Statistical Detection of EEG Synchrony Using Empirical Bayesian Inference

There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron''s Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.     

Functional connectivity between prefrontal cortex and striatum estimated by phase locking value

The interplay between the prefrontal cortex (PFC) and striatum has an important role in cognitive processes. To investigate interactive functions between the two areas in reward processing, we recorded local field potentials (LFPs) simultaneously from the two areas of two monkeys performing a reward prediction task (large reward vs small reward). The power of the LFPs was calculated in three frequency bands: the beta band (15–29 Hz), the low gamma band (30–49 Hz), and the high gamma band (50–100 Hz). We found that both the PFC and striatum encoded the reward information in the beta band. The reward information was also found in the high gamma band in the PFC, not in the striatum. We further calculated the phase-locking value (PLV) between two LFP signals to measure the phase synchrony between the PFC and striatum. It was found that significant differences occurred between PLVs in different task periods and in different frequency bands. The PLVs in small reward condition were significant higher than that in large reward condition in the beta band. In contrast, the PLVs in the high gamma band were stronger in large reward trials than in small trials. These results suggested that the functional connectivity between the PFC and striatum depended on the task periods and reward conditions. The beta synchrony between the PFC and striatum may regulate behavioral outputs of the monkeys in the small reward condition.     

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches

Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information flow among networks.     

Development of cue integration in human navigation

Mammalian navigation depends both on visual landmarks and on self-generated (e.g., vestibular and proprioceptive) cues that signal the organism's own movement [1-5]. When these conflict, landmarks can either reset estimates of self-motion or be integrated with them [6-9]. We asked how humans combine these information sources and whether children, who use both from a young age [10-12], combine them as adults do. Participants attempted to return an object to its original place in an arena when given either visual landmarks only, nonvisual self-motion information only, or both. Adults, but not 4- to 5-year-olds or 7- to 8-year-olds, reduced their response variance when both information sources were available. In an additional "conflict" condition that measured relative reliance on landmarks and self-motion, we predicted behavior under two models: integration (weighted averaging) of the cues and alternation between them. Adults' behavior was predicted by integration, in which the cues were weighted nearly optimally to reduce variance, whereas children's behavior was predicted by alternation. These results suggest that development of individual spatial-representational systems precedes development of the capacity to combine these within a common reference frame. Humans can integrate spatial cues nearly optimally to navigate, but this ability depends on an extended developmental process.     

神经元对于高频电刺激的动态响应

深部脑刺激(deep brain stimulation,DBS)在许多神经系统疾病的临床治疗上都展现出良好的应用前景,然而,其作用机制尚不明确.常规DBS采用高频刺激(high frequency stimulation,HFS)的脉冲序列,这种窄脉冲最容易激活神经元结构中的轴突部分,通过轴突的投射,将HFS的作用传播至下游神经元.因此,为了探讨DBS的作用机制,并鉴于海马脑区是治疗癫痫和痴呆症等疾病的重要靶点,我们研究了海马区轴突HFS对于下游神经元的作用.对麻醉大鼠的海马CA1区传入神经通路Schaffer侧支施加1 min的100 Hz高频刺激,记录并提取下游CA1区锥体神经元和中间神经元的单元锋电位.计算锋电位的发放率,以及它们与刺激脉冲之间的锁相值(phase-locking value,PLV)和潜伏期,以定量分析HFS期间神经元动作电位发放的变化趋势.结果显示,在传入轴突上施加HFS时,初期会诱发下游神经元群体同步产生动作电位(即群峰电位).在HFS后期(群峰电位消失之后),两类神经元的单元锋电位发放仍然持续,并且发放率较稳定.但是,锋电位与刺激脉冲之间的锁相性逐渐减弱、潜伏期逐渐延长.而且,与中间神经元相比较,锥体神经元锋电位的锁相性更弱、潜伏期更长.这些结果表明,持续的轴突HFS可以诱导下游神经元产生非同步的活动,高频脉冲刺激引起的不完全轴突传导阻滞可能是导致该现象产生的主要原因.本文的研究为揭示脑刺激的作用机制提供了重要信息.     

The frustrated brain: from dynamics on motifs to communities and networks

Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions. Synchronization fulfils integration by reducing entropy, while adaptive function mandates that a broad variety of stable states be readily accessible. Here, we elucidate two complementary influences on patterns of zero-lag synchrony that derive from basic properties of brain networks. First, mutually coupled pairs of neuronal subsystems—resonance pairs—promote stable zero-lag synchrony among the small motifs in which they are embedded, and whose effects can propagate along connected chains. Second, frustrated closed-loop motifs disrupt synchronous dynamics, enabling metastable configurations of zero-lag synchrony to coexist. We document these two complementary influences in small motifs and illustrate how these effects underpin stable versus metastable phase-synchronization patterns in prototypical modular networks and in large-scale cortical networks of the macaque (CoCoMac). We find that the variability of synchronization patterns depends on the inter-node time delay, increases with the network size and is maximized for intermediate coupling strengths. We hypothesize that the dialectic influences of resonance versus frustration may form a dynamic substrate for flexible neuronal integration, an essential platform across diverse cognitive processes.     

Chemosensory cross-modal stroop effects: congruent odors facilitate taste identification

In order to explore the cross-modal cognitive associations between smell and taste, a chemosensory analogue of the Stroop task (Stroop 1935) was developed. Fourteen participants were presented with an odorant and a tastant and asked to identify the tastant as "sweet" or "sour" by pressing 1 of 2 buttons as quickly as possible. Participants were faster to name the taste when it was presented with an odor that was congruent (e.g., strawberry/sweet) than with an incongruent odor (e.g., strawberry/sour). These results support the concept of a high level of cognitive integration between the senses of smell and taste and illustrates occasions of interference between information arising from different sensory systems.     

Young children do not integrate visual and haptic form information

Several studies have shown that adults integrate visual and haptic information (and information from other modalities) in a statistically optimal fashion, weighting each sense according to its reliability [1, 2]. When does this capacity for crossmodal integration develop? Here, we show that prior to 8 years of age, integration of visual and haptic spatial information is far from optimal, with either vision or touch dominating totally, even in conditions in which the dominant sense is far less precise than the other (assessed by discrimination thresholds). For size discrimination, haptic information dominates in determining both perceived size and discrimination thresholds, whereas for orientation discrimination, vision dominates. By 8-10 years, the integration becomes statistically optimal, like adults. We suggest that during development, perceptual systems require constant recalibration, for which cross-sensory comparison is important. Using one sense to calibrate the other precludes useful combination of the two sources.     

Causality in the association between P300 and alpha event-related desynchronization

Recent findings indicated that both P300 and alpha event-related desynchronization (α-ERD) were associated, and similarly involved in cognitive brain functioning, e.g., attention allocation and memory updating. However, an explicit causal influence between the neural generators of P300 and α-ERD has not yet been investigated. In the present study, using an oddball task paradigm, we assessed the task effect (target vs. non-target) on P300 and α-ERD elicited by stimuli of four sensory modalities, i.e., audition, vision, somatosensory, and pain, estimated their respective neural generators, and investigated the information flow among their neural generators using time-varying effective connectivity in the target condition. Across sensory modalities, the scalp topographies of P300 and α-ERD were similar and respectively maximal at parietal and occipital regions in the target condition. Source analysis revealed that P300 and α-ERD were mainly generated from posterior cingulate cortex and occipital lobe respectively. As revealed by time-varying effective connectivity, the cortical information was consistently flowed from α-ERD sources to P300 sources in the target condition for all four sensory modalities. All these findings showed that P300 in the target condition is modulated by the changes of α-ERD, which would be useful to explore neural mechanism of cognitive information processing in the human brain.     

Cracking the code of oscillatory activity

Neural oscillations are ubiquitous measurements of cognitive processes and dynamic routing and gating of information. The fundamental and so far unresolved problem for neuroscience remains to understand how oscillatory activity in the brain codes information for human cognition. In a biologically relevant cognitive task, we instructed six human observers to categorize facial expressions of emotion while we measured the observers' EEG. We combined state-of-the-art stimulus control with statistical information theory analysis to quantify how the three parameters of oscillations (i.e., power, phase, and frequency) code the visual information relevant for behavior in a cognitive task. We make three points: First, we demonstrate that phase codes considerably more information (2.4 times) relating to the cognitive task than power. Second, we show that the conjunction of power and phase coding reflects detailed visual features relevant for behavioral response--that is, features of facial expressions predicted by behavior. Third, we demonstrate, in analogy to communication technology, that oscillatory frequencies in the brain multiplex the coding of visual features, increasing coding capacity. Together, our findings about the fundamental coding properties of neural oscillations will redirect the research agenda in neuroscience by establishing the differential role of frequency, phase, and amplitude in coding behaviorally relevant information in the brain.     

Proximity and estrous synchrony in Mahale chimpanzees

Since McClintock [Nature 229:244-255, 1971] first reported menstrual synchrony in women, a number of studies have reported similar phenomena. Many researchers have suggested that one of the proximate factors leading to synchrony is spatial proximity among females (e.g., close friends or roommates). However, most studies on menstrual synchrony have been conducted in limited spaces, and it remains to be determined whether controlled environments, such as those used in experiments, actually exist in the wild. In this study, we analyzed the relationship between proximity and estrous synchrony using data from wild female chimpanzees at Mahale, Tanzania. In the cycling females, we observed two pairs that spent a large amount of time together. We compared the estrous synchrony indices (ESIs) between these two pairs and the other females. Our results showed that the ESIs of the high-proximity pairs did not differ from those of other pairs. .     

Evaluation of EEG Oscillatory Patterns and Cognitive Process during Simple and Compound Limb Motor Imagery

Motor imagery (MI), sharing similar neural representations to motor execution, is regarded as a window to investigate the cognitive motor processes. However, in comparison to simple limb motor imagery, significantly less work has been reported on brain oscillatory patterns induced by compound limb motor imagery which involves several parts of limbs. This study aims to investigate differences of the electroencephalogram (EEG) patterns as well as cognitive process between simple limb motor imagery and compound limb motor imagery. Ten subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet) and three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot). Simultaneous imagination of different limbs contributes to the activation of larger cortical areas as well as two estimated sources located at corresponding motor areas within beta rhythm. Compared with simple limb motor imagery, compound limb motor imagery presents a network with more effective interactions overlying larger brain regions, additionally shows significantly larger causal flow over sensorimotor areas and larger causal density over both sensorimotor areas and neighboring regions. On the other hand, compound limb motor imagery also shows significantly larger 10–11 Hz alpha desynchronization at occipital areas and central theta synchronization. Furthermore, the phase-locking value (PLV) between central and occipital areas of left/right hand combined with contralateral foot imagery is significantly larger than that of simple limb motor imagery. All these findings imply that there exist apparent intrinsic distinctions of neural mechanism between simple and compound limb motor imagery, which presents a more complex effective connectivity network and may involve a more complex cognitive process during information processing.     

Integrated information increases with fitness in the evolution of animats

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.     

Cognitive association formation in human memory revealed by spatiotemporal brain imaging

Cognitive theory posits association by juxtaposition or by fusion. We employed the measurement of event-related brain potentials (ERPs) to a concept fusion task in order to explore memory encoding of these two types of associations between word pairs, followed by a memory test for original pair order. Encoding processes were isolated by subtracting fusion task ERPs corresponding to pairs later retrieved quickly from ERPs corresponding to pairs later retrieved slowly, separately for pairs fused successfully and unsuccessfully (i.e., juxtaposed). Analyses revealed that the encoding of these two types of associations yields different ERP voltage polarities, scalp topographies, and brain sources extending over the entire time course of processing.     

Thalamic bursts modulate cortical synchrony locally to switch between states of global functional connectivity in a cognitive task

Performing a cognitive task requires going through a sequence of functionally diverse stages. Although it is typically assumed that these stages are characterized by distinct states of cortical synchrony that are triggered by sub-cortical events, little reported evidence supports this hypothesis. To test this hypothesis, we first identified cognitive stages in single-trial MEG data of an associative recognition task, showing with a novel method that each stage begins with local modulations of synchrony followed by a state of directed functional connectivity. Second, we developed the first whole-brain model that can simulate cortical synchrony throughout a task. The model suggests that the observed synchrony is caused by thalamocortical bursts at the onset of each stage, targeted at cortical synapses and interacting with the structural anatomical connectivity. These findings confirm that cognitive stages are defined by distinct states of cortical synchrony and explains the network-level mechanisms necessary for reaching stage-dependent synchrony states.     

The impact of stochasticity on the behaviour of nonlinear population models: synchrony and the Moran effect

Environmental variation is ubiquitous, but its effects on nonlinear population dynamics are poorly understood. Using simple (unstructured) nonlinear models we investigate the effects of correlated noise on the dynamics of two otherwise independent populations (the Moran effect), i.e. we focus on noise rather than dispersion or trophic interaction as the cause of population synchrony. We find that below the bifurcation threshold for periodic behaviour (1) synchrony between populations is strongly dependent on the shape of the noise distribution but largely insensitive to which model is studied, (2) there is, in general, a loss of synchrony as the noise is filtered by the model, (3) for specially structured noise distributions this loss can be effectively eliminated over a restricted range of distribution parameter values even though the model might be nonlinear, (4) for unstructured models there is no evidence of correlation enhancement, a mechanism suggested by Moran, but above the bifurcation threshold enhancement is possible for weak noise through phase-locking, (5) rapid desynchronisation occurs as the chaotic regime is approached. To carry out the investigation the stochastic models are (a) reformulated in terms of their joint asymptotic probability distributions and (b) simulated to analyse temporal patterns.     

Establishing a Statistical Link between Network Oscillations and Neural Synchrony

Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.     

Influence of connection type on phase synchrony: analysis of a neural mass model

Empirical studies have demonstrated synchronized frontal and parietal electrophysiological signals at 22-34?Hz during a conjunctive visual search task and at 36-56?Hz during a pop-out visual search task. Bidirectional (conjunctive) versus unidirectional (pop-out) information transfer between neuronal populations is hypothesized to underly this difference in synchronization frequency. This study modeled the influence of connection type (i.e., unidirectional vs. bidirectional) on phase synchrony between two neural populations using a neural mass model. Phase-locking values (PLVs) were used as the measure of synchrony between populations. Consistent with the connectivity hypothesis, the model revealed greater PLVs at 22-34?Hz when the two populations were connected bidirectionally than unidirectionally, but greater PLVs at 34-52?Hz when connected unidirectionally than bidirectionally. The model suggests that inter-population connectivity also changes with bottom-up versus top-down control of attention.     

The synchrony effect revisited: chronotype,time of day and cognitive performance in a semantic analogy task

ABSTRACT

The synchrony effect (i.e. superior performance at optimal, inferior performance at suboptimal times of day) has been broadly studied within the context of circadian rhythms. Whether one chronotype copes better with the synchrony effect than the other received only insufficient empirical attention. We report on an applied experimental study investigating the impact of chronotype on the synchrony effect in a semantic analogy task. To detect an analogy, 36 participants (12 males) aged between 18 and 40 had to decide whether the relation between events of a source pair was mirrored by the relation between events of a target pair (e.g. to cook: to eat = to saddle: to ride). Temporal orientation of the relation within each event pair was varied corresponding either to the chronological or reverse order. Response times (RTs), error rates, as well as the psychophysiological parameters pre-experimental pupil baseline and peak pupil dilation replicate findings of a synchrony effect (shorter RTs and allocation of less cognitive resources at optimal times of day) and show an impact of chronotype (morning types generally outperforming evening types). Most importantly, morning types appeared to cope better with the synchrony effect than evening types: At suboptimal times, morning types solved the analogy detection task more efficient; that is faster with the same accuracy and without the investment of more cognitive resources. They also showed greater alertness and wakefulness indexed by greater pre-experimental pupil baselines. At optimal times of day, morning types have more cognitive resources available to allocate these to the more demanding conditions to outperform evening types. We interpret these findings to suggest that morning types are more able to adapt to unfavourable circumstances (for instance, by avoiding wasteful resource allocation when there are less cognitive resources available). Evening types appear less able to adapt to suboptimal times than morning types, because they have to deal with social jetlag and decreased self-control.     

Adaptive Sampling of Information in Perceptual Decision-Making

In many perceptual and cognitive decision-making problems, humans sample multiple noisy information sources serially, and integrate the sampled information to make an overall decision. We derive the optimal decision procedure for two-alternative choice tasks in which the different options are sampled one at a time, sources vary in the quality of the information they provide, and the available time is fixed. To maximize accuracy, the optimal observer allocates time to sampling different information sources in proportion to their noise levels. We tested human observers in a corresponding perceptual decision-making task. Observers compared the direction of two random dot motion patterns that were triggered only when fixated. Observers allocated more time to the noisier pattern, in a manner that correlated with their sensory uncertainty about the direction of the patterns. There were several differences between the optimal observer predictions and human behaviour. These differences point to a number of other factors, beyond the quality of the currently available sources of information, that influences the sampling strategy.