Coherent Activity Patterns Research Articles

Towards a dynamical understanding of microstate analysis of M/EEG data

One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three “classically” used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.

Building Blocks for the Development of a Self-Consistent Electromagnetic Field Theory of Consciousness.

The goal of this work is to compile the basic components for the construction of an electromagnetic field theory of consciousness that meets the standards of a fundamental theory. An essential cornerstone of the conceptual framework is the vacuum state of quantum electrodynamics which, contrary to the classical notion of the vacuum, can be viewed as a vibrant ocean of energy, termed zero-point field (ZPF). Being the fundamental substrate mediating the electromagnetic force, the ubiquitous ZPF constitutes the ultimate bedrock of all electromagnetic phenomena. In particular, resonant interaction with the ZPF is critical for understanding rapidly forming, long-range coherent activity patterns that are characteristic of brain dynamics. Assuming that the entire phenomenal color palette is rooted in the vibrational spectrum of the ZPF and that each normal mode of the ZPF is associated with an elementary shade of consciousness, it stands to reason that conscious states are caused by the coupling of the brain to a particular set of normal modes selectively filtered from the full frequency spectrum of the ZPF. From this perspective, the brain is postulated to function as a resonant oscillator that couples to a specific range of ZPF modes, using these modes as a keyboard for the composition of an enormous variety of phenomenal states. Theoretical considerations suggest that the brain-ZPF interface is controlled by altering the concentrations of neurotransmitters, placing the detailed study of the neurotransmitter-ZPF interaction at the center of future research activities.

Striatal network modeling in Huntington's Disease.

Medium spiny neurons (MSNs) comprise over 90% of cells in the striatum. In vivo MSNs display coherent burst firing cell assembly activity patterns, even though isolated MSNs do not burst fire intrinsically. This activity is important for the learning and execution of action sequences and is characteristically dysregulated in Huntington's Disease (HD). However, how dysregulation is caused by the various neural pathologies affecting MSNs in HD is unknown. Previous modeling work using simple cell models has shown that cell assembly activity patterns can emerge as a result of MSN inhibitory network interactions. Here, by directly estimating MSN network model parameters from single unit spiking data, we show that a network composed of much more physiologically detailed MSNs provides an excellent quantitative fit to wild type (WT) mouse spiking data, but only when network parameters are appropriate for the striatum. We find the WT MSN network is situated in a regime close to a transition from stable to strongly fluctuating network dynamics. This regime facilitates the generation of low-dimensional slowly varying coherent activity patterns and confers high sensitivity to variations in cortical driving. By re-estimating the model on HD spiking data we discover network parameter modifications are consistent across three very different types of HD mutant mouse models (YAC128, Q175, R6/2). In striking agreement with the known pathophysiology we find feedforward excitatory drive is reduced in HD compared to WT mice, while recurrent inhibition also shows phenotype dependency. We show that these modifications shift the HD MSN network to a sub-optimal regime where higher dimensional incoherent rapidly fluctuating activity predominates. Our results provide insight into a diverse range of experimental findings in HD, including cognitive and motor symptoms, and may suggest new avenues for treatment.

The Basal Ganglia: More than just a switching device.

The basal ganglia consist of a variety of subcortical nuclei engaged in motor control and executive functions, such as motor learning, behavioral control, and emotion. The striatum, a major basal ganglia component, is particularly useful for cognitive planning of purposive motor acts owing to its structural features and the neuronal circuitry established with the cerebral cortex. Recent data indicate emergent functions played by the striatum. Indeed, cortico-striatal circuits carrying motor information are paralleled by circuits originating from associative and limbic territories, which are functionally integrated in the striatum. Functional integration between brain areas is achieved through patterns of coherent activity. Coherence belonging to cortico-basal ganglia circuits is also present in Parkinson's disease patients. Excessive synchronization occurring in this pathology is reduced by dopaminergic therapies. The mechanisms through which the dopaminergic effects may be addressed are the object of several ongoing investigations. Overall, the bulk of data reported in recent years has provided new vistas concerning basal ganglia role in the organization and control of movement and behavior, both in physiological and pathological conditions. In this review, basal ganglia functions involved in the organization of main movement categories and behaviors are critically discussed. Comparatively, the multiplicity of Parkinson's disease symptomatology is also revised.

Graph spectral characterization of the XY model on complex networks.

There is recent evidence that the XY spin model on complex networks can display three different macroscopic states in response to the topology of the network underpinning the interactions of the spins. In this work we present a way to characterize the macroscopic states of the XY spin model based on the spectral decomposition of time series using topological information about the underlying networks. We use three different classes of networks to generate time series of the spins for the three possible macroscopic states. We then use the temporal Graph Signal Transform technique to decompose the time series of the spins on the eigenbasis of the Laplacian. From this decomposition, we produce spatial power spectra, which summarize the activation of structural modes by the nonlinear dynamics, and thus coherent patterns of activity of the spins. These signatures of the macroscopic states are independent of the underlying network class and can thus be used as robust signatures for the macroscopic states. This work opens avenues to analyze and characterize dynamics on complex networks using temporal Graph Signal Analysis.

Spontaneous transient synchronization networks emerge from large-scale interactions

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (

Microbial rRNA:rDNA gene ratios may be unexpectedly low due to extracellular DNA preservation in soils

We tested a method of estimating the activity of detectable individual bacterial and archaeal OTUs within a community by calculating ratios of absolute 16S rRNA to rDNA copy numbers. We investigated phylogenetically coherent patterns of activity among soil prokaryotes in non-growing soil communities. ‘Activity ratios’ were calculated for bacteria and archaea in soil sampled from a tropical rainforest and temperate agricultural field and incubated for one year at two levels of moisture availability and with and without carbon additions. Prior to calculating activity ratios, we corrected the relative abundances of OTUs to account for multiple copies of the 16S gene per genome. Although necessary to ensure accurate activity ratios, this correction did not change our interpretation of differences in microbial community composition across treatments. Activity ratios in this study were lower than those previously published (0.0003–210, logarithmic mean=0.24), suggesting significant extracellular DNA preservation. After controlling for the influence of individual incubation jars, significant differences in activity ratios between all members of each phylum were observed. Planctomycetes and Firmicutes had the highest activity ratios and Crenarchaeota had the lowest activity overall. Our results suggest that greater caution should be taken in interpreting soil microbial community data derived from extracted DNA. Indirect extraction methods may be useful in ensuring that microbes identified from extracellular DNA are not erroneously interpreted as components of an active microbial community.

The relative phases of basal ganglia activities dynamically shape effective connectivity in Parkinson's disease.

Optimal phase alignment between oscillatory neural circuits is hypothesized to optimize information flow and enhance system performance. This theory is known as communication-through-coherence. The basal ganglia motor circuit exhibits exaggerated oscillatory and coherent activity patterns in Parkinson's disease. Such activity patterns are linked to compromised motor system performance as evinced by bradykinesia, rigidity and tremor, suggesting that network function might actually deteriorate once a certain level of net synchrony is exceeded in the motor circuit. Here, we characterize the processes underscoring excessive synchronization and its termination. To this end, we analysed local field potential recordings from the subthalamic nucleus and globus pallidus of five patients with Parkinson's disease (four male and one female, aged 37-64 years). We observed that certain phase alignments between subthalamic nucleus and globus pallidus amplified local neural synchrony in the beta frequency band while others either suppressed it or did not induce any significant change with respect to surrogates. The increase in local beta synchrony directly correlated with how long the two nuclei locked to beta-amplifying phase alignments. Crucially, administration of the dopamine prodrug, levodopa, reduced the frequency and duration of periods during which subthalamic and pallidal populations were phase-locked to beta-amplifying alignments. Conversely ON dopamine, the total duration over which subthalamic and pallidal populations were aligned to phases that left beta-amplitude unchanged with respect to surrogates increased. Thus dopaminergic input shifted circuit dynamics from persistent periods of locking to amplifying phase alignments, associated with compromised motoric function, to more dynamic phase alignment and improved motoric function. This effect of dopamine on local circuit resonance suggests means by which novel electrical interventions might prevent resonance-related pathological circuit interactions.

Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity.

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain’s anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous.

Dynamic connectivity at rest predicts attention task performance.

Consistent spatial patterns of coherent activity, representing large-scale networks, have been reliably identified in multiple populations. Most often, these studies have examined "stationary" connectivity. However, there is a growing recognition that there is a wealth of information in the time-varying dynamics of networks which has neural underpinnings, which changes with age and disease and that supports behavior. Using factor analysis of overlapping sliding windows across 25 participants with Parkinson disease (PD) and 21 controls (ages 41-86), we identify factors describing the covarying correlations of regions (dynamic connectivity) within attention networks and the default mode network, during two baseline resting-state and task runs. Cortical regions that support attention networks are affected early in PD, motivating the potential utility of dynamic connectivity as a sensitive way to characterize physiological disruption to these networks. We show that measures of dynamic connectivity are more reliable than comparable measures of stationary connectivity. Factors in the dorsal attention network (DAN) and fronto-parietal task control network, obtained at rest, are consistently related to the alerting and orienting reaction time effects in the subsequent Attention Network Task. In addition, the same relationship between the same DAN factor and the alerting effect was present during tasks. Although reliable, dynamic connectivity was not invariant, and changes between factor scores across sessions were related to changes in accuracy. In summary, patterns of time-varying correlations among nodes in an intrinsic network have a stability that has functional relevance.

Functional connectivity of distant cortical regions: Role of remote synchronization and symmetry in interactions

Functional MRI (fMRI) of ongoing brain activity at rest i.e. without any overt-directed behavior has revealed patterns of coherent activity, so called resting-state functional networks. The dynamical organization of nodes into these functional networks is closely related to the underlying structural connections. However, functional correlations have also been observed between cortical regions without apparent neural links, and mechanisms generating functional connectivity between distant cortical regions are largely unknown. It has been suggested that indirect connections and collective effects governed by the network properties of the cortex play a significant role. We use numerical simulations to investigate these mechanisms with reference to remote synchronization and network symmetry. Neural activity and the inferred hemodynamic response of the network nodes are modeled as sets of self-sustained oscillators, which are embedded in topologies of complex functional brain interactions. The coupling topology is based on connectivity maps derived from fMRI and DTI experiments. Consequently, our network model includes important information on whether direct or indirect neural connections exist between functionally associated regions. In the simulated functional networks, remote synchrony between pairs of nodes clearly arises from symmetry in the interactions, which are quantified by the number of shared neighbors. A larger joint neighborhood positively correlates with a higher level of synchrony. Therefore, our results indicate that a large overlapping neighborhood in complex networks of brain interactions gives rise to functional similarity between distant cortical regions.

Hippocampal Network Oscillations in APP/APLP2-Deficient Mice

The physiological function of amyloid precursor protein (APP) and its two homologues APP-like protein 1 (APLP1) and 2 (APLP2) is largely unknown. Previous work suggests that lack of APP or APLP2 impairs synaptic plasticity and spatial learning. There is, however, almost no data on the role of APP or APLP at the network level which forms a critical interface between cellular functions and behavior. We have therefore investigated memory-related synaptic and network functions in hippocampal slices from three lines of transgenic mice: APPsα-KI (mice expressing extracellular fragment of APP, corresponding to the secreted APPsα ectodomain), APLP2-KO, and combined APPsα-KI/APLP2-KO (APPsα-DM for “double mutants”). We analyzed two prominent patterns of network activity, gamma oscillations and sharp-wave ripple complexes (SPW-R). Both patterns were generally preserved in all strains. We find, however, a significantly reduced frequency of gamma oscillations in CA3 of APLP2-KO mice in comparison to APPsα-KI and WT mice. Network activity, basic synaptic transmission and short-term plasticity were unaltered in the combined mutants (APPsα-DM) which showed, however, reduced long-term potentiation (LTP). Together, our data indicate that APLP2 and the intracellular domain of APP are not essential for coherent activity patterns in the hippocampus, but have subtle effects on synaptic plasticity and fine-tuning of network oscillations.

Selective coupling between theta phase and neocortical fast gamma oscillations during REM-sleep in mice.

BackgroundThe mammalian brain expresses a wide range of state-dependent network oscillations which vary in frequency and spatial extension. Such rhythms can entrain multiple neurons into coherent patterns of activity, consistent with a role in behaviour, cognition and memory formation. Recent evidence suggests that locally generated fast network oscillations can be systematically aligned to long-range slow oscillations. It is likely that such cross-frequency coupling supports specific tasks including behavioural choice and working memory.Principal FindingsWe analyzed temporal coupling between high-frequency oscillations and EEG theta activity (4–12 Hz) in recordings from mouse parietal neocortex. Theta was exclusively present during active wakefulness and REM-sleep. Fast oscillations occurred in two separate frequency bands: gamma (40–100 Hz) and fast gamma (120–160 Hz). Theta, gamma and fast gamma were more prominent during active wakefulness as compared to REM-sleep. Coupling between theta and the two types of fast oscillations, however, was more pronounced during REM-sleep. This state-dependent cross-frequency coupling was particularly strong for theta-fast gamma interaction which increased 9-fold during REM as compared to active wakefulness. Theta-gamma coupling increased only by 1.5-fold.SignificanceState-dependent cross-frequency-coupling provides a new functional characteristic of REM-sleep and establishes a unique property of neocortical fast gamma oscillations. Interactions between defined patterns of slow and fast network oscillations may serve selective functions in sleep-dependent information processing.

Higher-Order Interactions Characterized in Cortical Activity

In the cortex, the interactions among neurons give rise to transient coherent activity patterns that underlie perception, cognition, and action. Recently, it was actively debated whether the most basic interactions, i.e., the pairwise correlations between neurons or groups of neurons, suffice to explain those observed activity patterns. So far, the evidence reported is controversial. Importantly, the overall organization of neuronal interactions and the mechanisms underlying their generation, especially those of high-order interactions, have remained elusive. Here we show that higher-order interactions are required to properly account for cortical dynamics such as ongoing neuronal avalanches in the alert monkey and evoked visual responses in the anesthetized cat. A Gaussian interaction model that utilizes the observed pairwise correlations and event rates and that applies intrinsic thresholding identifies those higher-order interactions correctly, both in cortical local field potentials and spiking activities. This allows for accurate prediction of large neuronal population activities as required, e.g., in brain-machine interface paradigms. Our results demonstrate that higher-order interactions are inherent properties of cortical dynamics and suggest a simple solution to overcome the apparent formidable complexity previously thought to be intrinsic to those interactions.

The role of APP and APLP for synaptic transmission, plasticity, and network function: lessons from genetic mouse models

APP, APLP1, and APLP2 form a family of mammalian membrane proteins with unknown function. APP, however, plays a key role in the molecular pathology of Alzheimer's disease (AD), indicating that it is somehow involved in synaptic transmission, synaptic plasticity, memory formation, and maintenance of neurons. At present, most of our knowledge about the function of APP comes from consequences of AD-related mutations. The native role of APP, and even more of APLP1/2, remains largely unknown. New genetic knockout and knockin models involving several members of the APP/APLP family may yield better insight into the synaptic and systemic functions of these proteins. Here, we summarize recent results from such transgenic animals with special emphasis on synaptic plasticity and coherent patterns of memory-related network activity in the hippocampus. Data from APP knockout mice suggest that this protein is needed for the expression of long-term potentiation (LTP) in aged, but not in juvenile mice. The missing function can be rescued by expressing part of the protein, as well as by blocking inhibition. Double knockout mice lacking APP and APLP2 die shortly after birth indicating that different members of the APP/APLP family can mutually compensate for genetic ablation of single proteins. Recent techniques allow for analysis of tissue with combined defects, e.g., by expressing only part of APP in APLP2 knockout mice or by growing stem cells with multiple deletions on normal slice cultures. Data from these experiments confirm that APP and APLP2 do indeed play an important role in synaptic plasticity. Much less is known about the role of APP/APLP at the network level. Coherent patterns of activity like hippocampal network oscillations are believed to support formation and consolidation of memory. Analysis of such activity patterns in tissue from mice with altered expression of APP/APLP has just started and may shed further light on the importance of these proteins for cognitive functions.

Coherent periodic activity in excitatory neural networks : the role of network connectivity

Neural collective oscillations have been observed in very many context in brain circuits, ranging from ubiquitous γ-oscillations to θ-rythm in the hyppocampus. The origin of these oscillations is commonly associated to the balance between excitation and inhibition in the network, while purely excitatory circuits are believed to lead to “unstructured population bursts” [1]. However, coherent activity patterns have been observed also in “in vivo” measurements of the developing rodent neocortex and hyppocampus for a short period after birth, despite the fact that at this early stage the nature of the involved synapses is essentially excitatory [2]. Of particular interest are the so-called giant depolarizing potentials (GDPs), recurrent oscillations which repetitevely synchronizes a relatively small assembly of neurons and whose degree of synchrony is orchestrate by hub neurons [3]. Furthermore, in their recent study Bonifazi et al. [3] found that the functional connectivity of developing hippocampal networks is characterized by a power-law distribution of the output links with an exponent δ ~ 1.1 – 1.3. On the other hand, computational and theoretical studies of excitatory networks of leaky integrate-and-fire (LIF) neurons have revealed a regime characterized by coherent periodic activity at a macroscopic level both in fully [4] as well as in diluted networks [5]. This regime is characterized by a partial synchronization (PS) among the neurons reflected by a periodic behaviour of collective observables, while the single neuron evolution is quasi-periodic in time. The random dilution in the neuronal connectivity induces a weak form of chaos which vanishes for sufficiently large networks, therefore the PS regime appears to be robust with respect to this kind of disorder [6]. In the same framework, namely pulse-coupled LIF excitatory networks, we have analyzed the role of the average degree of connectivity for the stability of the PS regime. In particular, we have considered directed random network with Erdos-Renyi topology with an average connectivity C corresponding to that of a finite scale-free network characterized by a power-law distribution with a decay exponent 1 <δ < 2. This amounts to have C which scales as N^(2-δ) with the number of neurons in the network. Therefore as two limiting cases massively connected networks are recovered for δ = 1 (where C is proportional to N) and for δ=2 sparse networks (where C is constant). Our analysis has revealed that, for a fixed system size N, coherent periodic dynamics can be observed only for a connectivity C larger than a critical value Г=Г(N) and that this value is independent from the exponent δ. Furthermore, in the thermodynamic limit Г tends to an asymptotic value which depends only how the random connections are generated, namely statically or dynamically. The emergence of collective dynamics is controlled simply by the number of afferent synapses, independent of all the other details in the network topology, in agreement with what found for the synchronization of bursting neurons [6]. Finally, we have found indications that these systems are weakly chaotic, in the sense that in the thermodynamic limit completely regular dynamical behaviours are recovered [5]. Nonetheless, the degree of chaoticity increases by increasing δ and in the sparse network limit we expect to observe a constant maximal Lyapunov exponent, independent by N.

Brain networks associated with sublexical properties of Chinese characters

Cognitive models of reading all assume some division of labor among processing pathways in mapping among print, sound and meaning. Many studies of the neural basis of reading have used task manipulations such as rhyme or synonym judgment to tap these processes independently. Here we take advantage of specific properties of the Chinese writing system to test how differential availability of sublexical information about sound and meaning, as well as the orthographic structure of characters, pseudo-characters and “artificial” control stimuli influence brain activation in the context of the same one-back task. Analyses combine a data-driven approach that identifies temporally coherent patterns of activity over the course of the entire experiment with hypothesis-testing based on the correlation of these patterns with predictors for different stimulus classes. The results reveal a large network of task-related activity. Both the extent of this network and activity in regions commonly observed in studies of Chinese reading are apparently related to task difficulty. Other regions, including temporo-parietal cortex, were sensitive to particular sublexical functional units in mapping among print, sound, and meaning.

Of sleep, seizures and networks

A common property of sleep and epileptic seizures is that both occur because of alteration in network activity within the nervous system. This is represented by an oscillating synchronization of spiking in large groups of neurons from the ever evanescent chaotic yet coherent patterns of activity that underlie sensory and motor operations of the brain. The idea that both slow wave sleep and epileptic seizures may have similar functions in brain homeostasis by purging and consolidating environmental cues is discussed. TMS can be used as a tool to investigate these patterns.

No Simple Brake – the Complex Functions of Inhibitory Synapses

Synaptic inhibition can be viewed as a counterbalance of synaptic excitation. However, multiple recent studies at the cellular and network level show that inhibition serves a variety of additional, highly specific functions in the mammalian nervous system. At the molecular and cellular level, inhibitory synapses express diverse postsynaptic reversal potentials, kinetics, plasticity, and pharmacological modulation. This heterogeneity corresponds to the complexity of inhibition at the network level, where interneurons are now perceived as diverse and highly specific organizers of coherent activity patterns. We review some important new developments in the molecular, cellular and network physiology of inhibition. It turns out that understanding inhibition is a key to understanding neuronal network behaviour and, ultimately, may provide important clues for the development of novel therapeutic strategies in neuro-psychiatric diseases.

Early NMDA receptor‐driven waves of activity in the developing neocortex: physiological or pathological network oscillations?

Several patterns of coherent activity have been described in developing cortical structures, thus providing a general framework for network maturation. A detailed timely description of network patterns at circuit and cell levels is essential for the understanding of pathogenic processes occurring during brain development. Disturbances in the expression timetable of this pattern sequence are very likely to affect network maturation. This review focuses on the maturation of coherent activity patterns in developing neocortical structures. It emphasizes the intrinsic and synaptic cellular properties that are unique to the immature neocortex and, in particular, the critical role played by extracellular glutamate in controlling network excitability and triggering synchronous network waves of activity.