Cortical Network Dynamics Research Articles

This Month in Anesthesiology

This Month in Anesthesiology

Abnormality of cortical network dynamics in behaving state of an ASD model mouse under VR environments

Abnormality of cortical network dynamics in behaving state of an ASD model mouse under VR environments

Marine and Anthropogenic Bromopyrroles Alter Cellular Ca2+ Dynamics of Murine Cortical Neuronal Networks by Targeting the Ryanodine Receptor and Sarco/Endoplasmic Reticulum Ca2+-ATPase.

Bromopyrroles (BrPyr) are synthesized naturally by marine sponge symbionts and produced anthropogenically as byproducts of wastewater treatment. BrPyr interact with ryanodine receptors (RYRs) and sarco/endoplasmic reticulum (SR/ER) Ca2+-ATPase (SERCA). Influences of BrPyr on the neuronal network activity remain uncharted. BrPyr analogues with differing spectra of RYR/SERCA activities were tested using RYR-null or RYR1-expressing HEK293 and murine cortical neuronal/glial cocultures (NGCs) loaded with Fluo-4 to elucidate their mechanisms altering Ca2+ dynamics. The NGC electrical spike activity (ESA) was measured from NGCs plated on multielectrode arrays. Nanomolar tetrabromopyrrole (TBP, 1) potentiated caffeine-triggered Ca2+ release independent of extracellular [Ca2+] in RYR1-HEK293, whereas higher concentrations produce slow and sustained rise in cytoplasmic [Ca2+] independent of RYR1 expression. TBP, 2,3,5-tribromopyrrole (2), pyrrole (3), 2,3,4-tribromopyrrole (4), and ethyl 4-bromopyrrole-2-carboxylate (5) added acutely to NGC showed differential potency; rank order TBP (IC50 ≈ 220 nM) > 2 ≫ 5, whereas 3 and 4 were inactive at 10 μM. TBP >2 μM elicited sustained elevation of cytoplasmic [Ca2+] and loss of neuronal viability. TBP did not alter network ESA. BrPyr from marine and anthropogenic sources are ecological signaling molecules and emerging anthropogenic pollutants of concern to environmental and human health that potently alter ER Ca2+ dynamics and warrant further investigation in vivo.

Thalamocortical Spectral Transmission Relies on Balanced Input Strengths

The thalamus is a key element of sensory transmission in the brain, as it gates and selects sensory streams through a modulation of its internal activity. A preponderant role in these functions is played by its internal activity in the alpha range ([8–14] Hz), but the mechanism underlying this process is not completely understood. In particular, how do thalamocortical connections convey stimulus driven information selectively over the back-ground of thalamic internally generated activity? Here we investigate this issue with a spiking network model of feedforward connectivity between thalamus and primary sensory cortex reproducing the local field potential of both areas. We found that in a feedforward network, thalamic oscillations in the alpha range do not entrain cortical activity for two reasons: (i) alpha range oscillations are weaker in neurons projecting to the cortex, (ii) the gamma resonance dynamics of cortical networks hampers oscillations over the 10–20 Hz range thus weakening alpha range oscillations. This latter mechanism depends on the balance of the strength of thalamocortical connections toward excitatory and inhibitory neurons in the cortex. Our results highlight the relevance of corticothalamic feedback to sustain alpha range oscillations and pave the way toward an integrated understanding of the sensory streams traveling between the periphery and the cortex.

Balanced networks under spike-time dependent plasticity.

The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory-inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike-timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity-induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.

The extracellular matrix regulates cortical layer dynamics and cross-columnar frequency integration in the auditory cortex

In the adult vertebrate brain, enzymatic removal of the extracellular matrix (ECM) is increasingly recognized to promote learning, memory recall, and restorative plasticity. The impact of the ECM on translaminar dynamics during cortical circuit processing is still not understood. Here, we removed the ECM in the primary auditory cortex (ACx) of adult Mongolian gerbils using local injections of hyaluronidase (HYase). Using laminar current-source density (CSD) analysis, we found layer-specific changes of the spatiotemporal synaptic patterns with increased cross-columnar integration and simultaneous weakening of early local sensory input processing within infragranular layers Vb. These changes had an oscillatory fingerprint within beta-band (25–36 Hz) selectively within infragranular layers Vb. To understand the laminar interaction dynamics after ECM digestion, we used time-domain conditional Granger causality (GC) measures to identify the increased drive of supragranular layers towards deeper infragranular layers. These results showed that ECM degradation altered translaminar cortical network dynamics with a stronger supragranular lead of the columnar response profile.

Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex.

Epileptic seizures are characterized by abnormal and excessive neural activity, where cortical network dynamics seem to become unstable. However, most of the time, during seizure-free periods, cortex of epilepsy patients shows perfectly stable dynamics. This raises the question of how recurring instability can arise in the light of this stable default state. In this work, we examine two potential scenarios of seizure generation: (i) epileptic cortical areas might generally operate closer to instability, which would make epilepsy patients generally more susceptible to seizures, or (ii) epileptic cortical areas might drift systematically towards instability before seizure onset. We analyzed single-unit spike recordings from both the epileptogenic (focal) and the nonfocal cortical hemispheres of 20 epilepsy patients. We quantified the distance to instability in the framework of criticality, using a novel estimator, which enables an unbiased inference from a small set of recorded neurons. Surprisingly, we found no evidence for either scenario: Neither did focal areas generally operate closer to instability, nor were seizures preceded by a drift towards instability. In fact, our results from both pre-seizure and seizure-free intervals suggest that despite epilepsy, human cortex operates in the stable, slightly subcritical regime, just like cortex of other healthy mammalians.

Adult trkB Signaling in Parvalbumin Interneurons is Essential to Prefrontal Network Dynamics.

Inhibitory interneurons expressing parvalbumin (PV) are central to cortical network dynamics, generation of γ oscillations, and cognition. Dysfunction of PV interneurons disrupts cortical information processing and cognitive behavior. Brain-derived neurotrophic factor (BDNF)/tyrosine receptor kinase B (trkB) signaling regulates the maturation of cortical PV interneurons but is also implicated in their adult multidimensional functions. Using a novel viral strategy for cell-type-specific and spatially restricted expression of a dominant-negative trkB (trkB.DN), we show that BDNF/trkB signaling is essential to the integrity and maintenance of prefrontal PV interneurons in adult male and female mice. Reduced BDNF/trkB signaling in PV interneurons in the medial prefrontal cortex (mPFC) resulted in deficient PV inhibition and increased baseline local field potential (LFP) activity in a broad frequency band. The altered network activity was particularly pronounced during increased activation of the prefrontal network and was associated with changed dynamics of local excitatory neurons, as well as decreased modulation of the LFP, abnormalities that appeared to generalize across stimuli and brain states. In addition, our findings link reduced BDNF/trkB signaling in prefrontal PV interneurons to increased aggression. Together our investigations demonstrate that BDNF/trkB signaling in PV interneurons in the adult mPFC is essential to local network dynamics and cognitive behavior. Our data provide direct support for the suggested association between decreased trkB signaling, deficient PV inhibition, and altered prefrontal circuitry.SIGNIFICANCE STATEMENT Brain-derived neurotrophic factor (BDNF)/tyrosine receptor kinase B (trkB) signaling promotes the maturation of inhibitory parvalbumin (PV) interneurons, neurons central to local cortical dynamics, γ rhythms, and cognition. Here, we used a novel viral approach for reduced BDNF/trkB signaling in PV interneurons in the medial prefrontal cortex (mPFC) to establish the role of BDNF/trkB signaling in adult prefrontal network activities. Reduced BDNF/trkB signaling caused pronounced morphologic alterations, reduced PV inhibition, and deficient prefrontal network dynamics. The altered network activity appeared to manifest across stimuli and brain states and was associated with aberrant local field potential (LFP) activities and increased aggression. The results demonstrate that adult BDNF/trkB signaling is essential to PV inhibition and prefrontal circuit function and directly links BDNF/trkB signaling to network integrity in the adult brain.

EEG Decoding of Dynamic Facial Expressions of Emotion: Evidence from SSVEP and Causal Cortical Network Dynamics

The neural cognitive mechanism in processing static facial expressions (FEs) has been well documented, whereas the one underlying perceiving dynamic faces remains unclear. In this study, Fourier transformation and time–frequency analysis of Electroencephalography (EEG) data were carried out to detect the brain activation underlying dynamic or static FEs while twenty-one participants were viewing dynamic or static faces flicking at 10 Hz. In particular, steady-state visual evoked potentials (SSVEPs) were quantified through spectral power analysis of EEG recordings. Besides, Granger causality (GC) analysis (GCA) was also performed to capture the causal cortical network dynamics during dynamic or static FEs of emotion. It was discovered that the dynamic (from neural to happy (N2H) or vice versa (H2N)) FEs elicited larger SSVEPs than the static ones. Additionally, GCA demonstrated that the H2N case, in which happy FEs were being gradually changed into neutral ones, exhibited larger GC measure during the late processing stage than that from the early stage. Consequently, enhanced SSVEPs and effective brain connectivity for dynamic FEs illustrated that participants might need consume more attentional resources to process the dynamic faces, particularly for the change from happy to neutral faces. The new neural index might facilitate us to better understand the cognitive processing of dynamic and static FEs.

The Dynamics of Language Network Interactions in Lexical Selection: An Intracranial EEG Study.

Speaking in sentences requires selection from contextually determined lexical representations. Although posterior temporal cortex (PTC) and Broca's areas play important roles in storage and selection, respectively, of lexical representations, there has been no direct evidence for physiological interactions between these areas on time scales typical of lexical selection. Using intracranial recordings of cortical population activity indexed by high-gamma power (70-150Hz) modulations, we studied the causal dynamics of cortical language networks while epilepsy surgery patients performed a sentence completion task in which the number of potential lexical responses was systematically varied. Prior to completion of sentences with more response possibilities, Broca's area was not only more active, but also exhibited more local network interactions with and greater top-down influences on PTC, consistent with activation of, and competition between, more lexical representations. These findings provide the most direct experimental support yet for network dynamics playing a role in lexical selection among competing alternatives during speech production.

Noise-Correlation Is Modulated by Reward Expectation in the Primary Motor Cortex Bilaterally During Manual and Observational Tasks in Primates.

Reward modulation is represented in the motor cortex (M1) and could be used to implement more accurate decoding models to improve brain-computer interfaces (BCIs; Zhao et al., 2018). Analyzing trial-to-trial noise-correlations between neural units in the presence of rewarding (R) and non-rewarding (NR) stimuli adds to our understanding of cortical network dynamics. We utilized Pearson’s correlation coefficient to measure shared variability between simultaneously recorded units (32–112) and found significantly higher noise-correlation and positive correlation between the populations’ signal- and noise-correlation during NR trials as compared to R trials. This pattern is evident in data from two non-human primates (NHPs) during single-target center out reaching tasks, both manual and action observation versions. We conducted a mean matched noise-correlation analysis to decouple known interactions between event-triggered firing rate changes and neural correlations. Isolated reward discriminatory units demonstrated stronger correlational changes than units unresponsive to reward firing rate modulation, however, the qualitative response was similar, indicating correlational changes within the network as a whole can serve as another information channel to be exploited by BCIs that track the underlying cortical state, such as reward expectation, or attentional modulation. Reward expectation and attention in return can be utilized with reinforcement learning (RL) towards autonomous BCI updating.

Functional reorganization of brain networks across the human menstrual cycle

The brain is an endocrine organ, sensitive to the rhythmic changes in sex hormone production that occurs in most mammalian species. In rodents and nonhuman primates, estrogen and progesterone’s impact on the brain is evident across a range of spatiotemporal scales. Yet, the influence of sex hormones on the functional architecture of the human brain is largely unknown. In this dense-sampling, deep phenotyping study, we examine the extent to which endogenous fluctuations in sex hormones alter intrinsic brain networks at rest in a woman who underwent brain imaging and venipuncture for 30 consecutive days. Standardized regression analyses illustrate estrogen and progesterone’s widespread associations with functional connectivity. Time-lagged analyses examined the temporal directionality of these relationships and suggest that cortical network dynamics (particularly in the Default Mode and Dorsal Attention Networks, whose hubs are densely populated with estrogen receptors) are preceded—and perhaps driven—by hormonal fluctuations. A similar pattern of associations was observed in a follow-up study one year later. Together, these results reveal the rhythmic nature in which brain networks reorganize across the human menstrual cycle. Neuroimaging studies that densely sample the individual connectome have begun to transform our understanding of the brain’s functional organization. As these results indicate, taking endocrine factors into account is critical for fully understanding the intrinsic dynamics of the human brain.

Spatially extended balanced networks without translationally invariant connectivity

Networks of neurons in the cerebral cortex exhibit a balance between excitation (positive input current) and inhibition (negative input current). Balanced network theory provides a parsimonious mathematical model of this excitatory-inhibitory balance using randomly connected networks of model neurons in which balance is realized as a stable fixed point of network dynamics in the limit of large network size. Balanced network theory reproduces many salient features of cortical network dynamics such as asynchronous-irregular spiking activity. Early studies of balanced networks did not account for the spatial topology of cortical networks. Later works introduced spatial connectivity structure, but were restricted to networks with translationally invariant connectivity structure in which connection probability depends on distance alone and boundaries are assumed to be periodic. Spatial connectivity structure in cortical network does not always satisfy these assumptions. We use the mathematical theory of integral equations to extend the mean-field theory of balanced networks to account for more general dependence of connection probability on the spatial location of pre- and postsynaptic neurons. We compare our mathematical derivations to simulations of large networks of recurrently connected spiking neuron models.

Antiepileptic drugs induce subcritical dynamics in human cortical networks

Cortical network functioning critically depends on finely tuned interactions to afford neuronal activity propagation over long distances while avoiding runaway excitation. This importance is highlighted by the pathological consequences and impaired performance resulting from aberrant network excitability in psychiatric and neurological diseases, such as epilepsy. Theory and experiment suggest that the control of activity propagation by network interactions can be adequately described by a branching process. This hypothesis is partially supported by strong evidence for balanced spatiotemporal dynamics observed in the cerebral cortex; however, evidence of a causal relationship between network interactions and cortex activity, as predicted by a branching process, is missing in humans. Here this cause-effect relationship is tested by monitoring cortex activity under systematic pharmacological reduction of cortical network interactions with antiepileptic drugs. This study reports that cortical activity cascades, presented by the propagating patterns of epileptic spikes, as well as temporal correlations decline precisely as predicted for a branching process. The results provide a missing link to the branching process theory of cortical network function with implications for understanding the foundations of cortical excitability and its monitoring in conditions like epilepsy.

Progressive alignment of inhibitory and excitatory delay may drive a rapid developmental switch in cortical network dynamics.

Nervous system maturation occurs on multiple levels-synaptic, circuit, and network-at divergent timescales. For example, many synaptic properties mature gradually, whereas emergent network dynamics can change abruptly. Here we combine experimental and theoretical approaches to investigate a sudden transition in spontaneous and sensory evoked thalamocortical activity necessary for the development of vision. Inspired by in vivo measurements of timescales and amplitudes of synaptic currents, we extend the Wilson and Cowan model to take into account the relative onset timing and amplitudes of inhibitory and excitatory neural population responses. We study this system as these parameters are varied within amplitudes and timescales consistent with developmental observations to identify the bifurcations of the dynamics that might explain the network behaviors in vivo. Our findings indicate that the inhibitory timing is a critical determinant of thalamocortical activity maturation; a gradual decay of the ratio of inhibitory to excitatory onset time drives the system through a bifurcation that leads to a sudden switch of the network spontaneous activity from high-amplitude oscillations to a nonoscillatory active state. This switch also drives a change from a threshold bursting to linear response to transient stimuli, also consistent with in vivo observation. Thus we show that inhibitory timing is likely critical to the development of network dynamics and may underlie rapid changes in activity without similarly rapid changes in the underlying synaptic and cellular parameters.NEW & NOTEWORTHY Relying on a generalization of the Wilson-Cowan model, which allows a solid analytic foundation for the understanding of the link between maturation of inhibition and network dynamics, we propose a potential explanation for the role of developing excitatory/inhibitory synaptic delays in mediating a sudden switch in thalamocortical visual activity preceding vision onset.

Barrel cortex VIP/ChAT interneurons suppress sensory responses in vivo.

Cortical interneurons expressing vasoactive intestinal polypeptide (VIP) and choline acetyltransferase (ChAT) are sparsely distributed throughout the neocortex, constituting only 0.5% of its neuronal population. The co-expression of VIP and ChAT suggests that these VIP/ChAT interneurons (VChIs) can release both γ-aminobutyric acid (GABA) and acetylcholine (ACh). In vitro physiological studies quantified the response properties and local connectivity patterns of the VChIs; however, the function of VChIs has not been explored in vivo. To study the role of VChIs in cortical network dynamics and their long-range connectivity pattern, we used in vivo electrophysiology and rabies virus tracing in the barrel cortex of mice. We found that VChIs have a low spontaneous spiking rate (approximately 1 spike/s) in the barrel cortex of anesthetized mice; nevertheless, they responded with higher fidelity to whisker stimulation than the neighboring layer 2/3 pyramidal neurons (Pyrs). Analysis of long-range inputs to VChIs with monosynaptic rabies virus tracing revealed that direct thalamic projections are a significant input source to these cells. Optogenetic activation of VChIs in the barrel cortex of awake mice suppresses the sensory responses of excitatory neurons in intermediate amplitudes of whisker deflections while increasing the evoked spike latency. The effect of VChI activation on the response was similar for both high-whisking (HW) and low-whisking (LW) conditions. Our findings demonstrate that, despite their sparsity, VChIs can effectively modulate sensory processing in the cortical microcircuit.

Cortical Network Dynamics Is Altered in Mouse Models of Huntington's Disease.

Huntington's disease (HD) is a neurodegenerative disorder characterized by involuntary movements, cognitive deficits, and psychiatric disturbances. Although evidence indicates that projections from motor cortical areas play a key role in the development of dysfunctional striatal activity and motor phenotype, little is known about the changes in cortical microcircuits and their role in the development of the HD phenotype. Here we used two-photon laser-scanning microscopy to evaluate network dynamics of motor cortical neurons in layers II/III in behaving transgenic R6/2 and knock-in Q175+/- mice. Symptomatic R6/2 mice displayed increased motion manifested by a significantly greater number of motion epochs, whereas symptomatic Q175 mice displayed decreased motion. In both models, calcium transients in symptomatic mice displayed reduced amplitude, suggesting decreased bursting activity. Changes in frequency were genotype- and time-dependent; for R6/2 mice, the frequency was reduced during both motion and nonmotion, whereas in symptomatic Q175 mice, the reduction only occurred during nonmotion. In presymptomatic Q175 mice, frequency was increased during both behavioral states. Interneuronal correlation coefficients were generally decreased in both models, suggesting disrupted interneuronal communication in HD cerebral cortex. These results indicate similar and contrasting effects of the HD mutation on cortical ensemble activity depending on mouse model and disease stage.

Neural Interactions in a Spatially-Distributed Cortical Network During Perceptual Decision-Making.

Behavioral experiments evidence that attention is not maintained at a constant level, but fluctuates with time. Recent studies associate such fluctuations with dynamics of attention-related cortical networks, however the exact mechanism remains unclear. To address this issue, we consider functional neuronal interactions during the accomplishment of a reaction time (RT) task which requires sustained attention. The participants are subjected to a binary classification of a large number of presented ambiguous visual stimuli with different degrees of ambiguity. Generally, high ambiguity causes high RT and vice versa. However, we demonstrate that RT fluctuates even when the stimulus ambiguity remains unchanged. The analysis of neuronal activity reveals that the subject's behavioral response is preceded by the formation of a distributed functional network in the β-frequency band. This network is characterized by high connectivity in the frontal cortex and supposed to subserve a decision-making process. We show that neither the network structure nor the duration of its formation depend on RT and stimulus ambiguity. In turn, RT is related to the moment of time when the β-band functional network emerges. We hypothesize that RT is affected by the processes preceding the decision-making stage, e.g., encoding visual sensory information and extracting decision-relevant features from raw sensory information.

Cortical network dynamics are coupled with cognitive aging in rats.

Changes in neuronal network activity and increased interindividual variability in memory are among the most consistent features of growing older. Here, we examined the relationship between these hallmarks of aging. Young and aged rats were trained on a water maze task where aged individuals reliably display an increased range of spatial memory capacities relative to young. Two weeks later, neuronal activity was induced pharmacologically with a low dose of pilocarpine and control animals received vehicle. Activity levels were proxied by quantifying the immediate early gene products Arc and c-Fos. While no relationship was observed between basal, resting activity, and individual differences in spatial memory in any brain region, pilocarpine-induced marker expression was tightly coupled with memory in all areas of the prefrontal cortex (PFC) and hippocampus examined. The nature of this association, however, differed across regions and in relation to age-related cognitive outcome. Specifically, in the medial PFC, induced activity was greatest in aged rats with cognitive impairment and correlated with water maze performance across all subjects. In the hippocampus, the range of induced marker expression was comparable between groups and similarly coupled with memory in both impaired and unimpaired aged rats. Together the findings highlight that the dynamic range of neural network activity across multiple brain regions is a critical component of neurocognitive aging.

Clinical relevance of cortical network dynamics in early primary progressive MS

Background: Structural cortical networks (SCNs) reflect the covariance between the cortical thickness of different brain regions, which may share common functions and a common developmental evolution. SCNs appear abnormal in neurodegenerative conditions such as Alzheimer’s and Parkinson’s diseases, but have never been assessed in primary progressive multiple sclerosis (PPMS). Objective: The aim of this study was to test whether SCNs are abnormal in early PPMS and change over 5 years, and correlate with disability worsening. Methods: A total of 29 PPMS patients and 13 healthy controls underwent clinical and brain magnetic resonance imaging (MRI) assessments for 5 years. Baseline and 5-year follow-up cortical thickness values were obtained and used to build correlation matrices, considered as weighted graphs to obtain network metrics. Bootstrap-based statistics assessed SCN differences between patients and controls and between patients with fast and slow progression. Results: At baseline, patients showed features of lower connectivity (p = 0.02) and efficiency (p < 0.001) than controls. Over 5 years, patients, especially those with fastest clinical progression, showed significant changes suggesting an increase in network connectivity (p < 0.001) and efficiency (p < 0.02), not observed in controls. Conclusion: SCNs are abnormal in early PPMS. Longitudinal SCN changes demonstrated a switch from low- to high-efficiency networks especially among fast progressors, indicating their clinical relevance.