Propagating waves mediate information transfer in the motor cortex

887

741

The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. (i) It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. (ii) It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. (iii) It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in vitro in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales.brain models ͉ cerebral cortex ͉ diffusion tensor imaging ͉ oscillations ͉ spike-timing-dependent synaptic plasticity T he last decade has seen great progress in our understanding of brain dynamics and underlying neuronal mechanisms. Linking these mechanisms to behavior such as perception is facilitated by large-scale computer simulations of anatomically detailed models of the cerebral cortex (1-3). Although these models have stressed microcircuitry and local dynamics, they have not incorporated multiple cortical regions, corticocortical connections, and synaptic plasticity. In the present article, we describe a large-scale model of the mammalian thalamocortical system that includes these components.Spatiotemporal dynamics of the simulation show that some features of normal brain activity, although not explicitly built into the model, emerged spontaneously. The model exhibited selfsustained activity in the absence of any external sources of input. The behavior of the model was extremely sensitive to contributions of individual spikes: adding or removing one spike of one neuron completely changed the state of the entire cortex in Ͻ0.5 s. Regions of the model brain exhibited collective waves and oscillations of local field potentials in the delta, alpha, and beta ranges, similar to those recorded in humans (4). Simulated fMRI signals exhibited slow fronto-parietal anti-phase oscillations, as seen in humans (5).The shape and connectivity of the model were determined by diffusion tensor imaging (DTI) data for a human brain. Experimental data from three species, human, cat, and rat, were incorporated to build other details of the model.Model ...

Section: Results Of Simulationssupporting

confidence: 62%

Large-scale model of mammalian thalamocortical systems

Izhikevich

Edelman

2008

887

741

“…Numerous studies in nonhuman primates have demonstrated significant task-related effects in the low beta band (4,5,(27)(28)(29), but except for the elegant studies of Tan and colleagues (30,31), oscillation during the postmovement period has not been analyzed in detail. Brief episodes of heightened spike activity at task end have been documented in both nonhuman primates and rodents (16)(17)(18)(19)(20)32).…”

Section: Discussionmentioning

confidence: 99%

“…basal ganglia | local field potentials | beta band | sequential movement | synchronization O scillations of brain activity in the beta band (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) have been implicated in sensorimotor control and integration (1)(2)(3)(4)(5) and are pathologically synchronized and exaggerated in Parkinson's disease (6)(7)(8)(9)(10)(11). Although reports have published examples of very brief (<150 ms) bursts of beta-band oscillation (12)(13)(14)(15), the analysis of beta-band activity has focused primarily on data averaged over trials, which show variations in average beta-band power occurring on a time scale of seconds.…”

mentioning

confidence: 99%

Bursts of beta oscillation differentiate postperformance activity in the striatum and motor cortex of monkeys performing movement tasks

Feingold

Gibson

DePasquale

et al. 2015

339

386

Studies of neural oscillations in the beta band (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) have demonstrated modulations in beta-band power associated with sensory and motor events on time scales of 1 s or more, and have shown that these are exaggerated in Parkinson's disease. However, even early reports of beta activity noted extremely fleeting episodes of beta-band oscillation lasting <150 ms. Because the interpretation of possible functions for beta-band oscillations depends strongly on the time scale over which they occur, and because of these oscillations' potential importance in Parkinson's disease and related disorders, we analyzed in detail the distributions of duration and power for beta-band activity in a large dataset recorded in the striatum and motor-premotor cortex of macaque monkeys performing reaching tasks. Both regions exhibited typical beta-band suppression during movement and postmovement rebounds of up to 3 s as viewed in data averaged across trials, but single-trial analysis showed that most beta oscillations occurred in brief bursts, commonly 90-115 ms long. In the motor cortex, the burst probabilities peaked following the last movement, but in the striatum, the burst probabilities peaked at task end, after reward, and continued through the postperformance period. Thus, what appear to be extended periods of postperformance beta-band synchronization reflect primarily the modulated densities of short bursts of synchrony occurring in region-specific and task-time-specific patterns. We suggest that these short-time-scale events likely underlie the functions of most beta-band activity, so that prolongation of these beta episodes, as observed in Parkinson's disease, could produce deleterious network-level signaling.basal ganglia | local field potentials | beta band | sequential movement | synchronization O scillations of brain activity in the beta band (13-30 Hz) have been implicated in sensorimotor control and integration (1-5) and are pathologically synchronized and exaggerated in Parkinson's disease (6-11). Although reports have published examples of very brief (<150 ms) bursts of beta-band oscillation (12-15), the analysis of beta-band activity has focused primarily on data averaged over trials, which show variations in average beta-band power occurring on a time scale of seconds.To address the apparent discrepancy in time scales between the single-trial results and the trial-averaged results, we analyzed the relationship of brief beta bursts as viewed at a single-trial level to the substantially slower variations in trial-averaged power that are conventionally referred to as periods of "synchronization" or "desynchronization" of beta-band activity. We examined betaband activity recorded in two regions of prime clinical interest, the motor-premotor regions of the neocortex and the striatum, in macaque monkeys performing well-learned movement sequences. Our findings suggest that these regions exhibit different peak times of synchronization of beta bursting during...

“…However, much of the information about traveling waves is based on studies in anesthetized animals (53)(54)(55). Although recent developments in multielectrode array recording technology have enabled the detection of (γ) fast oscillatory waves in anesthetized preparations (56), it is only recently that traveling β-waves have been reported in the awake state (57,58). In a recent study, it was shown that the anisotropy of the cortical wiring may influence the spatiotemporal spike patterning (at least in the motor cortex) and that FS cells were found to fire more reliably in phase with the β-oscillation (59).…”

Section: Discussionmentioning

confidence: 99%

High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle

Quyen

Muller

Teleńczuk

et al. 2016

Beta (β)-and gamma (γ)-oscillations are present in different cortical areas and are thought to be inhibition-driven, but it is not known if these properties also apply to γ-oscillations in humans. Here, we analyze such oscillations in high-density microelectrode array recordings in human and monkey during the wake-sleep cycle. In these recordings, units were classified as excitatory and inhibitory cells. We find that γ-oscillations in human and β-oscillations in monkey are characterized by a strong implication of inhibitory neurons, both in terms of their firing rate and their phasic firing with the oscillation cycle. The β-and γ-waves systematically propagate across the array, with similar velocities, during both wake and sleep. However, only in slow-wave sleep (SWS) β-and γ-oscillations are associated with highly coherent and functional interactions across several millimeters of the neocortex. This interaction is specifically pronounced between inhibitory cells. These results suggest that inhibitory cells are dominantly involved in the genesis of β-and γ-oscillations, as well as in the organization of their large-scale coherence in the awake and sleeping brain. The highest oscillation coherence found during SWS suggests that fast oscillations implement a highly coherent reactivation of wake patterns that may support memory consolidation during SWS.excitation | inhibition | state-dependent firing | wave propagation | synchrony N eocortical beta (β)-and gamma (γ)-oscillations have been largely studied in the context of action/cognition during wakefulness (1-3). Although numerous studies have focused intensely on the information-processing role of β and γ during wakefulness, these rhythms do also occur during deep anesthesia and natural sleep (4-6). Additionally, although very-high-frequency allocortical oscillations (e.g., ripples at 80-200 Hz and fast ripples at 200-500 Hz in rodent/human hippocampus) have been thoroughly implicated in sleep-dependent memory consolidation (ref. 7 and reviewed in refs. 8 and 9), the occurrence and role of neocortical β-and γ-oscillations during sleep remain largely unexplored and related studies have been limited to macroscopic (whole-brain) scales (e.g., ref. 10). A recent study using microwires and intracranial electroencephalography (iEEG) recordings in the neocortex of epileptics has verified the strong presence of γ-oscillations during slow-wave sleep (SWS) (11). That study also showed a marked increase of spiking during γ, a suggestive indicator of association with cortical UP states. Here, we evaluate the presence of neocortical β-and γ-oscillations during the wake-sleep cycle at the level of local circuitry using dense 2D multielectrode recordings with identified excitatory and inhibitory cells (12, 13).Experimental and theoretical studies suggest that thalamocortical oscillations (including β and γ) are generated through a combination of intrinsic mechanisms involving the interplay between different ionic currents and extrinsic interaction of inhibitory and excitatory c...