexity, we calculated the ApEn and MSEn of phrenic nerve bursts during eupneic (basal) discharge and during pre-B?tC-induced excitation of phasic inspiratory bursts. Chemical stimulation of the pre-B?tC using DL-homocysteic acid (DLH; 10 mM; 10-20 nl; n=10) significantly reduced the ApEn from 0.982+/-0.066 (mean+/-SE) to 0.664+/-0.067 (P<0.001) followed by recovery ( approximately 1-2 min after DLH) of the ApEn to 1.014+/-0.067; a slightly enhanced magnitude reduction in MSEn was observed. Focal pre-B?tC hypoxia (induced by sodium cyanide; NaCN; 1 mM; 20 nl; n=2) also elicited a reduction in both ApEn and MSEn, similar to those observed for the DLH-induced response. These observations demonstrate that activation of the pre-B?tC reduces inspiratory network complexity, suggesting a role for the pre-B?tC in regulation of complex respiratory dynamics.     

A review of EEG and MEG for brainnetome research

The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain’s multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new –ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG’s applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.     

一种改进的近似熵——样品熵及其在颞叶癫痫患者脑电信号分析中的应用

采用了近似熵(approximately entropy,ApEn)和它的改进算法,即样品熵(sample entropy,SampEn)分析了8位颞叶癫痫患者和10位健康人员的短程脑电信号。在计算过程中使用了两种滑动窗口和5个不同的过滤标准r。结果显示颞叶癫痫患者组脑电信号的熵值显著低于健康组,而且患者癫痫病灶所在的脑半球的复杂度远远小于非癫痫病灶的脑半球。小的滑动窗口能更多地反映与癫痫发作相关的细节。对于1秒的滑动窗口,过滤标准r不能小于时间序列标准差的0．15％;而对于4秒的滑动窗口,则过滤标准r不能小于时间序列标准差的10％。研究结果表明,在短程脑电信号的非线性分析中,样品熵是一种比近似熵更为可靠的非线性分析方法。颞叶癫痫患者脑电信号的熵值低于健康人员,这可能表明脑电活动的非线性程度的降低是由于神经信号在大脑内的传递受到了阻碍或者损坏,使得神经信号成了相对孤立的信息源。     

Multimodal functional network connectivity: an EEG-fMRI fusion in network space

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.     

Dynamics on Networks: The Role of Local Dynamics and Global Networks on the Emergence of Hypersynchronous Neural Activity

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

Artificial neural networks and the study of evolution of prey coloration

In this paper, I investigate the use of artificial neural networks in the study of prey coloration. I briefly review the anti-predator functions of prey coloration and describe both in general terms and with help of two studies as specific examples the use of neural network models in the research on prey coloration. The first example investigates the effect of visual complexity of background on evolution of camouflage. The second example deals with the evolutionary choice of defence strategy, crypsis or aposematism. I conclude that visual information processing by predators is central in evolution of prey coloration. Therefore, the capability to process patterns as well as to imitate aspects of predator's information processing and responses to visual information makes neural networks a well-suited modelling approach for the study of prey coloration. In addition, their suitability for evolutionary simulations is an advantage when complex or dynamic interactions are modelled. Since not all behaviours of neural network models are necessarily biologically relevant, it is important to validate a neural network model with empirical data. Bringing together knowledge about neural networks with knowledge about topics of prey coloration would provide a potential way to deepen our understanding of the specific appearances of prey coloration.     

健康人不同生理状态下的脑电近似熵的观测

目的：应用近似熵（ＡｐＥｎ）研究不同生理状态下脑电图非线性动力学特性。方法：在一组健康人４０例中,进行五种生理状态的脑电图记录：闭眼安静;睁眼安静;看图;听短纯音;闭眼数数目１００￣７,并计算各种状态的近似熵。结果：闭眼安静状态额区（Ｆ３,Ｆ４）的ＡｐＥｎ最高,枕区（Ｏ１,Ｏ２）最低,睁眼状态所有脑区的ＡｐＥｎ均增高,不同的生理刺激任务对ＥＥＧ ＡｐＥｎ产生不同的影响,其中额区的影响最大。结论：Ａ     

Down syndrome’s brain dynamics: analysis of fractality in resting state

To the best knowledge of the authors there is no study on nonlinear brain dynamics of down syndrome (DS) patients, whereas brain is a highly complex and nonlinear system. In this study, fractal dimension of EEG, as a key characteristic of brain dynamics, showing irregularity and complexity of brain dynamics, was used for evaluation of the dynamical changes in the DS brain. The results showed higher fractality of the DS brain in almost all regions compared to the normal brain, which indicates less centrality and higher irregular or random functioning of the DS brain regions. Also, laterality analysis of the frontal lobe showed that the normal brain had a right frontal laterality of complexity whereas the DS brain had an inverse pattern (left frontal laterality). Furthermore, the high accuracy of 95.8 % obtained by enhanced probabilistic neural network classifier showed the potential of nonlinear dynamic analysis of the brain for diagnosis of DS patients. Moreover, the results showed that the higher EEG fractality in DS is associated with the higher fractality in the low frequencies (delta and theta), in broad regions of the brain, and the high frequencies (beta and gamma), majorly in the frontal regions.     

Intrinsic activity in the fly brain gates visual information during behavioral choices

The small insect brain is often described as an input/output system that executes reflex-like behaviors. It can also initiate neural activity and behaviors intrinsically, seen as spontaneous behaviors, different arousal states and sleep. However, less is known about how intrinsic activity in neural circuits affects sensory information processing in the insect brain and variability in behavior. Here, by simultaneously monitoring Drosophila's behavioral choices and brain activity in a flight simulator system, we identify intrinsic activity that is associated with the act of selecting between visual stimuli. We recorded neural output (multiunit action potentials and local field potentials) in the left and right optic lobes of a tethered flying Drosophila, while its attempts to follow visual motion (yaw torque) were measured by a torque meter. We show that when facing competing motion stimuli on its left and right, Drosophila typically generate large torque responses that flip from side to side. The delayed onset (0.1-1 s) and spontaneous switch-like dynamics of these responses, and the fact that the flies sometimes oppose the stimuli by flying straight, make this behavior different from the classic steering reflexes. Drosophila, thus, seem to choose one stimulus at a time and attempt to rotate toward its direction. With this behavior, the neural output of the optic lobes alternates; being augmented on the side chosen for body rotation and suppressed on the opposite side, even though the visual input to the fly eyes stays the same. Thus, the flow of information from the fly eyes is gated intrinsically. Such modulation can be noise-induced or intentional; with one possibility being that the fly brain highlights chosen information while ignoring the irrelevant, similar to what we know to occur in higher animals.     

Temporal Dynamics of Visual Attention Measured with Event-Related Potentials

How attentional modulation on brain activities determines behavioral performance has been one of the most important issues in cognitive neuroscience. This issue has been addressed by comparing the temporal relationship between attentional modulations on neural activities and behavior. Our previous study measured the time course of attention with amplitude and phase coherence of steady-state visual evoked potential (SSVEP) and found that the modulation latency of phase coherence rather than that of amplitude was consistent with the latency of behavioral performance. In this study, as a complementary report, we compared the time course of visual attention shift measured by event-related potentials (ERPs) with that by target detection task. We developed a novel technique to compare ERPs with behavioral results and analyzed the EEG data in our previous study. Two sets of flickering stimulus at different frequencies were presented in the left and right visual hemifields, and a target or distracter pattern was presented randomly at various moments after an attention-cue presentation. The observers were asked to detect targets on the attended stimulus after the cue. We found that two ERP components, P300 and N2pc, were elicited by the target presented at the attended location. Time-course analyses revealed that attentional modulation of the P300 and N2pc amplitudes increased gradually until reaching a maximum and lasted at least 1.5 s after the cue onset, which is similar to the temporal dynamics of behavioral performance. However, attentional modulation of these ERP components started later than that of behavioral performance. Rather, the time course of attentional modulation of behavioral performance was more closely associated with that of the concurrently recorded SSVEPs analyzed. These results suggest that neural activities reflected not by either the P300 or N2pc, but by the SSVEPs, are the source of attentional modulation of behavioral performance.     

Sustained neural rhythms reveal endogenous oscillations supporting speech perception

Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned (or “entrained”) to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) responses and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, we provide fundamental results for several lines of research—including neural entrainment and tACS—and reveal endogenous neural oscillations as a key underlying principle for speech perception.

Just as a child on a swing continues to move after the pushing stops, this study reveals similar entrained rhythmic echoes in brain activity after hearing speech and electrical brain stimulation; perturbation with tACS shows that these brain oscillations help listeners to understand speech.     

Reaction time and interhemispheric interaction

The study was made on healthy adult subjects. The reaction time of the hand (RT) was measured under two conditions: 1) the choice of reaction (right or left hand) is determined by the nature of the warning stimulus; 2) decision on the choice is taken, depending on the second, trigger stimulus. Stimuli are presented at random sequences to different visual fields. The reaction time to the visual signal presented to the visual field ipsilateral to the hand is significantly shorter (by 15 to 26 msec) than to the stimulus in the contralateral visual field. In a simple motor reaction, when no discrimination of trigger stimulus and the decision on the choice of reaction is required, a hemispheric asymmetry of reaction time is manifested: the left hemisphere only responds differently to direct visual stimulation and to that mediated through the contralateral hemisphere.     

Fronto-Central Theta Oscillations Are Related to Oscillations in Saccadic Response Times (SRT): An EEG and Behavioral Data Analysis

The phase reset hypothesis states that the phase of an ongoing neural oscillation, reflecting periodic fluctuations in neural activity between states of high and low excitability, can be shifted by the occurrence of a sensory stimulus so that the phase value become highly constant across trials (Schroeder et al., 2008). From EEG/MEG studies it has been hypothesized that coupled oscillatory activity in primary sensory cortices regulates multi sensory processing (Senkowski et al. 2008). We follow up on a study in which evidence of phase reset was found using a purely behavioral paradigm by including also EEG measures. In this paradigm, presentation of an auditory accessory stimulus was followed by a visual target with a stimulus-onset asynchrony (SOA) across a range from 0 to 404 ms in steps of 4 ms. This fine-grained stimulus presentation allowed us to do a spectral analysis on the mean SRT as a function of the SOA, which revealed distinct peak spectral components within a frequency range of 6 to 11 Hz with a modus of 7 Hz. The EEG analysis showed that the auditory stimulus caused a phase reset in 7-Hz brain oscillations in a widespread set of channels. Moreover, there was a significant difference in the average phase at which the visual target stimulus appeared between slow and fast SRT trials. This effect was evident in three different analyses, and occurred primarily in frontal and central electrodes.     

Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process

Background

Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed.

Methodology/Principal Findings

We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d'') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30–60 Hz) and alpha (8–14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing.

Conclusions/Significance

We conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.     

How birds use their eyes: Opposite left-right specialization for the lateral and frontal visual hemifield in the domestic chick

Recent evidence has demonstrated that, in animals with laterally placed eyes, functional cerebral asymmetry is revealed by preferential use of either the left or right eye in a range of behaviors (birds: [1, 2, 3]; fish: [4, 5]; reptiles: [6, 7]). These findings pose a theoretical problem. It seems that there would be disadvantages in having a substantial degree of asymmetry in the use of the two eyes; a deficit on one side would leave the organism vulnerable to attack on that side or unable to exploit resources appearing on one side. We here report a possible solution to the problem. We have found that domestic chicks show selective use of the lateral visual field of the left eye and of the right hemifield in the binocular, frontal visual field when they peck at strangers but not at cagemates. Thus, during social recognition, there seems to be opposite and complementary left-right specialization for the lateral and frontal visual fields of the two eyes. These findings can reconcile the computational advantages associated with asymmetry of the left and right sides of the brain with the ecological demands for an animal to perceive and respond equally well to the left and right sides of its midline.     

Leopard frog priorities in choosing between prey at different locations

Frogs are able to respond to a prey stimulus throughout their 360° ground-level visual field as well as in the superior visual field. We compared the likelihood of frogs choosing between a more nasally located, ground-level prey versus a more temporally located ground-level prey, when the prey at the nasal location is further away from the frog. Two crickets were presented simultaneously at 9 pairs of angles that included both crickets in the binocular visual field, both crickets in the monocular visual field, or one cricket in the binocular field and one in the monocular field. Frogs chose the more nasally located prey at least 71% of the time when the more temporal prey was in the monocular field; and 64% of the time when both prey were in the binocular field. Frogs tended to choose the more nasally located prey, even though it takes the frog longer to reach the prey. In addition, when given a choice between a prey located at ground level versus a prey located in the superior field, frogs tend to choose the prey at ground-level. These results suggest that there is a neural mechanism that biases frogs’ responses to prey stimuli.     

Long lasting modulation of cortical oscillations after continuous theta burst transcranial magnetic stimulation

Transcranial magnetic theta burst stimulation (TBS) differs from other high-frequency rTMS protocols because it induces plastic changes up to an hour despite lower stimulus intensity and shorter duration of stimulation. However, the effects of TBS on neuronal oscillations remain unclear. In this study, we used electroencephalography (EEG) to investigate changes of neuronal oscillations after continuous TBS (cTBS), the protocol that emulates long-term depression (LTD) form of synaptic plasticity. We randomly divided 26 healthy humans into two groups receiving either Active or Sham cTBS as control over the left primary motor cortex (M1). Post-cTBS aftereffects were assessed with behavioural measurements at rest using motor evoked potentials (MEPs) and at active state during the execution of a choice reaction time (RT) task in combination with continuous electrophysiological recordings. The cTBS-induced EEG oscillations were assessed using event-related power (ERPow), which reflected regional oscillatory activity of neural assemblies of θ (4-7.5 Hz), low α (8-9.5 Hz), μ (10-12.5 Hz), low β (13-19.5 Hz), and high β (20-30 Hz) brain rhythms. Results revealed 20-min suppression of MEPs and at least 30-min increase of ERPow modulation, suggesting that besides MEPs, EEG has the potential to provide an accurate cortical readout to assess cortical excitability and to investigate the interference of cortical oscillations in the human brain post-cTBS. We also observed a predominant modulation of β frequency band, supporting the hypothesis that cTBS acts more on cortical level. Theta oscillations were also modulated during rest implying the involvement of independent cortical theta generators over the motor network post cTBS. This work provided more insights into the underlying mechanisms of cTBS, providing a possible link between synchronised neural oscillations and LTD in humans.     

Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks

Cortical neural networks exhibit high internal variability in spontaneous dynamic activities and they can robustly and reliably respond to external stimuli with multilevel features–from microscopic irregular spiking of neurons to macroscopic oscillatory local field potential. A comprehensive study integrating these multilevel features in spontaneous and stimulus–evoked dynamics with seemingly distinct mechanisms is still lacking. Here, we study the stimulus–response dynamics of biologically plausible excitation–inhibition (E–I) balanced networks. We confirm that networks around critical synchronous transition states can maintain strong internal variability but are sensitive to external stimuli. In this dynamical region, applying a stimulus to the network can reduce the trial-to-trial variability and shift the network oscillatory frequency while preserving the dynamical criticality. These multilevel features widely observed in different experiments cannot simultaneously occur in non-critical dynamical states. Furthermore, the dynamical mechanisms underlying these multilevel features are revealed using a semi-analytical mean-field theory that derives the macroscopic network field equations from the microscopic neuronal networks, enabling the analysis by nonlinear dynamics theory and linear noise approximation. The generic dynamical principle revealed here contributes to a more integrative understanding of neural systems and brain functions and incorporates multimodal and multilevel experimental observations. The E–I balanced neural network in combination with the effective mean-field theory can serve as a mechanistic modeling framework to study the multilevel neural dynamics underlying neural information and cognitive processes.     

Audiovisual integration of letters in the human brain

Letters of the alphabet have auditory (phonemic) and visual (graphemic) qualities. To investigate the neural representations of such audiovisual objects, we recorded neuromagnetic cortical responses to auditorily, visually, and audiovisually presented single letters. The auditory and visual brain activations first converged around 225 ms after stimulus onset and then interacted predominantly in the right temporo-occipito-parietal junction (280345 ms) and the left (380-540 ms) and right (450-535 ms) superior temporal sulci. These multisensory brain areas, playing a role in audiovisual integration of phonemes and graphemes, participate in the neural network supporting the supramodal concept of a "letter." The dynamics of these functions bring new insight into the interplay between sensory and association cortices during object recognition.     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.