Toward an integrated continuum model of cerebral dynamics: the cerebral rhythms, synchronous oscillation and cortical stability

Spectrum of connectivity fluctuations including the effect of activity-dependent feedback

The spatiotemporal spectrum of feedback-driven fluctuations of brain connectivity is investigated using nonlinear neural field theory of the corticothalamic system. Weakly nonlinear dynamics of neural feedbacks are expanded in terms of first order perturbations of neural activity relative to a fixed point. Susceptibilities are used to quantify the change in connectivity per unit change in presynaptic or postsynaptic activity caused by nonlinear feedbacks such as facilitation, depression, sensitization, potentiation, and the effects of discrete eigenmode structure are included for a spherical brain geometry. Spectral signatures such as resonances are identified that allow the presence of particular presynaptic and postsynaptic feedback effects to be inferred. These include additional resonances at high frequencies and shifts of existing spectral peaks, mostly visible in the lowest spatial modes of the response.

Spectral signatures of activity-dependent neural feedback in the corticothalamic system

The modulation of neural quantities by presynaptic and postsynaptic activities via local feedback processes is investigated by incorporating nonlinear phenomena such as relative refractory period, synaptic enhancement, synaptic depression, and habituation. This is done by introducing susceptibilities, which quantify the response in either firing threshold or synaptic strength to unit change in either presynaptic or postsynaptic activity. Effects on the power spectra are then analyzed for a realistic corticothalamic model to determine the spectral signatures of various nonlinear processes and to what extent these are distinct. Depending on the feedback processes, there can be enhancements or reductions in low-frequency and/or alpha power, splitting of the alpha resonance, and/or appearance of new resonances at high frequencies. These features in the power spectra allow processes to be fully distinguished where they are unique, or partly distinguished if they are common to only a subset of feedbacks, and can potentially be used to constrain the types, strengths, and dynamics of feedbacks present.

The crossroads of anxiety: distinct neurophysiological maps for different symptomatic groups

BACKGROUND

Despite the devastating impact of anxiety disorders (ADs) worldwide, long-lasting debates on causes and remedies have not solved the clinician's puzzle: who should be treated and how? Psychiatric classifications conceptualize ADs as distinct entities, with strong support from neuroscience fields. Yet, comorbidity and pharmacological response suggest a single "serotonin dysfunction" dimension. Whether AD is one or several disorders goes beyond academic quarrels, and the distinction has therapeutic relevance. Addressing the underlying dysfunctions should improve treatment response. By its own nature, neurophysiology can be the best tool to address dysfunctional processes.

PURPOSE

To search for neurophysiological dysfunctions and differences among panic disorder (PD), agoraphobia-social-specific phobia, obsessive-compulsive disorder (OCD) and generalized anxiety disorder.

METHODS

A sample population of 192 unmedicated patients and 30 aged-matched controls partook in this study. Hypothesis-related neurophysiological variables were combined into ten independent factors: 1) dysrhythmic patterns, 2) delta, 3) theta, 4) alpha, 5) beta (whole-head absolute power z-scores), 6) event-related potential (ERP) combined latency, 7) ERP combined amplitude (z-scores), 8) magnitude, 9) site, and 10) site of hyperactive networks. Combining single variables into representative factors was necessary because, as in all real-life phenomena, the complexity of interactive processes cannot be addressed through single variables and the multiplicity of potentially implicated variables would demand an extremely large sample size for statistical analysis.

RESULTS

The nonparametric analysis correctly classified 81% of the sample. Dysrhythmic patterns, decreased delta, and increased beta differentiated AD from controls. Shorter ERP latencies were found in several individual patients, mostly from the OCD group. Hyperactivities were found at the right frontorbital-striatal network in OCD and at the panic circuit in PD.

CONCLUSIONS

Our findings support diffuse cortical instability in AD in general, with individual differences in information processing deficits and regional hyperactivities in OCD and PD. Study limitations and the rationale behind the variable selection and combination strategy will be discussed before addressing the therapeutic implications of our findings.

Neocortical dynamics due to axon propagation delays in cortico-cortical fibers: EEG traveling and standing waves with implications for top-down influences on local networks and white matter disease

The brain is treated as a nested hierarchical complex system with substantial interactions across spatial scales. Local networks are pictured as embedded within global fields of synaptic action and action potentials. Global fields may act top-down on multiple networks, acting to bind remote networks. Because of scale-dependent properties, experimental electrophysiology requires both local and global models that match observational scales. Multiple local alpha rhythms are embedded in a global alpha rhythm. Global models are outlined in which cm-scale dynamic behaviors result largely from propagation delays in cortico-cortical axons and cortical background excitation level, controlled by neuromodulators on long time scales. The idealized global models ignore the bottom-up influences of local networks on global fields so as to employ relatively simple mathematics. The resulting models are transparently related to several EEG and steady state visually evoked potentials correlated with cognitive states, including estimates of neocortical coherence structure, traveling waves, and standing waves. The global models suggest that global oscillatory behavior of self-sustained (limit-cycle) modes lower than about 20 Hz may easily occur in neocortical/white matter systems provided: Background cortical excitability is sufficiently high; the strength of long cortico-cortical axon systems is sufficiently high; and the bottom-up influence of local networks on the global dynamic field is sufficiently weak. The global models provide “entry points” to more detailed studies of global top-down influences, including binding of weakly connected networks, modulation of gamma oscillations by theta or alpha rhythms, and the effects of white matter deficits.

Enhancement of gamma activity after selective activation of dopamine D4 receptors in freely moving rats and in a neurodevelopmental model of schizophrenia

Dopamine D4 receptor (D4R) mechanisms have been implicated in several psychiatric diseases, including schizophrenia, attention-deficit hyperactivity disorder (ADHD), and autism, which are characterized by cognitive deficits. The cellular mechanisms are poorly understood but impaired neuronal synchronization within cortical networks in the gamma frequency band has been proposed to contribute to these deficits. A D4R polymorphism was recently linked to variations in gamma power in both normal and ADHD subjects, and D4R activation was shown to enhance kainate-induced gamma oscillations in brain slices in vitro. The goal of this study was to investigate the effect of D4R activation on gamma oscillations in freely moving rats during natural behavior. Field potentials were recorded in the frontal, prefrontal, parietal, and occipital cortex and hippocampus. Gamma power was assessed before and after subcutaneous injection of a D4R agonist, A-412997, in several doses between 0.3 and 10.0 mg/kg. The experiments were also repeated in a neurodevelopmental model of schizophrenia, in which rats are prenatally treated with methylazoxymethanol (MAM). We found that the D4R agonist increased gamma power in all regions at short latency and lasted for ~2 h, both in normal and MAM-treated rats. The effect was dose dependent indicated by the significant difference between the effects after 3 and 10 mg/kg in pair-wise comparison, whereas 0.3 and 1.0 mg/kg injections were ineffective. This study demonstrates the involvement of D4R in cortical gamma oscillations in vivo and identifies this receptor as potential target for pharmacological treatment of cognitive deficits.

Stability constraints on large-scale structural brain networks

Stability is an important dynamical property of complex systems and underpins a broad range of coherent self-organized behavior. Based on evidence that some neurological disorders correspond to linear instabilities, we hypothesize that stability constrains the brain's electrical activity and influences its structure and physiology. Using a physiologically-based model of brain electrical activity, we investigated the stability and dispersion solutions of networks of neuronal populations with propagation time delays and dendritic time constants. We find that stability is determined by the spectrum of the network's matrix of connection strengths and is independent of the temporal damping rate of axonal propagation with stability restricting the spectrum to a region in the complex plane. Time delays and dendritic time constants modify the shape of this region but it always contains the unit disk. Instabilities resulting from changes in connection strength initially have frequencies less than a critical frequency. For physiologically plausible parameter values based on the corticothalamic system, this critical frequency is approximately 10 Hz. For excitatory networks and networks with randomly distributed excitatory and inhibitory connections, time delays and non-zero dendritic time constants have no impact on network stability but do effect dispersion frequencies. Random networks with both excitatory and inhibitory connections can have multiple marginally stable modes at low delta frequencies.

Brain rhythms reveal a hierarchical network organization

Recordings of ongoing neural activity with EEG and MEG exhibit oscillations of specific frequencies over a non-oscillatory background. The oscillations appear in the power spectrum as a collection of frequency bands that are evenly spaced on a logarithmic scale, thereby preventing mutual entrainment and cross-talk. Over the last few years, experimental, computational and theoretical studies have made substantial progress on our understanding of the biophysical mechanisms underlying the generation of network oscillations and their interactions, with emphasis on the role of neuronal synchronization. In this paper we ask a very different question. Rather than investigating how brain rhythms emerge, or whether they are necessary for neural function, we focus on what they tell us about functional brain connectivity. We hypothesized that if we were able to construct abstract networks, or "virtual brains", whose dynamics were similar to EEG/MEG recordings, those networks would share structural features among themselves, and also with real brains. Applying mathematical techniques for inverse problems, we have reverse-engineered network architectures that generate characteristic dynamics of actual brains, including spindles and sharp waves, which appear in the power spectrum as frequency bands superimposed on a non-oscillatory background dominated by low frequencies. We show that all reconstructed networks display similar topological features (e.g. structural motifs) and dynamics. We have also reverse-engineered putative diseased brains (epileptic and schizophrenic), in which the oscillatory activity is altered in different ways, as reported in clinical studies. These reconstructed networks show consistent alterations of functional connectivity and dynamics. In particular, we show that the complexity of the network, quantified as proposed by Tononi, Sporns and Edelman, is a good indicator of brain fitness, since virtual brains modeling diseased states display lower complexity than virtual brains modeling normal neural function. We finally discuss the implications of our results for the neurobiology of health and disease.

Generalization of learning by synchronous waves: from perceptual organization to invariant organization

From a few presentations of an object, perceptual systems are able to extract invariant properties such that novel presentations are immediately recognized. This may be enabled by inferring the set of all representations equivalent under certain transformations. We implemented this principle in a neurodynamic model that stores activity patterns representing transformed versions of the same object in a distributed fashion within maps, such that translation across the map corresponds to the relevant transformation. When a pattern on the map is activated, this causes activity to spread out as a wave across the map, activating all the transformed versions represented. Computational studies illustrate the efficacy of the proposed mechanism. The model rapidly learns and successfully recognizes rotated and scaled versions of a visual representation from a few prior presentations. For topographical maps such as primary visual cortex, the mechanism simultaneously represents identity and variation of visual percepts whose features change through time.

Temporal scaling properties and spatial synchronization of spontaneous blood oxygenation level-dependent (BOLD) signal fluctuations in rat sensorimotor network at different levels of isoflurane anesthesia

Spontaneous fluctuations in the blood oxygenation level-dependent (BOLD) MRI signal during the resting state are increasingly being studied in healthy and diseased brain in humans and animal models. Yet, the relationship between functional brain status and the characteristics of spontaneous BOLD fluctuations remains poorly understood. In order to obtain more insights into this relationship and, in particular, the effects of anesthesia thereupon, we investigated the spatial and temporal correlations of spontaneous BOLD fluctuations in somatosensory and motor regions of rat brain at different inhalation levels of the frequently applied anesthetic isoflurane. We found that the temporal scaling, characterized by the Hurst exponent (H), showed persistent behavior (H > 0.5) at 0.5-1.0% isoflurane. Furthermore, low-pass-filtered spontaneous BOLD oscillations were correlated significantly in bilateral somatosensory and bilateral motor cortices, reflective of interhemispheric functional connectivity. Under 2.9% isoflurane anesthesia, the temporal scaling characteristics approached those of Gaussian white noise (H = 0.5), the relative amplitude of BOLD low-frequency fluctuations declined, and cross-correlations of these oscillations between functionally connected regions decreased significantly. Loss of interhemispheric functional connectivity at 2.9% isoflurane anesthesia was stronger between bilateral motor regions than between bilateral somatosensory regions, which points to distinct effects of anesthesia on differentially organized neuronal networks. Although we cannot completely rule out a possible contribution from hemodynamic signals with a non-neuronal origin, our results emphasize that spatiotemporal characteristics of spontaneous BOLD fluctuations are related to the brain's specific functional status and network organization, and demonstrate that these are largely preserved under light to mild anesthesia with isoflurane.

Coherent Infomax as a computational goal for neural systems

Signal processing in the cerebral cortex is thought to involve a common multi-purpose algorithm embodied in a canonical cortical micro-circuit that is replicated many times over both within and across cortical regions. Operation of this algorithm produces widely distributed but coherent and relevant patterns of activity. The theory of Coherent Infomax provides a formal specification of the objectives of such an algorithm. It also formally derives specifications for both the short-term processing dynamics and for the learning rules whereby the connection strengths between units in the network can be adapted to the environment in which the system finds itself. A central assumption of the theory is that the local processors can combine reliable signal coding with flexible use of those codes because they have two classes of synaptic connection: driving connections which specify the information content of the neural signals, and contextual connections which modulate that signal processing. Here, we make the biological relevance of this theory more explicit by putting more emphasis upon the contextual guidance of ongoing processing, by showing that Coherent Infomax is consistent with a particular Bayesian interpretation for the contextual guidance of learning and processing, by explicitly specifying rules for on-line learning, and by suggesting approximations by which the learning rules can be made computationally feasible within systems composed of very many local processors.

Early disturbances of gamma band dynamics in mild cognitive impairment

Recent studies have indicated that gamma band oscillations participate in the temporal binding needed for the synchronization of cortical networks involved in short-term memory and attentional processes. To date, no study has explored the temporal dynamics of gamma band in the early stages of dementia. At baseline, gamma band analysis was performed in 29 cases with mild cognitive impairment (MCI) during the n-back task. Based on phase diagrams, multiple linear regression models were built to explore the relationship between the cognitive status and gamma oscillation changes over time. Individual measures of phase diagram complexity were made using fractal dimension values. After 1 year, all cases were assessed neuropsychologically using the same battery. A total of 16 MCI patients showed progressive cognitive decline (PMCI) and 13 remained stable (SMCI). When adjusted for gamma values at lag -2, and -3 ms, PMCI cases displayed significantly lower average changes in gamma values than SMCI cases both in detection and 2-back tasks. Gamma fractal dimension of PMCI cases displayed significantly higher gamma fractal dimension values compared to SMCI cases. This variable explained 11.8% of the cognitive variability in this series. Our data indicate that the progression of cognitive decline in MCI is associated with early deficits in temporal binding that occur during the activation of selective attention processes.

Decomposition of skin conductance data by means of nonnegative deconvolution

Skin conductance (SC) data are usually characterized by a sequence of overlapping phasic skin conductance responses (SCRs) overlying a tonic component. The variability of SCR shapes hereby complicates the proper decomposition of SC data. A method is proposed for full decomposition of SC data into tonic and phasic components. A two-compartment diffusion model was found to adequately describe a standard SCR shape based on the process of sweat diffusion. Nonnegative deconvolution is used to decompose SC data into discrete compact responses and at the same time assess deviations from the standard SCR shape, which could be ascribed to the additional process of pore opening. Based on the result of single non-overlapped SCRs, response parameters can be estimated precisely as shown in a paradigm with varying inter-stimulus intervals.

Effects of the anesthetic agent propofol on neural populations

The neuronal mechanisms of general anesthesia are still poorly understood. Besides several characteristic features of anesthesia observed in experiments, a prominent effect is the bi-phasic change of power in the observed electroencephalogram (EEG), i.e. the initial increase and subsequent decrease of the EEG-power in several frequency bands while increasing the concentration of the anaesthetic agent. The present work aims to derive analytical conditions for this bi-phasic spectral behavior by the study of a neural population model. This model describes mathematically the effective membrane potential and involves excitatory and inhibitory synapses, excitatory and inhibitory cells, nonlocal spatial interactions and a finite axonal conduction speed. The work derives conditions for synaptic time constants based on experimental results and gives conditions on the resting state stability. Further the power spectrum of Local Field Potentials and EEG generated by the neural activity is derived analytically and allow for the detailed study of bi-spectral power changes. We find bi-phasic power changes both in monostable and bistable system regime, affirming the omnipresence of bi-spectral power changes in anesthesia. Further the work gives conditions for the strong increase of power in the δ-frequency band for large propofol concentrations as observed in experiments.

Modeling brain activation patterns for the default and cognitive states

We argue that spatial patterns of cortical activation observed with EEG, MEG and fMRI might arise from spontaneous self-organisation of interacting populations of excitatory and inhibitory neurons. We examine the dynamical behavior of a mean-field cortical model that includes chemical and electrical (gap-junction) synapses, focusing on two limiting cases: the "slow-soma" limit with slow voltage feedback from soma to dendrite, and the "fast-soma" limit in which the feedback action of soma voltage onto dendrite reversal potentials is instantaneous. For slow soma-dendrite feedback, we find a low-frequency (approximately 1 Hz) dynamic Hopf instability, and a stationary Turing instability that catalyzes formation of patterned distributions of cortical firing-rate activity with pattern wavelength approximately 2 cm. Turing instability can only be triggered when gap-junction diffusion between inhibitory neurons is strong, but patterning is destroyed if the tonic level of subcortical excitation is raised sufficiently. Interaction between the Hopf and Turing instabilities may describe the non-cognitive background or "default" state of the brain, as observed by BOLD imaging. In the fast-soma limit, the model predicts a high-frequency Hopf (approximately 35 Hz) instability, and a traveling-wave gamma-band instability that manifests as a 2-D standing-wave pattern oscillating in place at approximately 30 Hz. Small levels of inhibitory diffusion enhance and broaden the definition of the gamma antinodal regions by suppressing higher-frequency spatial modes, but gamma emergence is not contingent on the presence of inhibitory gap junctions; higher levels of diffusion suppress gamma activity. Fast-soma instabilities are enhanced by increased subcortical stimulation. Prompt soma-dendrite feedback may be an essential component of the genesis and large-scale cortical synchrony of gamma activity observed at the point of cognition.

Monofractal and multifractal dynamics of low frequency endogenous brain oscillations in functional MRI

Fractal processes, like trees or coastlines, are defined by self-similarity or power law scaling controlled by a single exponent, simply related to the fractal dimension or Hurst exponent (H) of the process. Multifractal processes, like turbulence, have more complex behaviours defined by a spectrum of possible local scaling behaviours or singularity exponents (h). Here, we report two experiments that explore the relationships between instrumental and cognitive variables and the monofractal and multifractal parameters of functional magnetic resonance imaging (fMRI) data acquired in a no-task or resting state. First, we show that the Hurst exponent is greater in grey matter than in white matter regions, and it is maximal in grey matter when data were acquired with an echo time known to optimise BOLD contrast. Second, we show that latency of response in a fame decision/facial encoding task was negatively correlated with the Hurst exponent of resting state data acquired 30 min after task performance. This association was localised to a right inferior frontal cortical region activated by the fame decision task and indicated that people with shorter response latency had more persistent dynamics (higher values of H). Multifractal analysis revealed that faster responding participants had wider singularity spectra of resting fMRI time series in inferior frontal cortex. Endogenous brain oscillations measured by fMRI have monofractal and multifractal properties that can be related to instrumental and cognitive factors in a way, which indicates that these low frequency dynamics are relevant to neurocognitive function.

A network model for activity-dependent sleep regulation

We develop and characterize a dynamical network model for activity-dependent sleep regulation. Specifically, in accordance with the activity-dependent theory for sleep, we view organism sleep as emerging from the local sleep states of functional units known as cortical columns; these local sleep states evolve through integration of local activity inputs, loose couplings with neighboring cortical columns, and global regulation (e.g. by the circadian clock). We model these cortical columns as coupled or networked activity-integrators that transition between sleep and waking states based on thresholds on the total activity. The model dynamics for three canonical experiments (which we have studied both through simulation and system-theoretic analysis) match with experimentally observed characteristics of the cortical-column network. Most notably, assuming connectedness of the network graph, our model predicts the recovery of the columns to a synchronized state upon temporary overstimulation of a single column and/or randomization of the initial sleep and activity-integration states. In analogy with other models for networked oscillators, our model also predicts the possibility for such phenomena as mode-locking.

Neuronal synchrony during anesthesia: a thalamocortical model

There is growing evidence in favor of the temporal-coding hypothesis that temporal correlation of neuronal discharges may serve to bind distributed neuronal activity into unique representations and, in particular, that theta (3.5-7.5 Hz) and delta (0.5 < 3.5 Hz) oscillations facilitate information coding. The theta- and delta-rhythms are shown to be involved in various sleep stages, and during anesthesia, they undergo changes with the depth of anesthesia. We introduce a thalamocortical model of interacting neuronal ensembles to describe phase relationships between theta- and delta-oscillations, especially during deep and light anesthesia. Asymmetric and long-range interactions among the thalamocortical neuronal oscillators are taken into account. The model results are compared with experimental observations. The delta- and theta-activities are found to be separately generated and are governed by the thalamus and cortex, respectively. Changes in the degree of intraensemble and interensemble synchrony imply that the neuronal ensembles inhibit information coding during deep anesthesia and facilitate it during light anesthesia.

Gap junctions mediate large-scale Turing structures in a mean-field cortex driven by subcortical noise

One of the grand puzzles in neuroscience is establishing the link between cognition and the disparate patterns of spontaneous and task-induced brain activity that can be measured clinically using a wide range of detection modalities such as scalp electrodes and imaging tomography. High-level brain function is not a single-neuron property, yet emerges as a cooperative phenomenon of multiply-interacting populations of neurons. Therefore a fruitful modeling approach is to picture the cerebral cortex as a continuum characterized by parameters that have been averaged over a small volume of cortical tissue. Such mean-field cortical models have been used to investigate gross patterns of brain behavior such as anesthesia, the cycles of natural sleep, memory and erasure in slow-wave sleep, and epilepsy. There is persuasive and accumulating evidence that direct gap-junction connections between inhibitory neurons promote synchronous oscillatory behavior both locally and across distances of some centimeters, but, to date, continuum models have ignored gap-junction connectivity. In this paper we employ simple mean-field arguments to derive an expression for D2, the diffusive coupling strength arising from gap-junction connections between inhibitory neurons. Using recent neurophysiological measurements reported by Fukuda [J. Neurosci. 26, 3434 (2006)], we estimate an upper limit of D2 approximately 0.6cm2. We apply a linear stability analysis to a standard mean-field cortical model, augmented with gap-junction diffusion, and find this value for the diffusive coupling strength to be close to the critical value required to destabilize the homogeneous steady state. Computer simulations demonstrate that larger values of D2 cause the noise-driven model cortex to spontaneously crystalize into random mazelike Turing structures: centimeter-scale spatial patterns in which regions of high-firing activity are intermixed with regions of low-firing activity. These structures are consistent with the spatial variations in brain activity patterns detected with the BOLD (blood oxygen-level-dependent) signal detected with magnetic resonance imaging, and may provide a natural substrate for synchronous gamma-band rhythms observed across separated EEG (electroencephalogram) electrodes.

Single-trial EEG dynamics of object and face visual processing

There has been extensive work using early event-related potentials (ERPs) to study visual object processing. ERP analyses focus traditionally on mean amplitude differences, with the implicit assumption that all of the neuronal activity of interest is evoked by the stimulus in a time-locked manner from trial to trial. However, several recent studies have suggested that visual ERP components might be explained to a large extent by the partial phase resetting of ongoing activity in restricted frequency bands. Here we apply that approach to the neural processing of visual objects. We examine the single-trial dynamics of the EEG signal elicited by the presentation of noise textures, houses and faces. We show that the brain response to those stimuli is best explained by amplitude increase that is maximal in the 5- to 15-Hz frequency band. The results indicate also the presence of a substantial increase in phase coherence in the same frequency band. However, analyses of residual activity, after subtracting the mean from single trials, show that this increase in phase coherence is not due to phase resetting per se, but rather to the presence of the ERP+noise in each trial. In keeping with this idea, a simulation demonstrates that a purely evoked model of the ERP produces quantitatively very similar results. Finally, the stronger response to faces compared to other objects (the 'N170 face effect') can be explained by a pure modulation of amplitude centered in the 5- to 15-Hz band.

A theoretical basis for standing and traveling brain waves measured with human EEG with implications for an integrated consciousness

OBJECTIVE

We propose a theoretical framework for EEG and evoked potential studies based on the single postulate that these data are composed of a combination of waves (as this term is used in the physical sciences) and thalamocortical network activity.

METHODS

Using known properties of traveling and standing waves, independent of any neocortical dynamic theory, our simple postulate leads to experimental predictions, several of which have now been verified. A mathematical-physiological theory of "brain waves" based on known (but highly idealized) properties of cortical synaptic action and corticocortical fibers is used to support the framework.

RESULTS

Brain waves are predicted with links between temporal frequencies and the spatial distributions of synaptic activity. Such dispersion relations, which essentially define more general phenomena as waves, are shown to restrict the spatial-temporal dynamics of synaptic action with many experimental EEG consequences.

CONCLUSIONS

The proposed framework accounts for several salient features of spontaneous EEG and evoked potentials.

SIGNIFICANCE

We conjecture that wave-like behavior of synaptic action may facilitate interactions between remote cell assemblies, providing an important mechanism for the functional integration underlying conscious experience.

Timing in cognition and EEG brain dynamics: discreteness versus continuity

This article provides an overview of recent developments in solving the timing problem (discreteness vs. continuity) in cognitive neuroscience. Both theoretical and empirical studies have been considered, with an emphasis on the framework of operational architectonics (OA) of brain functioning (Fingelkurts and Fingelkurts in Brain Mind 2:291-29, 2001; Neurosci Biobehav Rev 28:827-836, 2005). This framework explores the temporal structure of information flow and interarea interactions within the network of functional neuronal populations by examining topographic sharp transition processes in the scalp EEG, on the millisecond scale. We conclude, based on the OA framework, that brain functioning is best conceptualized in terms of continuity-discreteness unity which is also the characteristic property of cognition. At the end we emphasize where one might productively proceed for the future research.

Neurophysiological markers of contextual processing: the relationship between P3b and Gamma synchrony and their modulation by arousal, performance and individual differences

The ability to identify and respond to significant events in the environment is a vital aspect of human cognition and yet is poorly understood as a dynamic neural process. While the response to a contextually-relevant stimulus involves a number of complimentary processes, including selective attention and neural binding, it is also subject to modulation by factors like arousal, age and sex. Adopting an integrative approach, we investigated contextual processing (as indexed by P3b and Gamma phase synchrony) in 120 healthy subjects performing an auditory oddball task while controlling for these other modulating factors. Results suggest a relationship between P3b and Gamma-2 synchrony in posterior regions only, with phasic anterior processing seemingly unrelated to that in posterior regions. However, only the P3b was significantly correlated to central and autonomic arousal. Further, while age and sex were associated with variation in individual measures, they did not strongly affect the relationship between the measures. We concluded that, in simple contextual processing, global and local elements of target stimuli are processed in parallel with little variation being shown between the sexes or resulting from increasing age.

Selecting parameters for phase space reconstruction of the electrocorticogram (ECoG)

The selection of parameters for phase space reconstruction of empirically observed data has been a source of criticism when estimating the correlation dimension (D2) from observed data rather than from the solution of differential equations, when analyzing noisy and potentially non-stationary signals, such as the electroencephalogram (EEG). The largely arbitrary selection of the time-delay reconstruction (T) of temporal dynamics, and for the embedding (M) of these series, has been widely criticized. This study adopted an analytic and statistical framework within which the scaling behavior of D2 with respect to T and M, could be examined over five data lengths (N = 4096, 8192, 12288, 16384, and 20480) over an 8 x 8 grid of cat EEG. It was found that D2 was invariant over all data lengths only within a very narrow T range (T = 10-16) for M = 4. A statistically significant T by M interaction was found using multiple analysis of variance, with D2 being highly correlated over T as a function of M. Finally, an examination of phase-randomized surrogates indicated that statistically significant differences existed between EEG and phase-randomized surrogates over all data lengths, with time delays (T = 10-16), indicating that the D2 for EEG is phase-dependent when it is invariant with respect to data length. The implications of these findings are discussed with respect to current models of ECoG generation, and their implication with respect to the integration in the brain.

Stochastic models of neuronal dynamics

Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microscopic behaviour of neurons. An evoked response potential (ERP) is the mean electrical potential measured from an electrode on the scalp, in response to some event. The purpose of this paper is to outline a population density approach to modelling ERPs. We propose a biologically plausible model of neuronal activity that enables the estimation of physiologically meaningful parameters from electrophysiological data. The model encompasses four basic characteristics of neuronal activity and organization: (i) neurons are dynamic units, (ii) driven by stochastic forces, (iii) organized into populations with similar biophysical properties and response characteristics and (iv) multiple populations interact to form functional networks. This leads to a formulation of population dynamics in terms of the Fokker-Planck equation. The solution of this equation is the temporal evolution of a probability density over state-space, representing the distribution of an ensemble of trajectories. Each trajectory corresponds to the changing state of a neuron. Measurements can be modelled by taking expectations over this density, e.g. mean membrane potential, firing rate or energy consumption per neuron. The key motivation behind our approach is that ERPs represent an average response over many neurons. This means it is sufficient to model the probability density over neurons, because this implicitly models their average state. Although the dynamics of each neuron can be highly stochastic, the dynamics of the density is not. This means we can use Bayesian inference and estimation tools that have already been established for deterministic systems. The potential importance of modelling density dynamics (as opposed to more conventional neural mass models) is that they include interactions among the moments of neuronal states (e.g. the mean depolarization may depend on the variance of synaptic currents through nonlinear mechanisms).Here, we formulate a population model, based on biologically informed model-neurons with spike-rate adaptation and synaptic dynamics. Neuronal sub-populations are coupled to form an observation model, with the aim of estimating and making inferences about coupling among sub-populations using real data. We approximate the time-dependent solution of the system using a bi-orthogonal set and first-order perturbation expansion. For didactic purposes, the model is developed first in the context of deterministic input, and then extended to include stochastic effects. The approach is demonstrated using synthetic data, where model parameters are identified using a Bayesian estimation scheme we have described previously.

Separating individual skin conductance responses in a short interstimulus-interval paradigm

We describe a new method for measuring skin conductance responses, designed to overcome the problem of overlapping skin conductance responses. The method relies on the assumptions that the underlying sudomotor nerve signal has a shorter time-constant than the skin conductance signal itself, and that the sudomotor bursts arrive as discrete, separated events. By converting the skin conductance signal into a time-series with a shorter time-constant, we are able to extract the separated peaks in the estimated underlying driver signal. The separated driver peaks are then used to re-estimate each individual skin conductance response. The method is automated and applied to a normative database of 735 subjects, for which skin conductance was measured during an auditory oddball paradigm.

Stabilization of bimanual coordination due to active interhemispheric inhibition: a dynamical account

Based on recent brain-imaging data and congruent theoretical insights, a dynamical model is derived to account for the patterns of brain activity observed during stable performance of bimanual multifrequency patterns, as well as during behavioral instabilities in the form of phase transitions between such patterns. The model incorporates four dynamical processes, defined over both motor and premotor cortices, which are coupled through inhibitory and excitatory inter- and intrahemispheric connections. In particular, the model underscores the crucial role of interhemispheric inhibition in reducing the interference between disparate frequencies during stable performance, as well as the failure of this reduction during behavioral transitions. As an aside, the model also accounts for in- and antiphase preferences during isofrequency movements. The viability of the proposed model is illustrated by magnetoencephalographic signals that were recorded from an experienced subject performing a polyrhythmic tapping task that was designed to induce transitions between multifrequency patterns. Consistent with the models dynamics, contra- and ipsilateral cortical areas of activation were frequency- and phase-locked, while their activation strength changed markedly in the vicinity of transitions in coordination.

The Role of Neural Synchronization in the Emergence of Cognition Across the Wake-Sleep Cycle

Searching for the neural code underlying consciousness and cognition is one of the most important activities in contemporary neuroscience. Research with neuronal oscillations at the level of single-neuron, local cell assemblies, and network system have provided invaluable insights into different mechanisms of synaptic interactions involved in the emergence of cognitive acts. A cognitive neuroscience of conscious experience is gradually emerging from behavioral and neuroimaging studies, which can be successfully complemented with the quantitative EEG findings discussed here. This review is an attempt to highlight the value of state-dependent changes in human neurophysiology for a better understanding of the neurobiological substrate underlying those aspects of cognition drastically affected by sleep states. Recent advances related to synchronization mechanisms potentially involved in brain integration processes are discussed, emphasizing the value of scalp and intracranial EEG recordings at determining local and large-scale dynamics in the human brain. Evidence supporting the critical role of state-dependent synchrony in brain integration comes mainly from studies on the theta and gamma oscillations across the wake-sleep continuum, as revealed by human intracranial recordings. This review blends results from different levels of analysis with the firm conviction that state-dependent brain dynamics at different levels of neural integration can provide a deeper understanding of neurobiological correlates of consciousness and sleep functions.

Dynamics of thalamo-cortical network oscillations and human perception

There is increasing evidence that human cognitive functions can be addressed from a robust neuroscience perspective. In particular, the distributed coherent electrical properties of central neuronal ensembles are considered to be a promising avenue of inquiry concerning global brain functions. The intrinsic oscillatory properties of neurons (Llinás, R. (1988) The intrinsic electrophysiological properties of mammalian neurons: Insights into central nervous system function. Science, 242: 1654-1664), supported by a large variety of voltage-gated ionic conductances are recognized to be the central elements in the generation of the temporal binding required for cognition. Research in neuroscience further indicates that oscillatory activity in the gamma band (25-50 Hz) can be correlated with both sensory acquisition and pre-motor planning, which are non-continuous functions in the time domain. From this perspective, gamma-band activity is viewed as serving a broad temporal binding function, where single-cell oscillators and the conduction time of the intervening pathways support large multicellular thalamo-cortical resonance that is closely linked with cognition and subjective experience. Our working hypothesis is that although dedicated units achieve sensory processing, the cognitive binding process is a common mechanism across modalities. Moreover, it is proposed that such time-dependent binding when altered, will result in modifications of the sensory motor integration that will affect and impair cognition and conscious perception.

Mining event-related brain dynamics

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.

Induced visual illusions and gamma oscillations in human primary visual cortex

Using magnetoencephalography, we studied the spatiotemporal properties of cortical responses in terms of event-related synchronization and event-related desynchronization to a range of stripe patterns in subjects with no neurological disorders. These stripes are known for their tendency to induce a range of abnormal sensations, such as illusions, nausea, dizziness, headache and attacks of pattern-sensitive epilepsy. The optimal stimulus must have specific physical properties, and maximum abnormalities occur at specific spatial frequency and contrast. Despite individual differences in the severity of discomfort experienced, psychophysical studies have shown that most observers experience some degree of visual anomaly on viewing such patterns. In a separate experiment, subjects reported the incidence of illusions and discomfort to each pattern. We found maximal cortical power in the gamma range (30-60 Hz) confined to the region of the primary visual cortex in response to patterns of 2-4 cycles per degree, peaking at 3 cycles per degree. This coincides with the peak of mean illusions and discomfort, also maximal for patterns of 2-4 cycles per degree. We show that gamma band activity in V1 is a narrow band function of spatial frequency. We hypothesize that the intrinsic properties of gamma oscillations may underlie visual discomfort and play a role in the onset of seizures.

Making complexity simpler: multivariability and metastability in the brain

This article provides a retrospective, current, and prospective overview on developments in brain research and neuroscience. Both theoretical and empirical studies are considered, with emphasis in the concept of multivariability and metastability in the brain. In this new view on the human brain, the potential multivariability of the neuronal networks appears to be far from continuous in time, but confined by the dynamics of short-term local and global metastable brain states. The article closes by suggesting some of the implications of this view in future multidisciplinary brain research.

Intrinsic connections in tree shrew V1 imply a global to local mapping

The local-global map hypothesis states that locally organized response properties--such as orientation preference--result from visuotopically organized local maps of non-retinotopic response properties. In the tree shrew, the lateral extent of horizontal patchy connections is as much as 80-100% of V1 and is consistent with the length summation property. We argue that neural signals can be transmitted across the entire extent of V1 and this allows the formation of maps at the local scale that are visuotopically organized. We describe mechanisms relevant to the formation of local maps and report modeling results showing the same patterns of horizontal connectivity, and relationships to orientation preference, seen in vivo. The structure of the connectivity that emerges in the simulations reveals a 'hub and spoke' organization. Singularities form the centers of local maps, and linear zones and saddle-points arise as smooth border transitions between maps. These findings are used to present the case for the local-global map hypothesis for tree shrew V1.

Local and remote functional connectivity of neocortex under the inhibition influence

The current paper focuses on a relatively new and promising area of the study of EEG transformations during brain information processing based on the reduction of the signal to the discrete quasi-stationary segment sequences which may reflect individual brain microstates or discrete operations. In this framework, the complex brain functions require integration of several operations throughout the whole neocortex. However, the role of inhibitory brain systems in such processes is still unsettled. The effects of a single dose (30 microg/kg) of lorazepam on the operational activity of neuronal populations and on the temporal binding between them were examined in a double-blind randomized crossover placebo-controlled study with eight healthy volunteers. EEG measures at 20 channels were evaluated on two occasions: (1) eyes closed, (2) eyes open. In short, we conducted a two-by-two factorial study where one factor manipulated GABAergic neurotransmission (lorazepam vs. placebo), and the other factor was simply brain state (eyes closed vs. eyes opened). We were primarily interested in the main effect of lorazepam. In the present study, a connection between the mesoscopic level, described by the local functional processes (neuronal assemblies or populations) and the macroscopic level, described as a sequence of metastable brain states (remote functionally synchronized neuronal populations) was established. The role of inhibitory brain systems facilitated by lorazepam in the operational dynamics of neuronal populations and in the process of EEG structural synchrony (SS) (topological peculiarities) was addressed for the first time. It was shown that GABA signaling reorganized the dynamics of local neuronal populations and the remote functional connectivity between them.

Genetics of human prefrontal function

Evolution of the prefrontal cortex was an essential precursor to civilization. During the past decade, it became increasingly obvious that human prefrontal function is under substantial genetic control. In particular, heritability studies of frontal lobe-related neuropsychological function, electrophysiology and neuroimaging have greatly improved our insight. Moreover, the first genes that are relevant for prefrontal function such as catechol-O-methyltransferase (COMT) are currently discovered. In this review, we summarize the present knowledge on the genetics of human prefrontal function. For historical reasons, we discuss the genetics of prefrontal function within the broader concept of general cognitive ability (intelligence). Special emphasis is also given to methodological concerns that need to be addressed when conducting research on the genetics of prefrontal function in humans.

Interactions between membrane conductances underlying thalamocortical slow-wave oscillations

Neurons of the central nervous system display a broad spectrum of intrinsic electrophysiological properties that are absent in the traditional "integrate-and-fire" model. A network of neurons with these properties interacting through synaptic receptors with many time scales can produce complex patterns of activity that cannot be intuitively predicted. Computational methods, tightly linked to experimental data, provide insights into the dynamics of neural networks. We review this approach for the case of bursting neurons of the thalamus, with a focus on thalamic and thalamocortical slow-wave oscillations. At the single-cell level, intrinsic bursting or oscillations can be explained by interactions between calcium- and voltage-dependent channels. At the network level, the genesis of oscillations, their initiation, propagation, termination, and large-scale synchrony can be explained by interactions between neurons with a variety of intrinsic cellular properties through different types of synaptic receptors. These interactions can be altered by neuromodulators, which can dramatically shift the large-scale behavior of the network, and can also be disrupted in many ways, resulting in pathological patterns of activity, such as seizures. We suggest a coherent framework that accounts for a large body of experimental data at the ion-channel, single-cell, and network levels. This framework suggests physiological roles for the highly synchronized oscillations of slow-wave sleep.

Peeling the onion: NMDA dysfunction as a unifying model in schizophrenia

N-methyl-d-aspartate receptor (NMDAR) dysfunction plays a crucial role in schizophrenia, leading to impairments in cognitive coordination. NMDAR agonists (e.g., glycine) ameliorate negative and cognitive symptoms, consistent with NMDAR models. However, not all types of cognitive coordination use NMDAR. Further, not all aspects of cognitive coordination are impaired in schizophrenia, suggesting the need for specificity in applying the cognitive coordination construct.

High-frequency synchronisation in schizophrenia: Too much or too little?

Phillips & Silverstein's focus on schizophrenia as a failure of “cognitive coordination” is welcome. They note that a simple hypothesis of reduced Gamma synchronisation subserving impaired coordination does not fully account for recent observations. We suggest that schizophrenia reflects a dynamic compensation to a core deficit of coordination, expressed either as hyper- or hyposynchronisation, with neurotransmitter systems and arousal as modulatory mechanisms.

No blind schizophrenics: Are NMDA-receptor dynamics involved?

Numerous searches have failed to identify a single co-occurrence of total blindness and schizophrenia. Evidence that blindness causes loss of certain NMDA-receptor functions is balanced by reports of compensatory gains. Connections between visual and anterior cingulate NMDA-receptor systems may help to explain how blindness could protect against schizophrenia.

Setting domain boundaries for convergence of biological and psychological perspectives on cognitive coordination in schizophrenia

The claim that the disorganized subtype of schizophrenia results from glutamate hypofunction is enhanced by consideration of current subtypology of schizophrenia, symptom definition, interdependence of neurotransmitters, and the nature of the data needed to support the hypothesis. Careful specification clarifies the clinical reality of disorganization as a feature of schizophrenia and increases the utility of the subtype.

Schizophrenia: Putting context in context

Although context-processing deficits may be core features of schizophrenia, context remains a poorly defined concept. To test Phillips & Silverstein's model, we need to operationalize context more precisely. We offer several useful ways of framing context and discuss enhancing or facilitating schizophrenic patients' performance under different contextual situations. Furthermore, creativity may be a byproduct of cognitive uncoordination.

Phenomenology, context, and self-experience in schizophrenia

Impairments in cognitive coordination in schizophrenia are supported by phenomenological data that suggest deficits in the processing of visual context. Although the target article is sympathetic to such a phenomenological perspective, we argue that the relevance of phenomenological data for a wider understanding of consciousness in schizophrenia is not sufficiently addressed by the authors.

Guarding against over-inclusive notions of “context”: Psycholinguistic and electrophysiological studies of specific context functions in schizophrenia

Phillips & Silverstein offer an exciting synthesis of ongoing efforts to link the clinical and cognitive manifestations of schizophrenia with cellular accounts of its pathophysiology. We applaud their efforts but wonder whether the highly inclusive notion of “context” adequately captures some important details regarding schizophrenia and NMDA/glutamate function that are suggested by work on language processing and cognitive electrophysiology.