Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that sleep oscillations can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Models with relatively weaker long-range connectivity resulted in mixed states of global and local slow waves. Increase of synaptic strength led to more synchronized global SO. These results were compared to human data to validate probable models of biophysically realistic slow waves. The model producing mixed states provided the best match to the spatial coherence profiles obtained in the human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

The role of age-related sleep EEG changes in memory decline: experiments and computational modeling

The slow oscillation (SO) observed during deep sleep is known to facilitate memory consolidation. However, the impact of age-related changes in sleep electroencephalography (EEG) oscillations and memory remains unknown. In this study, we aimed to investigate the contribution of age-related changes in sleep SO and its role in memory decline by combining EEG recordings and computational modeling. Based on the detected SO events, we found that older adults exhibit lower SO density, lower SO frequency, and longer Up and Down state durations during N3 sleep compared to young and middle-aged groups. Using a biophysically detailed thalamocortical network model, we simulated the "aged" brain as a partial loss of synaptic connections between neurons in the cortex. Our simulations showed that the changes in sleep SO properties in the "aged" brain, similar to those observed in older adults, resulting in impaired memory consolidation. Overall, this study provides mechanistic insights into how age-related changes modulate sleep SOs and memory decline.Clinical Relevance- This study contributes towards finding feasible biomarkers and target mechanism for designing therapy in older adults with memory deficits, such as Alzheimer's disease patients.

EMG-projected MEG High-Resolution Source Imaging of Human Motor Execution: Brain-Muscle Coupling above Movement Frequencies

Magnetoencephalography (MEG) is a non-invasive functional imaging technique for pre-surgical mapping. However, movement-related MEG functional mapping of primary motor cortex (M1) has been challenging in presurgical patients with brain lesions and sensorimotor dysfunction due to the large numbers of trails needed to obtain adequate signal to noise. Moreover, it is not fully understood how effective the brain communication is with the muscles at frequencies above the movement frequency and its harmonics. We developed a novel Electromyography (EMG)-projected MEG source imaging technique for localizing M1 during ~1 minute recordings of left and right self-paced finger movements (~1 Hz). High-resolution MEG source images were obtained by projecting M1 activity towards the skin EMG signal without trial averaging. We studied delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-90 Hz) bands in 13 healthy participants (26 datasets) and two presurgical patients with sensorimotor dysfunction. In healthy participants, EMG-projected MEG accurately localized M1 with high accuracy in delta (100.0%), theta (100.0%), and beta (76.9%) bands, but not alpha (34.6%) and gamma (0.0%) bands. Except for delta, all other frequency bands were above the movement frequency and its harmonics. In both presurgical patients, M1 activity in the affected hemisphere was also accurately localized, despite highly irregular EMG movement patterns in one patient. Altogether, our EMG-projected MEG imaging approach is highly accurate and feasible for M1 mapping in presurgical patients. The results also provide insight into movement related brain-muscle coupling above the movement frequency and its harmonics.

Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks

Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an important role in incremental learning by replaying recent and old conflicting memory traces. Here we tested the hypothesis that implementing a sleep-like phase in artificial neural networks can protect old memories during new training and alleviate catastrophic forgetting. Sleep was implemented as off-line training with local unsupervised Hebbian plasticity rules and noisy input. In an incremental learning framework, sleep was able to recover old tasks that were otherwise forgotten. Previously learned memories were replayed spontaneously during sleep, forming unique representations for each class of inputs. Representational sparseness and neuronal activity corresponding to the old tasks increased while new task related activity decreased. The study suggests that spontaneous replay simulating sleep-like dynamics can alleviate catastrophic forgetting in artificial neural networks.

Satiety Associated with Calorie Restriction and Time-Restricted Feeding: Peripheral Hormones

Calorie restriction (CR) is a common approach to inducing negative energy balance. Recently, time-restricted feeding (TRF), which involves consuming food within specific time windows during a 24-h day, has become popular owing to its relative ease of practice and potential to aid in achieving and maintaining a negative energy balance. TRF can be implemented intentionally with CR, or TRF might induce CR simply because of the time restriction. This review focuses on summarizing our current knowledge on how TRF and continuous CR affect gut peptides that influence satiety. Based on peer-reviewed studies, in response to CR there is an increase in the orexigenic hormone ghrelin and a reduction in fasting leptin and insulin. There is likely a reduction in glucagon-like peptide-1 (GLP-1), peptide YY (PYY), and cholecystokinin (CCK), albeit the evidence for this is weak. After TRF, unlike CR, fasting ghrelin decreased in some TRF studies, whereas it showed no change in several others. Further, a reduction in fasting leptin, insulin, and GLP-1 has been observed. In conclusion, when other determinants of food intake are held equal, the peripheral satiety systems appear to be somewhat similarly affected by CR and TRF with regard to leptin, insulin, and GLP-1. But unlike CR, TRF did not appear to robustly increase ghrelin, suggesting different influences on appetite with a potential decrease of hunger after TRF when compared with CR. However, there are several established and novel gut peptides that have not been measured within the context of CR and TRF, and studies that have evaluated effects of TRF are often short-term, with nonuniform study designs and highly varying temporal eating patterns. More evidence and studies addressing these aspects are needed to draw definitive conclusions.

Satiety Associated with Calorie Restriction and Time-Restricted Feeding: Central Neuroendocrine Integration

This review focuses on summarizing current knowledge on how time-restricted feeding (TRF) and continuous caloric restriction (CR) affect central neuroendocrine systems involved in regulating satiety. Several interconnected regions of the hypothalamus, brainstem, and cortical areas of the brain are involved in the regulation of satiety. Following CR and TRF, the increase in hunger and reduction in satiety signals of the melanocortin system [neuropeptide Y (NPY), proopiomelanocortin (POMC), and agouti-related peptide (AgRP)] appear similar between CR and TRF protocols, as do the dopaminergic responses in the mesocorticolimbic circuit. However, ghrelin and leptin signaling via the melanocortin system appears to improve energy balance signals and reduce hyperphagia following TRF, which has not been reported in CR. In addition to satiety systems, CR and TRF also influence circadian rhythms. CR influences the suprachiasmatic nucleus (SCN) or the primary circadian clock as seen by increased clock gene expression. In contrast, TRF appears to affect both the SCN and the peripheral clocks, as seen by phasic changes in the non-SCN (potentially the elusive food entrainable oscillator) and metabolic clocks. The peripheral clocks are influenced by the primary circadian clock but are also entrained by food timing, sleep timing, and other lifestyle parameters, which can supersede the metabolic processes that are regulated by the primary circadian clock. Taken together, TRF influences hunger/satiety, energy balance systems, and circadian rhythms, suggesting a role for adherence to CR in the long run if implemented using the TRF approach. However, these suggestions are based on only a few studies, and future investigations that use standardized protocols for the evaluation of the effect of these diet patterns (time, duration, meal composition, sufficiently powered) are necessary to verify these preliminary observations.

Replay in Deep Learning: Current Approaches and Missing Biological Elements

Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a critical role in memory formation, retrieval, and consolidation. Replay-like mechanisms have been incorporated in deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge. Replay algorithms have been successfully used in a wide range of deep learning methods within supervised, unsupervised, and reinforcement learning paradigms. In this letter, we provide the first comprehensive comparison between replay in the mammalian brain and replay in artificial neural networks. We identify multiple aspects of biological replay that are missing in deep learning systems and hypothesize how they could be used to improve artificial neural networks.

N-Glycosylation Network Construction and Analysis to Modify Glycans on the Spike (S) Glycoprotein of SARS-CoV-2

Background: The N-glycan structure and composition of the spike (S) protein of SARS-CoV-2 are pertinent to vaccine development and efficacy.

Methods: We reconstructed the glycosylation network based on previously published mass spectrometry data using GNAT, a glycosylation network analysis tool. Our compilation of the network tool had 26 glycosyltransferase and glucosidase enzymes and could infer the pathway of glycosylation machinery based on glycans in the virus spike protein. Once the glycan biosynthesis pathway was generated, we simulated the effect of blocking specific enzymes—swainsonine or deoxynojirimycin for blocking mannosidase-II and indolizidine for blocking alpha-1,6-fucosyltransferase—to see how they would affect the biosynthesis network and the glycans that were synthesized.

Results: The N-glycan biosynthesis network of SARS-CoV-2 spike protein shows an elaborate enzymatic pathway with several intermediate glycans, along with the ones identified by mass spectrometric studies. Of the 26 enzymes, the following were involved—Man-Ia, MGAT1, MGAT2, MGAT4, MGAT5, B3GalT, B4GalT, Man-II, SiaT, ST3GalI, ST3GalVI, and FucT8. Blocking specific enzymes resulted in a substantially modified glycan profile of SARS-CoV-2.

Conclusion: Variations in the final N-glycan profile of the virus, given its site-specific microheterogeneity, are factors in the host response to the infection, vaccines, and antibodies. Heterogeneity in the N-glycan profile of the spike (S) protein and its potential effect on vaccine efficacy or adverse reactions to the vaccines remain unexplored. Here, we provide all the resources we generated—the glycans in the glycoCT xml format and the biosynthesis network for future work.

Bidirectional Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Spiking Activity During NREM Sleep

The dialogue between cortex and hippocampus is known to be crucial for sleep-dependent memory consolidation. During slow wave sleep, memory replay depends on slow oscillation (SO) and spindles in the (neo)cortex and sharp wave-ripples (SWRs) in the hippocampus. The mechanisms underlying interaction of these rhythms are poorly understood. We examined the interaction between cortical SO and hippocampal SWRs in a model of the hippocampo-cortico-thalamic network and compared the results with human intracranial recordings during sleep. We observed that ripple occurrence peaked following the onset of an Up-state of SO and that cortical input to hippocampus was crucial to maintain this relationship. A small fraction of ripples occurred during the Down-state and controlled initiation of the next Up-state. We observed that the effect of ripple depends on its precise timing, which supports the idea that ripples occurring at different phases of SO might serve different functions, particularly in the context of encoding the new and reactivation of the old memories during memory consolidation. The study revealed complex bidirectional interaction of SWRs and SO in which early hippocampal ripples influence transitions to Up-state, while cortical Up-states control occurrence of the later ripples, which in turn influence transition to Down-state.

Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI

Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brain's connection pattern is complex, currently used methods are prone to producing false functional connections. We introduce differential covariance analysis, a new method that uses derivatives of the signal for estimating functional connectivity. We generated neural activities from dynamical causal modeling and a neural network of Hodgkin-Huxley neurons and then converted them to hemodynamic signals using the forward balloon model. The simulated fMRI signals, together with the ground-truth connectivity pattern, were used to benchmark our method with other commonly used methods. Differential covariance achieved better results in complex network simulations. This new method opens an alternative way to estimate functional connectivity.

Can sleep protect memories from catastrophic forgetting?

Continual learning remains an unsolved problem in artificial neural networks. The brain has evolved mechanisms to prevent catastrophic forgetting of old knowledge during new training. Building upon data suggesting the importance of sleep in learning and memory, we tested a hypothesis that sleep protects old memories from being forgotten after new learning. In the thalamocortical model, training a new memory interfered with previously learned old memories leading to degradation and forgetting of the old memory traces. Simulating sleep after new learning reversed the damage and enhanced old and new memories. We found that when a new memory competed for previously allocated neuronal/synaptic resources, sleep replay changed the synaptic footprint of the old memory to allow overlapping neuronal populations to store multiple memories. Our study predicts that memory storage is dynamic, and sleep enables continual learning by combining consolidation of new memory traces with reconsolidation of old memory traces to minimize interference.

Stimulation Augments Spike Sequence Replay and Memory Consolidation during Slow-Wave Sleep

Newly acquired memory traces are spontaneously reactivated during slow-wave sleep (SWS), leading to the consolidation of recent memories. Empirical studies found that sensory stimulation during SWS can selectively enhance memory consolidation with the effect depending on the phase of stimulation. In this new study, we aimed to understand the mechanisms behind the role of sensory stimulation on memory consolidation using computational models implementing effects of neuromodulators to simulate transitions between awake and SWS sleep, and synaptic plasticity to allow the change of synaptic connections due to the training in awake or replay during sleep. We found that when closed-loop stimulation was applied during the Down states of sleep slow oscillation, particularly right before the transition from Down to Up state, it significantly affected the spatiotemporal pattern of the slow waves and maximized memory replay. In contrast, when the stimulation was presented during the Up states, it did not have a significant impact on the slow waves or memory performance after sleep. For multiple memories trained in awake, presenting stimulation cues associated with specific memory trace could selectively augment replay and enhance consolidation of that memory and interfere with consolidation of the others (particularly weak) memories. Our study proposes a synaptic-level mechanism of how memory consolidation is affected by sensory stimulation during sleep.SIGNIFICANCE STATEMENT Stimulation, such as training-associated cues or auditory stimulation, during sleep can augment consolidation of the newly encoded memories. In this study, we used a computational model of the thalamocortical system to describe the mechanisms behind the role of stimulation in memory consolidation during slow-wave sleep. Our study suggests that stimulation preferentially strengthens memory traces when delivered at a specific phase of the slow oscillation, just before the Down to Up state transition when it makes the largest impact on the spatiotemporal pattern of sleep slow waves. In the presence of multiple memories, presenting sensory cues during sleep could selectively strengthen selected memories. Our study proposes a synaptic-level mechanism of how memory consolidation is affected by sensory stimulation during sleep.

Timing between Cortical Slow Oscillations and Heart Rate Bursts during Sleep Predicts Temporal Processing Speed, but Not Offline Consolidation

Central and autonomic nervous system activities are coupled during sleep. Cortical slow oscillations (SOs; <1 Hz) coincide with brief bursts in heart rate (HR), but the functional consequence of this coupling in cognition remains elusive. We measured SO-HR temporal coupling (i.e., the peak-to-peak interval between downstate of SO event and HR burst) during a daytime nap and asked whether this SO-HR timing measure was associated with temporal processing speed and learning on a texture discrimination task by testing participants before and after a nap. The coherence of SO-HR events during sleep strongly correlated with an individual's temporal processing speed in the morning and evening test sessions, but not with their change in performance after the nap (i.e., consolidation). We confirmed this result in two additional experimental visits and also discovered that this association was visit-specific, indicating a state (not trait) marker. Thus, we introduce a novel physiological index that may be a useful marker of state-dependent processing speed of an individual.

Ionic and synaptic mechanisms of seizure generation and epileptogenesis

The biophysical mechanisms underlying epileptogenesis and the generation of seizures remain to be better understood. Among many factors triggering epileptogenesis are traumatic brain injury breaking normal synaptic homeostasis and genetic mutations disrupting ionic concentration homeostasis. Impairments in these mechanisms, as seen in various brain diseases, may push the brain network to a pathological state characterized by increased susceptibility to unprovoked seizures. Here, we review recent computational studies exploring the roles of ionic concentration dynamics in the generation, maintenance, and termination of seizures. We further discuss how ionic and synaptic homeostatic mechanisms may give rise to conditions which prime brain networks to exhibit recurrent spontaneous seizures and epilepsy.

Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics

BACKGROUND

Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain.

METHOD

We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors.

RESULTS

The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted.

COMPARISON WITH EXISTING METHODS

Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales.

CONCLUSIONS

This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

Coupling of autonomic and central events during sleep benefits declarative memory consolidation

While anatomical pathways between forebrain cognitive and brainstem autonomic nervous centers are well-defined, autonomic-central interactions during sleep and their contribution to waking performance are not understood. Here, we analyzed simultaneous central activity via electroencephalography (EEG) and autonomic heart beat-to-beat intervals (RR intervals) from electrocardiography (ECG) during wake and daytime sleep. We identified bursts of ECG activity that lasted 4-5 s and predominated in non-rapid-eye-movement sleep (NREM). Using event-based analysis of NREM sleep, we found an increase in delta (0.5-4 Hz) and sigma (12-15 Hz) power and an elevated density of slow oscillations (0.5-1 Hz) about 5 s prior to peak of the heart rate burst, as well as a surge in vagal activity, assessed by high-frequency (HF) component of RR intervals. Using regression framework, we show that these Autonomic/Central Events (ACE) positively predicted post-nap improvement in a declarative memory task after controlling for the effects of spindles and slow oscillations from sleep periods without ACE. No such relation was found between memory performance and a control nap. Additionally, NREM ACE negatively correlated with REM sleep and learning in a non-declarative memory task. These results provide the first evidence that coordinated autonomic and central events play a significant role in declarative memory consolidation.

Differential roles of sleep spindles and sleep slow oscillations in memory consolidation

Sleep plays an important role in the consolidation of recent memories. However, the cellular and synaptic mechanisms of consolidation during sleep remain poorly understood. In this study, using a realistic computational model of the thalamocortical network, we tested the role of Non-Rapid Eye Movement (NREM) sleep in memory consolidation. We found that sleep spindles (the hallmark of N2 stage sleep) and slow oscillations (the hallmark of N3 stage sleep) both promote replay of the spike sequences learned in the awake state and replay was localized at the trained network locations. Memory performance improved after a period of NREM sleep but not after the same time period in awake. When multiple memories were trained, the local nature of the spike sequence replay during spindles allowed replay of the distinct memory traces independently, while slow oscillations promoted competition that could prevent replay of the weak memories in a presence of the stronger memory traces. This could lead to extinction of the weak memories unless when sleep spindles (N2 sleep) preceded slow oscillations (N3 sleep), as observed during the natural sleep cycle. Our study presents a mechanistic explanation for the role of sleep rhythms in memory consolidation and proposes a testable hypothesis how the natural structure of sleep stages provides an optimal environment to consolidate memories.

Thalamocortical and intracortical laminar connectivity determines sleep spindle properties

Sleep spindles are brief oscillatory events during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Here, using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the contrasting properties of the core and matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, which projects to middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. This property was sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Furthermore, consistent with human recordings, in the model, spindles occurred independently in the core system but the matrix system spindles commonly co-occurred with core spindles. We also found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of intra- and inter-cortical connectivity can potentially be a mechanism for increased spindle density as has been observed during learning.

Computational model of brain-stem circuit for state-dependent control of hypoglossal motoneurons

In patients with obstructive sleep apnea (OSA), the pharyngeal muscles become relaxed during sleep, which leads to a partial or complete closure of upper airway. Experimental studies suggest that withdrawal of noradrenergic and serotonergic drives importantly contributes to depression of hypoglossal motoneurons and, therefore, may contribute to OSA pathophysiology; however, specific cellular and synaptic mechanisms remain unknown. In this new study, we developed a biophysical network model to test the hypothesis that, to explain experimental observations, the neuronal network for monoaminergic control of excitability of hypoglossal motoneurons needs to include excitatory and inhibitory perihypoglossal interneurons that mediate noradrenergic and serotonergic drives to hypoglossal motoneurons. In the model, the state-dependent activation of the hypoglossal motoneurons was in qualitative agreement with in vivo data during simulated rapid eye movement (REM) and non-REM sleep. The model was applied to test the mechanisms of action of noradrenergic and serotonergic drugs during REM sleep as observed in vivo. We conclude that the proposed minimal neuronal circuit is sufficient to explain in vivo data and supports the hypothesis that perihypoglossal interneurons may mediate state-dependent monoaminergic drive to hypoglossal motoneurons. The population of the hypothesized perihypoglossal interneurons may serve as novel targets for pharmacological treatment of OSA. NEW & NOTEWORTHY In vivo studies suggest that during rapid eye movement sleep, withdrawal of noradrenergic and serotonergic drives critically contributes to depression of hypoglossal motoneurons (HMs), which innervate the tongue muscles. By means of a biophysical model, which is consistent with a broad range of empirical data, we demonstrate that the neuronal network controlling the excitability of HMs needs to include excitatory and inhibitory interneurons that mediate noradrenergic and serotonergic drives to HMs.

Role of KCC2-dependent potassium efflux in 4-Aminopyridine-induced Epileptiform synchronization

A balance between excitation and inhibition is necessary to maintain stable brain network dynamics. Traditionally, seizure activity is believed to arise from the breakdown of this delicate balance in favor of excitation with loss of inhibition. Surprisingly, recent experimental evidence suggests that this conventional view may be limited, and that inhibition plays a prominent role in the development of epileptiform synchronization. Here, we explored the role of the KCC2 co-transporter in the onset of inhibitory network-induced seizures. Our experiments in acute mouse brain slices, of either sex, revealed that optogenetic stimulation of either parvalbumin- or somatostatin-expressing interneurons induced ictal discharges in rodent entorhinal cortex during 4-aminopyridine application. These data point to a proconvulsive role of GABAA receptor signaling that is independent of the inhibitory input location (i.e., dendritic vs. somatic). We developed a biophysically realistic network model implementing dynamics of ion concentrations to explore the mechanisms leading to inhibitory network-induced seizures. In agreement with experimental results, we found that stimulation of the inhibitory interneurons induced seizure-like activity in a network with reduced potassium A-current. Our model predicts that interneuron stimulation triggered an increase of interneuron firing, which was accompanied by an increase in the intracellular chloride concentration and a subsequent KCC2-dependent gradual accumulation of the extracellular potassium promoting epileptiform ictal activity. When the KCC2 activity was reduced, stimulation of the interneurons was no longer able to induce ictal events. Overall, our study provides evidence for a proconvulsive role of GABAA receptor signaling that depends on the involvement of the KCC2 co-transporter.

Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals.

Cellular and neurochemical basis of sleep stages in the thalamocortical network

The link between the combined action of neuromodulators in the brain and global brain states remains a mystery. In this study, using biophysically realistic models of the thalamocortical network, we identified the critical intrinsic and synaptic mechanisms, associated with the putative action of acetylcholine (ACh), GABA and monoamines, which lead to transitions between primary brain vigilance states (waking, non-rapid eye movement sleep [NREM] and REM sleep) within an ultradian cycle. Using ECoG recordings from humans and LFP recordings from cats and mice, we found that during NREM sleep the power of spindle and delta oscillations is negatively correlated in humans and positively correlated in animal recordings. We explained this discrepancy by the differences in the relative level of ACh. Overall, our study revealed the critical intrinsic and synaptic mechanisms through which different neuromodulators acting in combination result in characteristic brain EEG rhythms and transitions between sleep stages.

Hippocampal CA1 Ripples as Inhibitory Transients

Memories are stored and consolidated as a result of a dialogue between the hippocampus and cortex during sleep. Neurons active during behavior reactivate in both structures during sleep, in conjunction with characteristic brain oscillations that may form the neural substrate of memory consolidation. In the hippocampus, replay occurs within sharp wave-ripples: short bouts of high-frequency activity in area CA1 caused by excitatory activation from area CA3. In this work, we develop a computational model of ripple generation, motivated by in vivo rat data showing that ripples have a broad frequency distribution, exponential inter-arrival times and yet highly non-variable durations. Our study predicts that ripples are not persistent oscillations but result from a transient network behavior, induced by input from CA3, in which the high frequency synchronous firing of perisomatic interneurons does not depend on the time scale of synaptic inhibition. We found that noise-induced loss of synchrony among CA1 interneurons dynamically constrains individual ripple duration. Our study proposes a novel mechanism of hippocampal ripple generation consistent with a broad range of experimental data, and highlights the role of noise in regulating the duration of input-driven oscillatory spiking in an inhibitory network.

Our memories are consolidated while we sleep through a bidirectional exchange of information between two brain areas called cortex and hippocampus. Neurons that were active in behavioral tasks reactivate again in both structures during sleep in a process of linking and modifying memories from the short term storage of the hippocampus to permanent storage in the neocortex. This process occurs mainly during short oscillatory hippocampal electrical events called sharp wave-ripples. We propose a novel mechanism of ripple generation consistent with a wide range of experimental data, to explain how hippocampal network properties shape ripple frequency and duration. Understanding the neuronal mechanism underlying ripples is crucial to explaining how the interaction between hippocampus and cortex during sleep enables memory consolidation.

Coupling of Thalamocortical Sleep Oscillations Are Important for Memory Consolidation in Humans

Sleep, specifically non-rapid eye movement (NREM) sleep, is thought to play a critical role in the consolidation of recent memories. Two main oscillatory activities observed during NREM, cortical slow oscillations (SO, 0.5–1.0Hz) and thalamic spindles (12–15Hz), have been shown to independently correlate with memory improvement. Yet, it is not known how these thalamocortical events interact, or the significance of this interaction, during the consolidation process. Here, we found that systemic administration of the GABAergic drug (zolpidem) increased both the phase-amplitude coupling between SO and spindles, and verbal memory improvement in humans. These results suggest that thalamic spindles that occur during transitions to the cortical SO Up state are optimal for memory consolidation. Our study predicts that the timely interactions between cortical and thalamic events during consolidation, contribute to memory improvement and is mediated by the level of inhibitory neurotransmission.

Phencyclidine Disrupts the Auditory Steady State Response in Rats

The Auditory Steady-State Response (ASSR) in the electroencephalogram (EEG) is usually reduced in schizophrenia (SZ), particularly to 40 Hz stimulation. The gamma frequency ASSR deficit has been attributed to N-methyl-D-aspartate receptor (NMDAR) hypofunction. We tested whether the NMDAR antagonist, phencyclidine (PCP), produced similar ASSR deficits in rats. EEG was recorded from awake rats via intracranial electrodes overlaying the auditory cortex and at the vertex of the skull. ASSRs to click trains were recorded at 10, 20, 30, 40, 50, and 55 Hz and measured by ASSR Mean Power (MP) and Phase Locking Factor (PLF). In Experiment 1, the effect of different subcutaneous doses of PCP (1.0, 2.5 and 4.0 mg/kg) on the ASSR in 12 rats was assessed. In Experiment 2, ASSRs were compared in PCP treated rats and control rats at baseline, after acute injection (5 mg/kg), following two weeks of subchronic, continuous administration (5 mg/kg/day), and one week after drug cessation. Acute administration of PCP increased PLF and MP at frequencies of stimulation below 50 Hz, and decreased responses at higher frequencies at the auditory cortex site. Acute administration had a less pronounced effect at the vertex site, with a reduction of either PLF or MP observed at frequencies above 20 Hz. Acute effects increased in magnitude with higher doses of PCP. Consistent effects were not observed after subchronic PCP administration. These data indicate that acute administration of PCP, a NMDAR antagonist, produces an increase in ASSR synchrony and power at low frequencies of stimulation and a reduction of high frequency (> 40 Hz) ASSR activity in rats. Subchronic, continuous administration of PCP, on the other hand, has little impact on ASSRs. Thus, while ASSRs are highly sensitive to NMDAR antagonists, their translational utility as a cross-species biomarker for NMDAR hypofunction in SZ and other disorders may be dependent on dose and schedule.

Electrogenic properties of the Na⁺/K⁺ ATPase control transitions between normal and pathological brain states

Ionic concentrations fluctuate significantly during epileptic seizures. In this study, using a combination of in vitro electrophysiology, computer modeling, and dynamical systems analysis, we demonstrate that changes in the potassium and sodium intra- and extracellular ion concentrations ([K(+)] and [Na(+)], respectively) during seizure affect the neuron dynamics by modulating the outward Na(+)/K(+) pump current. First, we show that an increase of the outward Na(+)/K(+) pump current mediates termination of seizures when there is a progressive increase in the intracellular [Na(+)]. Second, we show that the Na(+)/K(+) pump current is crucial in maintaining the stability of the physiological network state; a reduction of this current leads to the onset of seizures via a positive-feedback loop. We then present a novel dynamical mechanism for bursting in neurons with a reduced Na(+)/K(+) pump. Overall, our study demonstrates the profound role of the current mediated by Na(+)/K(+) ATPase on the stability of neuronal dynamics that was previously unknown.

Disrupted gamma-band neural oscillations during coherent motion perception in heavy cannabis users

Previous work in animals and humans has shown that exogenous cannabinoids disrupt time-locked, evoked gamma oscillations (30-80 Hz). However, no studies to date have examined the effect of cannabis on non-time-locked, induced gamma oscillations during more complex Gestalt perception. The current study therefore utilized electroencephalography (EEG) to examine gamma oscillations during coherent motion perception in heavy cannabis users and controls. Chronic cannabis users (n = 24; 12 h abstinence before study; positive 11-nor-9-carboxy-delta-9-tetrahydrocannabinol urine levels) and cannabis-naive controls (n = 23) were evaluated. Stimuli consisted of random dot kinetograms (RDKs) that subjects passively viewed during three different conditions: coherent motion, incoherent motion, and static. Time × frequency analysis on EEG data was performed using Fourier-based mean trial power (MTP). Transient event-related potentials (ERPs) to stimulus onset (visual N100 and P200 components) were also evaluated. The results showed that the coherent motion condition produced a robust increase in neural activity in the gamma range (induced power from 40 to 59 Hz) as compared with the incoherent motion and static conditions. As predicted, the cannabis group showed significant reductions in induced gamma power in the coherent condition relative to healthy controls. No differences were observed between the groups in the N100 or P200 components, indicating intact primary sensory processing. Finally, cannabis users showed a trend toward increased scores on the Chapman Perceptual Aberration Scale (PAS) that was positively correlated with total years of active cannabis use. These data suggest that cannabis use may interfere with the generation of induced gamma-band neural oscillations that could in part mediate the perceptual-altering effects of exogenous cannabinoids.

Multipulse phase resetting curves

In this paper, we introduce and study systematically, in terms of phase response curves, the effect of dual-pulse excitation on the dynamics of an autonomous oscillator. Specifically, we test the deviations from linear summation of phase advances resulting from two small perturbations. We analytically derive a correction term, which generally appears for oscillators whose intrinsic dimensionality is >1. The nonlinear correction term is found to be proportional to the square of the perturbation. We demonstrate this effect in the Stuart-Landau model and in various higher dimensional neuronal models. This deviation from the superposition principle needs to be taken into account in studies of networks of pulse-coupled oscillators. Further, this deviation could be used in the verification of oscillator models via a dual-pulse excitation.

Dynamics of high-frequency synchronization during seizures

Pathological synchronization of neuronal firing is considered to be an inherent property of epileptic seizures. However, it remains unclear whether the synchrony increases for the high-frequency multiunit activity as well as for the local field potentials (LFPs). We present spatio-temporal analysis of synchronization during epileptiform activity using wide-band (up to 2,000 Hz) spectral analysis of multielectrode array recordings at up to 60 locations throughout the mouse hippocampus in vitro. Our study revealed a prominent structure of LFP profiles during epileptiform discharges, triggered by elevated extracellular potassium, with characteristic distribution of current sinks and sources with respect to anatomical structure. The cross-coherence of high-frequency activity (500-2,000 Hz) across channels was reduced during epileptic bursts compared with baseline activity and showed the opposite trend for lower frequencies. Furthermore, the magnitude of cross-coherence during epileptiform activity was dependent on distance: electrodes closer to the epileptic foci showed increased cross-coherence and electrodes further away showed reduced cross-coherence for high-frequency activity. These experimental observations were re-created and supported in a computational model. Our study suggests that different intrinsic and synaptic processes can mediate paroxysmal synchronization at low, medium, and high frequencies.

The auditory steady-state response (ASSR): a translational biomarker for schizophrenia

Electrophysiological methods have demonstrated disturbances of neural synchrony and oscillations in schizophrenia which affect a broad range of sensory and cognitive processes. These disturbances may account for a loss of neural integration and effective connectivity in the disorder. The mechanisms responsible for alterations in synchrony are not well delineated, but may reflect disturbed interactions within GABAergic and glutamatergic circuits, particularly in the gamma range. Auditory steady-state responses (ASSRs) provide a non-invasive technique used to assess neural synchrony in schizophrenia and in animal models at specific response frequencies. ASSRs are electrophysiological responses entrained to the frequency and phase of a periodic auditory stimulus generated by auditory pathway and auditory cortex activity. Patients with schizophrenia show reduced ASSR power and phase locking to gamma range stimulation. We review alterations of ASSRs in schizophrenia, schizotypal personality disorder, and first-degree relatives of patients with schizophrenia. In vitro and in vivo approaches have been used to test cellular mechanisms for this pattern of findings. This translational, cross-species approach provides support for the role of N-methyl-D-aspartate and GABAergic dysregulation in the genesis of perturbed ASSRs in schizophrenia and persons at risk.

Steady state responses: electrophysiological assessment of sensory function in schizophrenia

Persons with schizophrenia experience subjective sensory anomalies and objective deficits on assessment of sensory function. Such deficits could be produced by abnormal signaling in the sensory pathways and sensory cortex or later stage disturbances in cognitive processing of such inputs. Steady state responses (SSRs) provide a noninvasive method to test the integrity of sensory pathways and oscillatory responses in schizophrenia with minimal task demands. SSRs are electrophysiological responses entrained to the frequency and phase of a periodic stimulus. Patients with schizophrenia exhibit pronounced auditory SSR deficits within the gamma frequency range (35-50 Hz) in response to click trains and amplitude-modulated tones. Visual SSR deficits are also observed, most prominently in the alpha and beta frequency ranges (7-30 Hz) in response to high-contrast, high-luminance stimuli. Visual SSR studies that have used the psychophysical properties of a stimulus to target specific visual pathways predominantly report magnocellular-based deficits in those with schizophrenia. Disruption of both auditory and visual SSRs in schizophrenia are consistent with neuropathological and magnetic resonance imaging evidence of anatomic abnormalities affecting the auditory and visual cortices. Computational models suggest that auditory SSR abnormalities at gamma frequencies could be secondary to gamma-aminobutyric acid-mediated or N-methyl-D-aspartic acid dysregulation. The pathophysiological process in schizophrenia encompasses sensory processing that probably contributes to alterations in subsequent encoding and cognitive processing. The developmental evolution of these abnormalities remains to be characterized.

Auditory sensory gating in the neonatal ventral hippocampal lesion model of schizophrenia

BACKGROUND/AIMS

The neonatal ventral hippocampal lesion (NVHL) rat model shows biological and behavioral abnormalities similar to schizophrenia. Disturbed sensory gating reflects a consistent neurobiological abnormality in schizophrenia. Although of critical interest, sensory gating has not been evaluated in the NVHL model.

METHODS

The N40 rat analog of the human P50 was measured to assess sensory response and gating in NVHL and sham rats. Epidural electrodes recorded evoked potentials (EPs), from which amplitudes, latencies, difference scores (S1-S2) and gating ratios (S2/S1) were assessed. Power and phase locking were computed for evoked EEG activity, to test for frequency-specific abnormalities.

RESULTS

Prolonged S1 N40 latency was detected in the NVHL group, but amplitude and power measures did not differ. NVHL rats demonstrated disturbed phase-locked sensory gating at theta and beta frequencies, as well as reduced phase-locked gamma activity across stimuli, most robustly at S1.

CONCLUSIONS

While measures of sensory gating obtained from the EP were relatively insensitive to the NVHL model, phase locking across trials was affected. NVHL rats may have increased evoked response temporal variability, similar to patients with schizophrenia. This pattern of findings likely reflects core developmental NVHL disturbances in dorsal hippocampal circuits associated with temporal and frontal areas.

Steady state and induced auditory gamma deficits in schizophrenia

Steady state auditory evoked potentials (SSAEPs) in the electroencephalogram (EEG) and magnetoencephalogram (MEG) have been reported to be reduced in schizophrenia, most consistently to frequencies in the gamma range (40 Hz and greater). The current study evaluated the specificity of this deficit over a broad range of stimulus frequencies and harmonics, the relationship between phase locking and signal power, and whether induced 40 Hz activity was also affected. SSAEPs to amplitude modulated tones from 5 to 50 Hz were obtained from subjects with schizophrenia (SZ) and healthy control subjects in 5 Hz steps. Time-frequency spectral analysis was used to differentiate EEG activity synchronized in phase across trials using Phase Locking Factor (PLF) and Mean Power (MP) change from baseline activity. In the SSAEP frequency response condition, patients with SZ showed broad band reductions in both PLF and MP. In addition, the control subjects showed a more pronounced increase in PLF with increases in power compared to SZ subjects. A noise pulse embedded in 40 Hz stimuli resulted in a transient reduction of PLF and MP at 40 Hz in control subjects, while SZ showed diminished overall PLF. Finally, induced gamma (around 40 Hz) response to unmodulated tone stimuli was also reduced in SZ, indicating that disturbances in this oscillatory activity are not confined to SSAEPs. In summary, SZ subjects show impaired oscillatory responses in the gamma range across a wide variety of experimental conditions. Reduction of PLF along with reduced MP may reflect abnormalities in the auditory cortical circuits, such as a reduction in pyramidal cell volume, spine density and alterations in GABAergic neurons.

The effect of selective attention on the gamma-band auditory steady-state response

Studies have demonstrated that selective attention can modulate the steady-state evoked potential to repetitive visual and tactile stimulation. However, examinations of the effect of attention on the auditory steady-state response (ASSR) have proven equivocal. The current experiment therefore utilized EEG to examine the effect of attention on the ASSR in healthy humans (n=15). Auditory click trains in the beta (20 Hz) and gamma (40 Hz) ranges were randomly presented binaurally in an oddball discrimination paradigm (each frequency served as the oddball (target) in each of two blocks). A Fast Fourier Transform was used to assess the effect of attention on the ASSR (signal power), and phase consistency across trials was assessed using the phase-locking factor (PLF). As expected, both 20 and 40 Hz targets elicited a robust P300 response, with maximal amplitudes over parietal regions. For the ASSR, it was found that EEG signal power was larger to 40 Hz targets compared to 40 Hz frequent stimuli across all frontocentral electrodes. No differences in signal power were observed during 20 Hz stimulation. Finally, increased PLF values were observed for 40 Hz targets compared to frequent trials. These results provide evidence that selective attention can enhance signal power and phase-locking of the ASSR, particularly to auditory stimulation in the gamma range.

Psychophysiological evidence of altered neural synchronization in cannabis use: relationship to schizotypy

OBJECTIVE

Cannabis use may produce neurophysiological disturbances similar to those observed in schizophrenia, particularly in relation to altered neural synchronization. Therefore, the current experiment examined the effect of cannabis use on EEG neural synchronization using the auditory steady-state evoked potential.

METHOD

Auditory steady-state evoked potentials were assessed using varying rates of stimulation (auditory click-trains of 20, 30, 40 Hz) in current cannabis users (N=17) and drug-naive comparison subjects (N=16). EEG spectral power and signal-to-noise ratio at each stimulation frequency were compared between groups.

RESULTS

Cannabis users showed decreased EEG power and signal-to-noise ratio at the stimulation frequency of 20 Hz. In addition, current cannabis users demonstrated increased schizotypal personality characteristics as assessed with the Schizotypal Personality Questionnaire, which positively correlated with total years of cannabis use. Finally, within the cannabis group, 20-Hz power values were negatively correlated with Schizotypal Personality Questionnaire scores.

CONCLUSIONS

These data provide evidence for neural synchronization and early-stage sensory processing deficits in cannabis use. This finding, along with the observed increased rates of schizotypy in cannabis users, adds support for a cannabinoid link to schizophrenia spectrum disorders.

The effect of cannabis use and gender on the visual steady state evoked potential

OBJECTIVE

Cannabis use often induces subjective distortions of perception. However, little work has been done examining the electroencephalographic (EEG) correlates of early sensory processing in cannabis users. The present study therefore examined visual function in cannabis users as assessed via the steady state visual evoked potential. (SSVEP).

METHODS

SSVEPs were examined in current cannabis users (n = 17; 59% male; mean age = 23.2 (S.D. = 5.3)) and drug-naive controls (n = 16; 38% male; mean age = 21.3 (S.D. = 3.1)) to periodic photic stimulation presented at 18 and 25 Hz. The visual SSVEP was quantified via spectral power and the phase-locking factor (PLF) at each frequency of stimulation. The transient N160 event-related potential (ERP) was also evaluated at stimulus onset.

RESULTS

The results showed that for both frequencies, female subjects in general displayed larger visual SSVEPs. A significant gender X group interaction also occurred at 18 Hz of stimulation, and age of onset of cannabis use positively correlated with 18 Hz spectral power values. Finally, the transient N160 component was shown to be reduced in cannabis users, regardless of gender.

CONCLUSIONS

The present study was the first to demonstrate altered visual SSVEPs in cannabis users, and extends previous research demonstrating increased steady state responses in female subjects. While decreased SSVEPs provide initial evidence of altered oscillatory properties in primary visual circuits, reduction of the transient N160 component suggests disruption of later-stage visual processing in cannabis users.

SIGNIFICANCE

These data provide evidence of cannabinoid modulation of sensory/perceptual function in the visual system, and indicates that cannabis use may affect the oscillatory properties of sensory cortical circuits.

Steady state visual evoked potential abnormalities in schizophrenia

OBJECTIVE

The steady state visual evoked potential (SSVEP) can be used to test the frequency response function of neural circuits. Previous studies have shown reduced SSVEPs to alpha and lower frequencies of stimulation in schizophrenia. We investigated SSVEPs in schizophrenia at frequencies spanning the theta (4Hz) to gamma (40Hz) range.

METHODS

The SSVEPs to seven different frequencies of stimulation (4, 8, 17, 20, 23, 30 and 40Hz) were obtained from 18 schizophrenia subjects and 33 healthy control subjects. Power at stimulating frequency (signal power) and power at frequencies above and below the stimulating frequency (noise power) were used to quantify the SSVEP responses.

RESULTS

Both groups showed an inverse relationship between power and frequency of stimulation. Schizophrenia subjects showed reduced signal power compared to healthy control subjects at higher frequencies (above 17Hz), but not at 4 and 8Hz at occipital region. Noise power was higher in schizophrenia subjects at frequencies between 4 and 20Hz over occipital region and at 4, 17 and 20Hz over frontal region.

CONCLUSIONS

SSVEP signal power at beta and gamma frequencies of stimulation were reduced in schizophrenia. Schizophrenia subjects showed higher levels of EEG noise during photic stimulation at beta and lower frequencies.

SIGNIFICANCE

Inability to generate or maintain oscillations in neural networks may contribute to deficits in visual processing in schizophrenia.

Relationship between steady-state and induced gamma activity to motion

When a moving stimulus is presented at a specific temporal frequency, both steady-state responses and induced gamma activity may be elicited in the electroencephalogram. The electroencephalogram was recorded when study participants viewed random dot kinematograms under three conditions: coherent motion, incoherent motion and stationary. Dot position was changed at a rate of 9.3 Hz in the coherent and incoherent conditions. Induced power at 40 Hz was increased during coherent motion compared with the other conditions. In contrast, the steady-state response at 9.3 Hz showed a trend for increased power during the incoherent condition. These results suggest that steady-state responses to moving stimuli reflect sensory activation, while the induced gamma activity indexes perceptual processes.

Neural synchronization deficits to auditory stimulation in bipolar disorder

Patients with bipolar disorder show cognitive deficits and disorganized behavior, which may reflect a disturbance in neural synchronization. We tested whether EEG measures of auditory neural synchronization were abnormal in bipolar disorder. Nineteen symptomatic patients with bipolar disorder and 32 non-psychiatric control subjects were evaluated. Click trains (500 ms duration) presented at 20, 30, 40 and 50 Hz were used to evoke EEG synchronization. Patients with bipolar disorder showed reduced power across the frequencies of stimulation. Phase-locking across trials was also disturbed in bipolar disorder, consistent with poor phase synchronization between the stimulus and EEG. Abnormal high frequency neural synchronization may contribute to cognitive deficits in bipolar disorder.