Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night

Macroscale Traveling Waves Evoked by Single-Pulse Stimulation of the Human Brain

Understanding the spatiotemporal dynamics of neural signal propagation is fundamental to unraveling the complexities of brain function. Emerging evidence suggests that corticocortical-evoked potentials (CCEPs) resulting from single-pulse electrical stimulation (SPES) may be used to characterize the patterns of information flow between and within brain networks. At present, the basic spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that SPES evokes neural traveling waves detectable in the three-dimensional space sampled by intracranial stereoelectroencephalography. Across a cohort of 21 adult males and females with intractable epilepsy, we delivered 17,631 stimulation pulses and recorded CCEP responses in 1,019 electrode contacts. The distance between each pair of electrode contacts was approximated using three different metrics (Euclidean distance, path length, and geodesic distance), representing direct, tractographic, and transcortical propagation, respectively. For each robust CCEP, we extracted amplitude-, spectral-, and phase-based features to identify traveling waves emanating from the site of stimulation. Many evoked responses to stimulation appear to propagate as traveling waves (∼14-28%, ∼5-19% with false discovery rate correction), despite sparse sampling throughout the brain. These stimulation-evoked traveling waves exhibited biologically plausible propagation velocities (range, 0.1-9.6 m/s). Our results reveal that direct electrical stimulation elicits neural activity with variable spatiotemporal dynamics that can be modeled as a traveling wave.

Interictal network dysfunction and cognitive impairment in epilepsy

Epilepsy is diagnosed when neural networks become capable of generating excessive or hypersynchronous activity patterns that result in observable seizures. In many cases, epilepsy is associated with cognitive comorbidities that persist between seizures and negatively impact quality of life. Dysregulation of the coordinated physiological network interactions that are required for cognitive function has been implicated in mediating these enduring symptoms, but the causal mechanisms are often elusive. Here, we provide an overview of neural network abnormalities with the potential to contribute to cognitive dysfunction in epilepsy. We examine these pathological interactions across spatial and temporal scales, additionally highlighting the dynamics that arise in response to the brain's intrinsic capacity for plasticity. Understanding these processes will facilitate development of network-level interventions to address cognitive comorbidities that remain undertreated by currently available epilepsy therapeutics.

An Expandable Brain-Machine Interface Enabled by Origami Materials and Structures for Tracking Epileptic Traveling Waves

Tracking neural activities across multiple brain regions remains a daunting challenge due to the non-negligible skull injuries during implantations of large-area electrocorticography (ECoG) grids and the limited spatial accessibility of conventional rectilinear depth probes. Here, a multiregion Brain-machine Interface (BMI) is proposed comprising an expandable bio-inspired origami ECoG electrode covering cortical areas larger than the cranial window, and an expandable origami depth probe capable of reaching multiple deep brain regions beyond a single implantation axis. Using the proposed BMI, it is observed that, in rat models of focal seizures, cortical multiband epileptiform activities mainly manifest as expanding traveling waves outward from a cortical source.

Brain wave dynamics in a Hopfield-Kuramoto model

Whole brain neural oscillation activities exhibit multiple wave phase patterns and seem to be supported by the common circuit network structure. We proposed a Hopfield Kuramoto model based entirely on heterogeneous connectivity strength rather than phase delay. Multiple wave phase patterns can be encoded in heterogeneous connectivity networks via Hebbian rule and retrieved as attractors. We systematically investigated how the model dynamic landscape influenced by attractors and their corresponding eigenvalues, as well as how to control the stability of equilibrium points and the occurrence of high dimensional bifurcations. This framework enables us to reproduce the dominant wave activity components in human brain functional MRI signal, and provides a canonical model for the multi-body physical system spatio-temporal pattern attractor dynamics.

Protocol for detecting and analyzing non-oscillatory traveling waves from high-spatiotemporal-resolution human electrophysiological recordings

Innovations in electrophysiological recordings and computational analytic techniques enable high-resolution analysis of neural traveling waves. Here, we present a protocol for the detection and analysis of traveling waves from multi-day microelectrode array human electrophysiological recordings through a multi-linear regression statistical approach using point estimator data. We describe steps for traveling wave detection, feature characterization, and propagation pattern analysis. This protocol may improve our understanding of the coordination of neurons during non-oscillatory neural dynamics. For complete details on the use and execution of this protocol, please refer to Smith et al.1.

Neuronal traveling waves form preferred pathways using synaptic plasticity

Traveling waves of neuronal spiking activity are commonly observed across the brain, but their intrinsic function is still a matter of investigation. Experiments suggest that they may be valuable in the consolidation of memory or learning, indicating that consideration of traveling waves in the presence of plasticity might be important. A possible outcome of this consideration is that the synaptic pathways, necessary for the propagation of these waves, will be modified by the waves themselves. This will create a feedback loop where both the traveling waves and the strengths of the available synaptic pathways will change. To computationally investigate this, we model a sheet of cortical tissue by considering a quasi two-dimensional network of model neurons locally connected with plastic synaptic weights using Spike-Timing Dependent Plasticity (STDP). By using different stimulation conditions (central, stochastic, and alternating stimulation), we demonstrate that starting from a random network, traveling waves with STDP will form and strengthen propagation pathways. With progressive formation of traveling waves, we observe increases in synaptic weight along the direction of wave propagation, increases in propagation speed when pathways are strengthened over time, and an increase in the local order of synaptic weights. We also present evidence that the interaction between traveling waves and plasticity can serve as a mechanism of network-wide competition between available pathways. With an improved understanding of the interactions between traveling waves and synaptic plasticity, we can approach a fuller understanding of mechanisms of learning, computation, and processing within the brain.

Bifurcations and collective states of Kuramoto oscillators with higher-order interactions and rotational symmetry breaking

We study a network of identical Kuramoto oscillators with higher-order interactions that also break the rotational symmetry of the system. To gain analytical insights into this model, we use the Watanabe-Strogatz Ansatz, which allows us to reduce the dimensionality of the original system of equations. The study of stability and bifurcations of the reduced system reveals a codimension two Bogdanov-Takens bifurcation and several other associated bifurcations. Such analysis is corroborated by numerical simulations of the associated Kuramoto system, which, in turn, unveils a variety of collective behaviors such as synchronized motion, oscillation death, chimeras, incoherent states, and traveling waves. Importantly, this system displays a case where alternating chimeras emerge in an indistinguishable single population of oscillators, which may offer insights into the unihemispheric slow-wave sleep phenomenon observed in mammals and birds.

Dynamical properties and mechanisms of metastability: A perspective in neuroscience

Metastability, characterized by a variability of regimes in time, is a ubiquitous type of neural dynamics. It has been formulated in many different ways in the neuroscience literature, however, which may cause some confusion. In this Perspective, we discuss metastability from the point of view of dynamical systems theory. We extract from the literature a very simple but general definition through the concept of metastable regimes as long-lived but transient epochs of activity with unique dynamical properties. This definition serves as an umbrella term that encompasses formulations from other works, and readily connects to concepts from dynamical systems theory. This allows us to examine general dynamical properties of metastable regimes, propose in a didactic manner several dynamics-based mechanisms that generate them, and discuss a theoretical tool to characterize them quantitatively. This Perspective leads to insights that help to address issues debated in the literature and also suggests pathways for future research.

Touch-evoked traveling waves establish a translaminar spacetime code

Linking sensory-evoked traveling waves to underlying circuit patterns is critical to understanding the neural basis of sensory perception. To form this link, we performed simultaneous electrophysiology and two-photon calcium imaging through transparent NeuroGrids and mapped touch-evoked traveling waves and underlying microcircuit dynamics. In awake mice, both passive and active whisker touch elicited traveling waves within and across barrels, with a fast early component followed by a late wave that lasted hundreds of milliseconds poststimulus. Notably, late waves were modulated by perceived value and predicted behavioral choice in a two-whisker discrimination task. We found that the late wave feature was (i) modulated by motor feedback, (ii) differentially engaged a sparse ensemble reactivation pattern across layer 2/3, which a balanced-state network model reconciled via feedback-induced inhibitory stabilization, and (iii) aligned to regenerative layer 5 apical dendritic Ca2+ events. Our results reveal that translaminar spacetime patterns organized by cortical feedback support sparse touch-evoked traveling waves.

Hemodynamic cortical ripples through cyclicity analysis

A fine-grained understanding of dynamics in cortical networks is crucial to unpacking brain function. Resting-state functional magnetic resonance imaging (fMRI) gives rise to time series recordings of the activity of different brain regions, which are aperiodic and lack a base frequency. Cyclicity analysis, a novel technique robust under time reparametrizations, is effective in recovering the temporal ordering of such time series, collectively considered components of a multidimensional trajectory. Here, we extend this analytical method for characterizing the dynamic interaction between distant brain regions and apply it to the data from the Human Connectome Project. Our analysis detected cortical traveling waves of activity propagating along a spatial axis, resembling cortical hierarchical organization with consistent lead-lag relationships between specific brain regions in resting-state scans. In fMRI scans involving tasks, we observed short bursts of task-modulated strong temporal ordering that dominate overall lead-lag relationships between pairs of regions in the brain that align temporally with stimuli from the tasks. Our results suggest a possible role played by waves of excitation sweeping through brain regions that underlie emergent cognitive functions.

Transformers and cortical waves: encoders for pulling in context across time

The capabilities of transformer networks such as ChatGPT and other large language models (LLMs) have captured the world's attention. The crucial computational mechanism underlying their performance relies on transforming a complete input sequence - for example, all the words in a sentence - into a long 'encoding vector' that allows transformers to learn long-range temporal dependencies in naturalistic sequences. Specifically, 'self-attention' applied to this encoding vector enhances temporal context in transformers by computing associations between pairs of words in the input sequence. We suggest that waves of neural activity traveling across single cortical areas, or multiple regions on the whole-brain scale, could implement a similar encoding principle. By encapsulating recent input history into a single spatial pattern at each moment in time, cortical waves may enable a temporal context to be extracted from sequences of sensory inputs, the same computational principle as that used in transformers.

Does slow oscillation-spindle coupling contribute to sleep-dependent memory consolidation? A Bayesian meta-analysis

The active system consolidation theory suggests that information transfer between the hippocampus and cortex during sleep underlies memory consolidation. Neural oscillations during sleep, including the temporal coupling between slow oscillations (SO) and sleep spindles (SP), may play a mechanistic role in memory consolidation. However, differences in analytical approaches and the presence of physiological and behavioral moderators have led to inconsistent conclusions. This meta-analysis, comprising 23 studies and 297 effect sizes, focused on four standard phase-amplitude coupling measures including coupling phase, strength, percentage, and SP amplitude, and their relationship with memory retention. We developed a standardized approach to incorporate non-normal circular-linear correlations. We found strong evidence supporting that precise and strong SO-fast SP coupling in the frontal lobe predicts memory consolidation. The strength of this association is mediated by memory type, aging, and dynamic spatio-temporal features, including SP frequency and cortical topography. In conclusion, SO-SP coupling should be considered as a general physiological mechanism for memory consolidation.

Macroscale traveling waves evoked by single-pulse stimulation of the human brain

Understanding the spatiotemporal dynamics of neural signal propagation is fundamental to unraveling the complexities of brain function. Emerging evidence suggests that cortico-cortical evoked potentials (CCEPs) resulting from single-pulse electrical stimulation may be used to characterize the patterns of information flow between and within brain networks. At present, the basic spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that single-pulse electrical stimulation evokes neural traveling waves detectable in the three-dimensional space sampled by intracranial stereoelectroencephalography. Across a cohort of 21 adult patients with intractable epilepsy, we delivered 17,631 stimulation pulses and recorded CCEP responses in 1,019 electrode contacts. The distance between each pair of electrode contacts was approximated using three different metrics (Euclidean distance, path length, and geodesic distance), representing direct, tractographic, and transcortical propagation, respectively. For each robust CCEP, we extracted amplitude-, spectral-, and phase-based features to identify traveling waves emanating from the site of stimulation. Many evoked responses to stimulation appear to propagate as traveling waves (~14-28%), despite sparse sampling throughout the brain. These stimulation-evoked traveling waves exhibited biologically plausible propagation velocities (range 0.1-9.6 m/s). Our results reveal that direct electrical stimulation elicits neural activity with variable spatiotemporal dynamics, including the initiation of neural traveling waves.

Brain states as wave-like motifs

There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.

The direction of theta and alpha travelling waves modulates human memory processing

To support a range of behaviours, the brain must flexibly coordinate neural activity across widespread brain regions. One potential mechanism for this coordination is a travelling wave, in which a neural oscillation propagates across the brain while organizing the order and timing of activity across regions. Although travelling waves are present across the brain in various species, their potential functional relevance has remained unknown. Here, using rare direct human brain recordings, we demonstrate a distinct functional role for travelling waves of theta- and alpha-band (2-13 Hz) oscillations in the cortex. Travelling waves propagate in different directions during separate cognitive processes. In episodic memory, travelling waves tended to propagate in a posterior-to-anterior direction during successful memory encoding and in an anterior-to-posterior direction during recall. Because travelling waves of oscillations correspond to local neuronal spiking, these patterns indicate that rhythmic pulses of activity move across the brain in different directions for separate behaviours. More broadly, our results suggest a fundamental role for travelling waves and oscillations in dynamically coordinating neural connectivity, by flexibly organizing the timing and directionality of network interactions across the cortex to support cognition and behaviour.

Human connectome topology directs cortical traveling waves and shapes frequency gradients

Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While the direction of traveling waves has been linked to brain function and dysfunction, the factors that determine this direction remain elusive. We hypothesized that structural connectivity instrength gradients - defined as the gradually varying sum of incoming connection strengths across the cortex - could shape both traveling wave direction and frequency gradients. We confirm the presence of instrength gradients in the human connectome across diverse cohorts and parcellations. Using a cortical network model, we demonstrate how these instrength gradients direct traveling waves and shape frequency gradients. Our model fits resting-state MEG functional connectivity best in a regime where instrength-directed traveling waves and frequency gradients emerge. We further show how structural subnetworks of the human connectome generate opposing wave directions and frequency gradients observed in the alpha and beta bands. Our findings suggest that structural connectivity instrength gradients affect both traveling wave direction and frequency gradients.

An electroencephalogram microdisplay to visualize neuronal activity on the brain surface

Functional mapping during brain surgery is applied to define brain areas that control critical functions and cannot be removed. Currently, these procedures rely on verbal interactions between the neurosurgeon and electrophysiologist, which can be time-consuming. In addition, the electrode grids that are used to measure brain activity and to identify the boundaries of pathological versus functional brain regions have low resolution and limited conformity to the brain surface. Here, we present the development of an intracranial electroencephalogram (iEEG)-microdisplay that consists of freestanding arrays of 2048 GaN light-emitting diodes laminated on the back of micro-electrocorticography electrode grids. With a series of proof-of-concept experiments in rats and pigs, we demonstrate that these iEEG-microdisplays allowed us to perform real-time iEEG recordings and display cortical activities by spatially corresponding light patterns on the surface of the brain in the surgical field. Furthermore, iEEG-microdisplays allowed us to identify and display cortical landmarks and pathological activities from rat and pig models. Using a dual-color iEEG-microdisplay, we demonstrated coregistration of the functional cortical boundaries with one color and displayed the evolution of electrical potentials associated with epileptiform activity with another color. The iEEG-microdisplay holds promise to facilitate monitoring of pathological brain activity in clinical settings.

Neural assemblies coordinated by cortical waves are associated with waking and hallucinatory brain states

The relationship between sensory stimuli and perceptions is brain-state dependent: in wakefulness, suprathreshold stimuli evoke perceptions; under anesthesia, perceptions are abolished; and during dreaming and in dissociated states, percepts are internally generated. Here, we exploit this state dependence to identify brain activity associated with internally generated or stimulus-evoked perceptions. In awake mice, visual stimuli phase reset spontaneous cortical waves to elicit 3-6 Hz feedback traveling waves. These stimulus-evoked waves traverse the cortex and entrain visual and parietal neurons. Under anesthesia as well as during ketamine-induced dissociation, visual stimuli do not disrupt spontaneous waves. Uniquely, in the dissociated state, spontaneous waves traverse the cortex caudally and entrain visual and parietal neurons, akin to stimulus-evoked waves in wakefulness. Thus, coordinated neuronal assemblies orchestrated by traveling cortical waves emerge in states in which perception can manifest. The awake state is privileged in that this coordination is reliably elicited by external visual stimuli.

Sequentially activated discrete modules appear as traveling waves in neuronal measurements with limited spatiotemporal sampling

Numerous studies have identified traveling waves in the cortex and suggested they play important roles in brain processing. These waves are most often measured using macroscopic methods that are unable to assess the local spiking activity underlying wave dynamics. Here, we investigated the possibility that waves may not be traveling at the single neuron scale. We first show that sequentially activating two discrete brain areas can appear as traveling waves in EEG simulations. We next reproduce these results using an analytical model of two sequentially activated regions. Using this model, we were able to generate wave-like activity with variable directions, velocities, and spatial patterns, and to map the discriminability limits between traveling waves and modular sequential activations. Finally, we investigated the link between field potentials and single neuron excitability using large-scale measurements from turtle cortex ex vivo. We found that while field potentials exhibit wave-like dynamics, the underlying spiking activity was better described by consecutively activated spatially adjacent groups of neurons. Taken together, this study suggests caution when interpreting phase delay measurements as continuously propagating wavefronts in two different spatial scales. A careful distinction between modular and wave excitability profiles across scales will be critical for understanding the nature of cortical computations.

Intraoperative application and early experience with novel high-resolution, high-channel-count thin-film electrodes for human microelectrocorticography

OBJECTIVE

The study objective was to evaluate intraoperative experience with newly developed high-spatial-resolution microelectrode grids composed of poly(3,4-ethylenedioxythiophene) with polystyrene sulfonate (PEDOT:PSS), and those composed of platinum nanorods (PtNRs).

METHODS

A cohort of patients who underwent craniotomy for pathological tissue resection and who had high-spatial-resolution microelectrode grids placed intraoperatively were evaluated. Patient demographic and baseline clinical variables as well as relevant microelectrode grid characteristic data were collected. The primary and secondary outcome measures of interest were successful microelectrode grid utilization with usable resting-state or task-related data, and grid-related adverse intraoperative events and/or grid dysfunction.

RESULTS

Included in the analysis were 89 cases of patients who underwent a craniotomy for resection of neoplasms (n = 58) or epileptogenic tissue (n = 31). These cases accounted for 94 grids: 58 PEDOT:PSS and 36 PtNR grids. Of these 94 grids, 86 were functional and used successfully to obtain cortical recordings from 82 patients. The mean cortical grid recording duration was 15.3 ± 1.15 minutes. Most recordings in patients were obtained during experimental tasks (n = 52, 58.4%), involving language and sensorimotor testing paradigms, or were obtained passively during resting state (n = 32, 36.0%). There were no intraoperative adverse events related to grid placement. However, there were instances of PtNR grid dysfunction (n = 8) related to damage incurred by suboptimal preoperative sterilization (n = 7) and improper handling (n = 1); intraoperative recordings were not performed. Vaporized peroxide sterilization was the most optimal sterilization method for PtNR grids, providing a significantly greater number of usable channels poststerilization than did steam-based sterilization techniques (median 905.0 [IQR 650.8-935.5] vs 356.0 [IQR 18.0-597.8], p = 0.0031).

CONCLUSIONS

High-spatial-resolution microelectrode grids can be readily incorporated into appropriately selected craniotomy cases for clinical and research purposes. Grids are reliable when preoperative handling and sterilization considerations are accounted for. Future investigations should compare the diagnostic utility of these high-resolution grids to commercially available counterparts and assess whether diagnostic discrepancies relate to clinical outcomes.

Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep

The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.

Does the brain behave like a (complex) network? I. Dynamics

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.

A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Neuroscience is moving toward a more integrative discipline where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow-wave activity (<1 Hz), which occurs during unconscious brain states like sleep and general anesthesia and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow-wave characteristics across multiple, openly available electrocorticography (ECoG) and calcium imaging datasets.

Identifying sources of human interictal discharges with travelling wave and white matter propagation

Interictal epileptiform discharges have been shown to propagate from focal epileptogenic sources as travelling waves or through more rapid white matter conduction. We hypothesize that both modes of propagation are necessary to explain interictal discharge timing delays. We propose a method that, for the first time, incorporates both propagation modes to identify unique potential sources of interictal activity. We retrospectively analysed 38 focal epilepsy patients who underwent intracranial EEG recordings and diffusion-weighted imaging for epilepsy surgery evaluation. Interictal discharges were detected and localized to the most likely source based on relative delays in time of arrival across electrodes, incorporating travelling waves and white matter propagation. We assessed the influence of white matter propagation on distance of spread, timing and clinical interpretation of interictal activity. To evaluate accuracy, we compared our source localization results to earliest spiking regions to predict seizure outcomes. White matter propagation helps to explain the timing delays observed in interictal discharge sequences, underlying rapid and distant propagation. Sources identified based on differences in time of receipt of interictal discharges are often distinct from the leading electrode location. Receipt of activity propagating rapidly via white matter can occur earlier than more local activity propagating via slower cortical travelling waves. In our cohort, our source localization approach was more accurate in predicting seizure outcomes than the leading electrode location. Inclusion of white matter in addition to travelling wave propagation in our model of discharge spread did not improve overall accuracy but allowed for identification of unique and at times distant potential sources of activity, particularly in patients with persistent postoperative seizures. Since distant white matter propagation can occur more rapidly than local travelling wave propagation, combined modes of propagation within an interictal discharge sequence can decouple the commonly assumed relationship between spike timing and distance from the source. Our findings thus highlight the clinical importance of recognizing the presence of dual modes of propagation during interictal discharges, as this may be a cause of clinical mislocalization.

How far neuroscience is from understanding brains

The cellular biology of brains is relatively well-understood, but neuroscientists have not yet generated a theory explaining how brains work. Explanations of how neurons collectively operate to produce what brains can do are tentative and incomplete. Without prior assumptions about the brain mechanisms, I attempt here to identify major obstacles to progress in neuroscientific understanding of brains and central nervous systems. Most of the obstacles to our understanding are conceptual. Neuroscience lacks concepts and models rooted in experimental results explaining how neurons interact at all scales. The cerebral cortex is thought to control awake activities, which contrasts with recent experimental results. There is ambiguity distinguishing task-related brain activities from spontaneous activities and organized intrinsic activities. Brains are regarded as driven by external and internal stimuli in contrast to their considerable autonomy. Experimental results are explained by sensory inputs, behavior, and psychological concepts. Time and space are regarded as mutually independent variables for spiking, post-synaptic events, and other measured variables, in contrast to experimental results. Dynamical systems theory and models describing evolution of variables with time as the independent variable are insufficient to account for central nervous system activities. Spatial dynamics may be a practical solution. The general hypothesis that measurements of changes in fundamental brain variables, action potentials, transmitter releases, post-synaptic transmembrane currents, etc., propagating in central nervous systems reveal how they work, carries no additional assumptions. Combinations of current techniques could reveal many aspects of spatial dynamics of spiking, post-synaptic processing, and plasticity in insects and rodents to start with. But problems defining baseline and reference conditions hinder interpretations of the results. Furthermore, the facts that pooling and averaging of data destroy their underlying dynamics imply that single-trial designs and statistics are necessary.

Beta traveling waves in monkey frontal and parietal areas encode recent reward history

Brain function depends on neural communication, but the mechanisms of this communication are not well understood. Recent studies suggest that one form of neural communication is through traveling waves (TWs)-patterns of neural oscillations that propagate within and between brain areas. We show that TWs are robust in microarray recordings in frontal and parietal cortex and encode recent reward history. Two adult male monkeys made saccades to obtain probabilistic rewards and were sensitive to the (statistically irrelevant) reward on the previous trial. TWs in frontal and parietal areas were stronger in trials that followed a prior reward versus a lack of reward and, in the frontal lobe, correlated with the monkeys' behavioral sensitivity to the prior reward. The findings suggest that neural communication mediated by TWs within the frontal and parietal lobes contribute to maintaining information about recent reward history and mediating the impact of this history on the monkeys' expectations.

Sleep and vigilance states: Embracing spatiotemporal dynamics

The classic view of sleep and vigilance states is a global stationary perspective driven by the interaction between neuromodulators and thalamocortical systems. However, recent data are challenging this view by demonstrating that vigilance states are highly dynamic and regionally complex. Spatially, sleep- and wake-like states often co-occur across distinct brain regions, as in unihemispheric sleep, local sleep in wakefulness, and during development. Temporally, dynamic switching prevails around state transitions, during extended wakefulness, and in fragmented sleep. This knowledge, together with methods monitoring brain activity across multiple regions simultaneously at millisecond resolution with cell-type specificity, is rapidly shifting how we consider vigilance states. A new perspective incorporating multiple spatial and temporal scales may have important implications for considering the governing neuromodulatory mechanisms, the functional roles of vigilance states, and their behavioral manifestations. A modular and dynamic view highlights novel avenues for finer spatiotemporal interventions to improve sleep function.

Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing

The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.

A CMOS-based highly scalable flexible neural electrode interface

Perception, thoughts, and actions are encoded by the coordinated activity of large neuronal populations spread over large areas. However, existing electrophysiological devices are limited by their scalability in capturing this cortex-wide activity. Here, we developed an electrode connector based on an ultra-conformable thin-film electrode array that self-assembles onto silicon microelectrode arrays enabling multithousand channel counts at a millimeter scale. The interconnects are formed using microfabricated electrode pads suspended by thin support arms, termed Flex2Chip. Capillary-assisted assembly drives the pads to deform toward the chip surface, and van der Waals forces maintain this deformation, establishing Ohmic contact. Flex2Chip arrays successfully measured extracellular action potentials ex vivo and resolved micrometer scale seizure propagation trajectories in epileptic mice. We find that seizure dynamics in absence epilepsy in the Scn8a+/- model do not have constant propagation trajectories.

Cortical activity emerges in region-specific patterns during early brain development

The development of precise neural circuits in the brain requires spontaneous patterns of neural activity prior to functional maturation. In the rodent cerebral cortex, patchwork and wave patterns of activity develop in somatosensory and visual regions, respectively, and are present at birth. However, whether such activity patterns occur in noneutherian mammals, as well as when and how they arise during development, remain open questions relevant for understanding brain formation in health and disease. Since the onset of patterned cortical activity is challenging to study prenatally in eutherians, here we offer an approach in a minimally invasive manner using marsupial dunnarts, whose cortex forms postnatally. We discovered similar patchwork and travelling waves in the dunnart somatosensory and visual cortices at stage 27 (equivalent to newborn mice) and examined earlier stages of development to determine the onset of these patterns and how they first emerge. We observed that these patterns of activity emerge in a region-specific and sequential manner, becoming evident as early as stage 24 in somatosensory and stage 25 in visual cortices (equivalent to embryonic day 16 and 17, respectively, in mice), as cortical layers establish and thalamic axons innervate the cortex. In addition to sculpting synaptic connections of existing circuits, evolutionarily conserved patterns of neural activity could therefore help regulate other early events in cortical development.

Cytoelectric Coupling: Electric fields sculpt neural activity and "tune" the brain's infrastructure

We propose and present converging evidence for the Cytoelectric Coupling Hypothesis: Electric fields generated by neurons are causal down to the level of the cytoskeleton. This could be achieved via electrodiffusion and mechanotransduction and exchanges between electrical, potential and chemical energy. Ephaptic coupling organizes neural activity, forming neural ensembles at the macroscale level. This information propagates to the neuron level, affecting spiking, and down to molecular level to stabilize the cytoskeleton, "tuning" it to process information more efficiently.

Characterization of spatiotemporal dynamics in EEG data during picture naming with optical flow patterns

In this study, we investigate the spatiotemporal dynamics of the neural oscillations by analyzing the electric potential that arises from neural activity. We identify two types of dynamics based on the frequency and phase of oscillations: standing waves or as out-of-phase and modulated waves, which represent a combination of standing and moving waves. To characterize these dynamics, we use optical flow patterns such as sources, sinks, spirals and saddles. We compare analytical and numerical solutions with real EEG data acquired during a picture-naming task. Analytical approximation of standing waves helps us to establish some properties of pattern location and number. Specifically, sources and sinks are mainly located in the same location, while saddles are positioned between them. The number of saddles correlates with the sum of all the other patterns. These properties are confirmed in both the simulated and real EEG data. In particular, source and sink clusters in the EEG data overlap with each other with median percentages around 60%, and hence have high spatial correlation, while source/sink clusters overlap with saddle clusters in less than 1%, and have different locations. Our statistical analysis showed that saddles account for about 45% of all patterns, while the remaining patterns are present in similar proportions.

Spike-phase coupling patterns reveal laminar identity in primate cortex

The cortical column is one of the fundamental computational circuits in the brain. In order to understand the role neurons in different layers of this circuit play in cortical function it is necessary to identify the boundaries that separate the laminar compartments. While histological approaches can reveal ground truth they are not a practical means of identifying cortical layers in vivo. The gold standard for identifying laminar compartments in electrophysiological recordings is current-source density (CSD) analysis. However, laminar CSD analysis requires averaging across reliably evoked responses that target the input layer in cortex, which may be difficult to generate in less well-studied cortical regions. Further, the analysis can be susceptible to noise on individual channels resulting in errors in assigning laminar boundaries. Here, we have analyzed linear array recordings in multiple cortical areas in both the common marmoset and the rhesus macaque. We describe a pattern of laminar spike-field phase relationships that reliably identifies the transition between input and deep layers in cortical recordings from multiple cortical areas in two different non-human primate species. This measure corresponds well to estimates of the location of the input layer using CSDs, but does not require averaging or specific evoked activity. Laminar identity can be estimated rapidly with as little as a minute of ongoing data and is invariant to many experimental parameters. This method may serve to validate CSD measurements that might otherwise be unreliable or to estimate laminar boundaries when other methods are not practical.

Beta oscillations and waves in motor cortex can be accounted for by the interplay of spatially structured connectivity and fluctuating inputs

The beta rhythm (13-30 Hz) is a prominent brain rhythm. Recordings in primates during instructed-delay reaching tasks have shown that different types of traveling waves of oscillatory activity are associated with episodes of beta oscillations in motor cortex during movement preparation. We propose here a simple model of motor cortex based on local excitatory-inhibitory neuronal populations coupled by long-range excitation, where additionally inputs to the motor cortex from other neural structures are represented by stochastic inputs on the different model populations. We show that the model accurately reproduces the statistics of recording data when these external inputs are correlated on a short time scale (25 ms) and have two different components, one that targets the motor cortex locally and another one that targets it in a global and synchronized way. The model reproduces the distribution of beta burst durations, the proportion of the different observed wave types, and wave speeds, which we show not to be linked to axonal propagation speed. When the long-range connectivity or the local input targets are anisotropic, traveling waves are found to preferentially propagate along the axis where connectivity decays the fastest. Different from previously proposed mechanistic explanations, the model suggests that traveling waves in motor cortex are the reflection of the dephasing by external inputs, putatively of thalamic origin, of an oscillatory activity that would otherwise be spatially synchronized by recurrent connectivity.

Spatiotemporal patterns of sleep spindle activity in human anterior thalamus and cortex

Sleep spindles (8 - 16 Hz) are transient electrophysiological events during non-rapid eye movement sleep. While sleep spindles are routinely observed in the cortex using scalp electroencephalography (EEG), recordings of their thalamic counterparts have not been widely studied in humans. Based on a few existing studies, it has been hypothesized that spindles occur as largely local phenomena. We investigated intra-thalamic and thalamocortical spindle co-occurrence, which may underlie thalamocortical communication. We obtained scalp EEG and thalamic recordings from 7 patients that received bilateral deep brain stimulation (DBS) electrodes to the anterior thalamus for the treatment of drug resistant focal epilepsy. Spindles were categorized into subtypes based on their main frequency (i.e., slow (10±2 Hz) or fast (14±2 Hz)) and their level of thalamic involvement (spanning one channel, or spreading uni- or bilaterally within the thalamus). For the first time, we contrasted observed spindle patterns with permuted data to estimate random spindle co-occurrence. We found that multichannel spindle patterns were systematically coordinated at the thalamic and thalamocortical level. Importantly, distinct topographical patterns of thalamocortical spindle overlap were associated with slow and fast subtypes of spindles. These observations provide further evidence for coordinated spindle activity in thalamocortical networks.

Constructing 2D maps of human spinal cord activity and isolating the functional midline with high-density microelectrode arrays

Intraoperative neuromonitoring (IONM) is a widely used practice in spine surgery for early detection and minimization of neurological injury. IONM is most commonly conducted by indirectly recording motor and somatosensory evoked potentials from either muscles or the scalp, which requires large-amplitude electrical stimulation and provides limited spatiotemporal information. IONM may inform of inadvertent events during neurosurgery after they occur, but it does not guide safe surgical procedures when the anatomy of the diseased spinal cord is distorted. To overcome these limitations and to increase our understanding of human spinal cord neurophysiology, we applied a microelectrode array with hundreds of channels to the exposed spinal cord during surgery and resolved spatiotemporal dynamics with high definition. We used this method to construct two-dimensional maps of responsive channels and define with submillimeter precision the electrophysiological midline of the spinal cord. The high sensitivity of our microelectrode array allowed us to record both epidural and subdural responses at stimulation currents that are well below those used clinically and to resolve postoperative evoked potentials when IONM could not. Together, these advances highlight the potential of our microelectrode arrays to capture previously unexplored spinal cord neural activity and its spatiotemporal dynamics at high resolution, offering better electrophysiological markers that can transform IONM.

Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest

In the absence of external stimuli, neural activity continuously evolves from one configuration to another. Whether these transitions or explorations follow some underlying arrangement or lack a predictable ordered plan remains to be determined. Here, using fMRI data from highly sampled individuals (~5 hours of resting-state data per individual), we aimed to reveal the rules that govern transitions in brain activity at rest. Our Topological Data Analysis based Mapper approach characterized a highly visited transition state of the brain that acts as a switch between different neural configurations to organize the spontaneous brain activity. Further, while the transition state was characterized by a uniform representation of canonical resting-state networks (RSNs), the periphery of the landscape was dominated by a subject-specific combination of RSNs. Altogether, we revealed rules or principles that organize spontaneous brain activity using a precision dynamics approach.

The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold

Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.

Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate electrocorticography (ECoG), human electroencephalogram (EEG), and clinical intracranial electroencephalogram (iEEG) recordings in the human. Within each recording type, we find widespread spindles occur much more frequently than previously reported. We then analyzed the spatiotemporal patterns of these large-scale, multi-area spindles and, in the EEG recordings, how spindle patterns change following a visual memory task. Our results reveal a potential role for widespread, multi-area spindles in consolidation of memories in networks widely distributed across primate cortex.

The brain processes memories as we sleep, generating rhythms of electrical activity called ‘sleep spindles’. Sleep spindles were long thought to be a state where the entire brain was fully synchronized by this rhythm. This was based on EEG recordings, short for electroencephalogram, a technique that uses electrodes on the scalp to measure electrical activity in the outermost layer of the brain, the cortex. But more recent intracranial recordings of people undergoing brain surgery have challenged this idea and suggested that sleep spindles may not be a state of global brain synchronization, but rather localised to specific areas.

Mofrad et al. sought to clarify the extent to which spindles co-occur at multiple sites in the brain, which could shed light on how networks of neurons coordinate memory storage during sleep. To analyse highly variable brain wave recordings, Mofrad et al. adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves. The resulting algorithm, designed to more sensitively detect spindles amongst other brain activity, was then applied to a range of sleep recordings from humans and macaque monkeys.

The analyses revealed that widespread and complex patterns of spindle rhythms, spanning multiple areas in the cortex of the brain, actually appear much more frequently than previously thought. This finding was consistent across all the recordings analysed, even recordings under the skull, which provide the clearest window into brain circuits. Further analyses found that these multi-area spindles occurred more often in sleep after people had completed tasks that required holding many visual scenes in memory, as opposed to control conditions with fewer visual scenes.

In summary, Mofrad et al. show that neuroscientists had previously not appreciated the complex and dynamic patterns in this sleep rhythm. These patterns in sleep spindles may be able to adapt based on the demands needed for memory storage, and this will be the subject of future work. Moreover, the findings support the idea that sleep spindles help coordinate the consolidation of memories in brain circuits that stretch across the cortex. Understanding this mechanism may provide insights into how memory falters in aging and sleep-related diseases, such as Alzheimer’s disease. Lastly, the algorithm developed by Mofrad et al. stands to be a useful tool for analysing other rhythmic waveforms in noisy recordings.

Spontaneous Spiking Is Governed by Broadband Fluctuations

Populations of cortical neurons generate rhythmic fluctuations in their ongoing spontaneous activity. These fluctuations can be seen in the local field potential (LFP), which reflects summed return currents from synaptic activity in the local population near a recording electrode. The LFP is spectrally broad, and many researchers view this breadth as containing many narrowband oscillatory components that may have distinct functional roles. This view is supported by the observation that the phase of narrowband oscillations is often correlated with cortical excitability and can relate to the timing of spiking activity and the fidelity of sensory evoked responses. Accordingly, researchers commonly tune in to these channels by narrowband filtering the LFP. Alternatively, neural activity may be fundamentally broadband and composed of transient, nonstationary rhythms that are difficult to approximate as oscillations. In this view, the instantaneous state of the broad ensemble relates directly to the excitability of the local population with no particular allegiance to any frequency band. To test between these alternatives, we asked whether the spiking activity of neocortical neurons in marmoset of either sex is better aligned with the phase of the LFP within narrow frequency bands or with a broadband measure. We find that the phase of broadband LFP fluctuations provides a better predictor of spike timing than the phase after filtering in narrow bands. These results challenge the view of the neocortex as a system composed of narrowband oscillators and supports a view in which neural activity fluctuations are intrinsically broadband.SIGNIFICANCE STATEMENT Research into the dynamical state of neural populations often attributes unique significance to the state of narrowband oscillatory components. However, rhythmic fluctuations in cortical activity are nonstationary and broad spectrum. We find that the timing of spontaneous spiking activity is better captured by the state of broadband fluctuations over any latent oscillatory component. These results suggest narrowband interpretations of rhythmic population activity may be limited, and broader representations may provide higher fidelity in describing moment-to-moment fluctuations in cortical activity.

Emerging principles of spacetime in brains: Meeting report on spatial neurodynamics

How do neurons and networks of neurons interact spatially? Here, we overview recent discoveries revealing how spatial dynamics of spiking and postsynaptic activity efficiently expose and explain fundamental brain and brainstem mechanisms behind detection, perception, learning, and behavior.

Human Spindle Variability

In humans, sleep spindles are 10- to 16-Hz oscillations lasting approximately 0.5-2 s. Spindles, along with cortical slow oscillations, may facilitate memory consolidation by enabling synaptic plasticity. Early recordings of spindles at the scalp found anterior channels had overall slower frequency than central-posterior channels. This robust, topographical finding led to dichotomizing spindles as "slow" versus "fast," modeled as two distinct spindle generators in frontal versus posterior cortex. Using a large dataset of intracranial stereoelectroencephalographic (sEEG) recordings from 20 patients (13 female, 7 male) and 365 bipolar recordings, we show that the difference in spindle frequency between frontal and parietal channels is comparable to the variability in spindle frequency within the course of individual spindles, across different spindles recorded by a given site, and across sites within a given region. Thus, fast and slow spindles only capture average differences that obscure a much larger underlying overlap in frequency. Furthermore, differences in mean frequency are only one of several ways that spindles differ. For example, compared with parietal, frontal spindles are smaller, tend to occur after parietal when both are engaged, and show a larger decrease in frequency within-spindles. However, frontal and parietal spindles are similar in being longer, less variable, and more widespread than occipital, temporal, and Rolandic spindles. These characteristics are accentuated in spindles which are highly phase-locked to posterior hippocampal spindles. We propose that rather than a strict parietal-fast/frontal-slow dichotomy, spindles differ continuously and quasi-independently in multiple dimensions, with variability due about equally to within-spindle, within-region, and between-region factors.SIGNIFICANCE STATEMENT Sleep spindles are 10- to 16-Hz neural oscillations generated by cortico-thalamic circuits that promote memory consolidation. Spindles are often dichotomized into slow-anterior and fast-posterior categories for cognitive and clinical studies. Here, we show that the anterior-posterior difference in spindle frequency is comparable to that observed between different cycles of individual spindles, between spindles from a given site, or from different sites within a region. Further, we show that spindles vary on other dimensions such as duration, amplitude, spread, primacy and consistency, and that these multiple dimensions vary continuously and largely independently across cortical regions. These findings suggest that multiple continuous variables rather than a strict frequency dichotomy may be more useful biomarkers for memory consolidation or psychiatric disorders.

Spontaneous neuronal oscillations in the human insula are hierarchically organized traveling waves

The insula plays a fundamental role in a wide range of adaptive human behaviors, but its electrophysiological dynamics are poorly understood. Here, we used human intracranial electroencephalographic recordings to investigate the electrophysiological properties and hierarchical organization of spontaneous neuronal oscillations within the insula. We analyzed the neuronal oscillations of the insula directly and found that rhythms in the theta and beta frequency oscillations are widespread and spontaneously present. These oscillations are largely organized along the anterior–posterior (AP) axis of the insula. Both the left and right insula showed anterior-­to-posterior decreasing gradients for the power of oscillations in the beta frequency band. The left insula also showed a posterior-to-anterior decreasing frequency gradient and an anterior-to-posterior decreasing power gradient in the theta frequency band. In addition to measuring the power of these oscillations, we also examined the phase of these signals across simultaneous recording channels and found that the insula oscillations in the theta and beta bands are traveling waves. The strength of the traveling waves in each frequency was positively correlated with the amplitude of each oscillation. However, the theta and beta traveling waves were uncoupled to each other in terms of phase and amplitude, which suggested that insular traveling waves in the theta and beta bands operate independently. Our findings provide new insights into the spatiotemporal dynamics and hierarchical organization of neuronal oscillations within the insula, which, given its rich connectivity with widespread cortical regions, indicates that oscillations and traveling waves have an important role in intrainsular and interinsular communications.

Nine-day continuous recording of EEG and 2-hour of high-density EEG under chronic sleep restriction in mice

This work provides an EEG dataset collected from nine mice during the sleep deprivation (SD) paradigm for the sleep science community. It includes 9-day of continuous recording of the frontal and parietal EEG, accelerometer, and 2-hour of high-density EEG (HD-EEG) under SD and SD-free conditions. Eighteen hours of SD were conducted on 5 consecutive days. The HD-EEG data were saved in the EEGLAB format and stored as the brain imaging data structure (BIDS). These datasets can be used to (i) compare mouse HD-EEG to human HD-EEG, (ii) track oscillatory activities of the sleep EEG (e.g., slow waves, spindles) across the cortical regions under different conditions of sleep pressure, and (iii) investigate the cortical traveling waves in the mouse brain. We also provided Python code for basic analyses of this dataset, including the detection of slow waves and sleep spindles. We hope that our dataset will reveal hidden activities during sleep and lead to a better understanding of the functions and mechanisms of sleep.

Brain-wide interactions during hippocampal sharp wave ripples

During periods of disengagement from the environment, transient population bursts, known as sharp wave ripples (SPW-Rs), occur sporadically. While numerous experiments have characterized the bidirectional relationship between SPW-Rs and activity in chosen brain areas, the topographic relationship between different segments of the hippocampus and brain-wide target areas has not been studied at high temporal and spatial resolution. Yet, such knowledge is necessary to infer the direction of communication. We analyzed two publicly available datasets with simultaneous high-density silicon probe recordings from across the mouse forebrain. We found that SPW-Rs coincide with a transient brain-wide increase in functional connectivity. In addition, we show that the diversity in SPW-R features, such as their incidence, magnitude, and intrahippocampal topography in the septotemporal axis, are correlated with slower excitability fluctuations in cortical and subcortical areas. Further, variations in SPW-R features correlated with the timing, sign, and magnitude of downstream responses with large-amplitude SPW-Rs followed by transient silence in extrahippocampal structures. Our findings expand on previous results and demonstrate that the activity patterns in extrahippocampal structures depend both on the intrahippocampal topographic origin and magnitude of hippocampal SPW-Rs.

Event-Related Phase Synchronization Propagates Rapidly across Human Ventral Visual Cortex

Visual inputs to early visual cortex integrate with semantic, linguistic and memory inputs in higher visual cortex, in a manner that is rapid and accurate, and enables complex computations such as face recognition and word reading. This implies the existence of fundamental organizational principles that enable such efficiency. To elaborate on this, we performed intracranial recordings in 82 individuals while they performed tasks of varying visual and cognitive complexity. We discovered that visual inputs induce highly organized posterior-to-anterior propagating patterns of phase modulation across the ventral occipitotemporal cortex. At individual electrodes there was a stereotyped temporal pattern of phase progression following both stimulus onset and offset, consistent across trials and tasks. The phase of low frequency activity in anterior regions was predicted by the prior phase in posterior cortical regions. This spatiotemporal propagation of phase likely serves as a feed-forward organizational influence enabling the integration of information across the ventral visual stream. This phase modulation manifests as the early components of the event related potential; one of the most commonly used measures in human electrophysiology. These findings illuminate fundamental organizational principles of the higher order visual system that enable the rapid recognition and characterization of a variety of inputs.

Propofol Anesthesia Alters Cortical Traveling Waves

Oscillatory dynamics in cortex seem to organize into traveling waves that serve a variety of functions. Recent studies show that propofol, a widely used anesthetic, dramatically alters cortical oscillations by increasing slow-delta oscillatory power and coherence. It is not known how this affects traveling waves. We compared traveling waves across the cortex of non-human primates before, during, and after propofol-induced loss of consciousness (LOC). After LOC, traveling waves in the slow-delta (∼1 Hz) range increased, grew more organized, and traveled in different directions relative to the awake state. Higher frequency (8-30 Hz) traveling waves, by contrast, decreased, lost structure, and switched to directions where the slow-delta waves were less frequent. The results suggest that LOC may be due, in part, to increases in the strength and direction of slow-delta traveling waves that, in turn, alter and disrupt traveling waves in the higher frequencies associated with cognition.

Slow waves form expanding, memory-rich mesostates steered by local excitability in fading anesthesia

In the arousal process, the brain restores its integrative activity from the synchronized state of slow wave activity (SWA). The mechanisms underpinning this state transition remain, however, to be elucidated. Here we simultaneously probed neuronal assemblies throughout the whole cortex with micro-electrocorticographic recordings in mice. We investigated the progressive shaping of propagating SWA at different levels of isoflurane. We found a form of memory of the wavefront shapes at deep anesthesia, tightly alternating posterior-anterior-posterior patterns. At low isoflurane, metastable patterns propagated in more directions, reflecting an increased complexity. The wandering across these mesostates progressively increased its randomness, as predicted by simulations of a network of spiking neurons, and confirmed in our experimental data. The complexity increase is explained by the elevated excitability of local assemblies with no modifications of the network connectivity. These results shed new light on the functional reorganization of the cortical network as anesthesia fades out.

•

Complexity of isoflurane-induced slow waves reliably determines anesthesia level

•

In deep anesthesia, the propagation strictly alternates between front-back-front patterns

•

In light anesthesia, there is a continuum of directions and faster propagation

•

Local excitability underpins the cortical reorganization in fading anesthesia

Cortical neural dynamics unveil the rhythm of natural visual behavior in marmosets

Numerous studies have shown that the visual system consists of functionally distinct ventral and dorsal streams; however, its exact spatial-temporal dynamics during natural visual behavior remain to be investigated. Here, we report cerebral neural dynamics during active visual exploration recorded by an electrocorticographic array covering the entire lateral surface of the marmoset cortex. We found that the dorsal stream was activated before the primary visual cortex with saccades and followed by the alteration of suppression and activation signals along the ventral stream. Similarly, the signal that propagated from the dorsal to ventral visual areas was accompanied by a travelling wave of low frequency oscillations. Such signal dynamics occurred at an average of 220 ms after saccades, which corresponded to the timing when whole-brain activation returned to background levels. We also demonstrated that saccades could occur at any point of signal flow, indicating the parallel computation of motor commands. Overall, this study reveals the neural dynamics of active vision, which are efficiently linked to the natural rhythms of visual exploration.