How local is the local field potential?

Dissociation of tic generation from tic expression during the sleep-wake cycle

Motor tics, the hallmark of Tourette syndrome (TS), are modulated by different behavioral and environmental factors. A major modulating factor is the sleep-wake cycle in which tics are attenuated to a large extent during sleep. This study demonstrates a similar reduction in tic expression during sleep in an animal model of chronic tic disorders and investigates the underlying neural mechanism. We recorded the neuronal activity during spontaneous sleep-wake cycles throughout continuous GABA_A antagonist infusion into the striatum. Analysis of video streams and concurrent kinematic assessments indicated tic reduction during sleep in both frequency and intensity. Extracellular recordings in the striatum revealed a state-dependent dissociation between motor tic expression and their macro-level neural correlates ("LFP spikes") during the sleep-wake cycle. Local field potential (LFP) spikes, which are highly correlated with tic expression during wakefulness, persisted during tic-free sleep and did not change their properties despite the reduced behavioral expression. Local, micro-level, activity near the infusion site was time-locked to the LFP spikes during wakefulness, but this locking decreased significantly during sleep. These results suggest that whereas LFP spikes encode motor tic generation and feasibility, the behavioral expression of tics requires local striatal neural activity entrained to the LFP spikes, leading to the propagation of the activity to downstream targets and consequently their motor expression. These findings point to a possible mechanism for the modulation of tic expression in patients with TS during sleep and potentially during other behavioral states.

Neural activity temporal pattern dictates long-range propagation targets

Brain possesses a complex spatiotemporal architecture for efficient information processing and computing. However, it remains unknown how neural signal propagates to its intended targets brain-wide. Using optogenetics and functional MRI, we arbitrarily initiated various discrete neural activity pulse trains with different temporal patterns and revealed their distinct long-range propagation targets within the well-defined, topographically organized somatosensory thalamo-cortical circuit. We further observed that such neural activity propagation over long range could modulate brain-wide sensory functions. Electrophysiological analysis indicated that distinct propagation pathways arose from system level neural adaptation and facilitation in response to the neural activity temporal characteristics. Together, our findings provide fundamental insights into the long-range information transfer and processing. They directly support that temporal coding underpins the whole brain functional architecture in presence of the vast and relatively static anatomical architecture.

Computational Models in Electroencephalography

Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

A model integrating multiple processes of synchronization and coherence for information instantiation within a cortical area

What is the form of dynamic, e.g., sensory, information in the mammalian cortex? Information in the cortex is modeled as a coherence map of a mixed chimera state of synchronous, phasic, and disordered minicolumns. The theoretical model is built on neurophysiological evidence. Complex spatiotemporal information is instantiated through a system of interacting biological processes that generate a synchronized cortical area, a coherent aperture. Minicolumn elements are grouped in macrocolumns in an array analogous to a phased-array radar, modeled as an aperture, a "hole through which radiant energy flows." Coherence maps in a cortical area transform inputs from multiple sources into outputs to multiple targets, while reducing complexity and entropy. Coherent apertures can assume extremely large numbers of different information states as coherence maps, which can be communicated among apertures with corresponding very large bandwidths. The coherent aperture model incorporates considerable reported research, integrating five conceptually and mathematically independent processes: 1) a damped Kuramoto network model, 2) a pumped area field potential, 3) the gating of nearly coincident spikes, 4) the coherence of activity across cortical lamina, and 5) complex information formed through functions in macrocolumns. Biological processes and their interactions are described in equations and a functional circuit such that the mathematical pieces can be assembled the same way the neurophysiological ones are. The model can be conceptually convolved over the specifics of local cortical areas within and across species. A coherent aperture becomes a node in a graph of cortical areas with a corresponding distribution of information.

The Argo: a high channel count recording system for neural recording in vivo

OBJECTIVE

Decoding neural activity has been limited by the lack of tools available to record from large numbers of neurons across multiple cortical regions simultaneously with high temporal fidelity. To this end, we developed the Argo system to record cortical neural activity at high data rates.

APPROACH

Here we demonstrate a massively parallel neural recording system based on platinum-iridium microwire electrode arrays bonded to a CMOS voltage amplifier array. The Argo system is the highest channel count in vivo neural recording system, supporting simultaneous recording from 65 536 channels, sampled at 32 kHz and 12-bit resolution. This system was designed for cortical recordings, compatible with both penetrating and surface microelectrodes.

MAIN RESULTS

We validated this system through initial bench testing to determine specific gain and noise characteristics of bonded microwires, followed by in-vivo experiments in both rat and sheep cortex. We recorded spiking activity from 791 neurons in rats and surface local field potential activity from over 30 000 channels in sheep.

SIGNIFICANCE

These are the largest channel count microwire-based recordings in both rat and sheep. While currently adapted for head-fixed recording, the microwire-CMOS architecture is well suited for clinical translation. Thus, this demonstration helps pave the way for a future high data rate intracortical implant.

Lack of redundancy between electrophysiological measures of long-range neuronal communication

Background

Communication between brain areas has been implicated in a wide range of cognitive and emotive functions and is impaired in numerous mental disorders. In rodent models, various metrics have been used to quantify inter-regional neuronal communication. However, in individual studies, typically, only very few measures of coupling are reported and, hence, redundancy across such indicators is implicitly assumed.

Results

In order to test this assumption, we here comparatively assessed a broad range of directional and non-directional metrics like coherence, Weighted Phase Lag Index (wPLI), phase-locking value (PLV), pairwise phase consistency (PPC), parametric and non-parametric Granger causality (GC), partial directed coherence (PDC), directed transfer function (DTF), spike-phase coupling (SPC), cross-regional phase-amplitude coupling, amplitude cross-correlations and others. We applied these analyses to simultaneous field recordings from the prefrontal cortex and the ventral and dorsal hippocampus in the schizophrenia-related Gria1-knockout mouse model which displays a robust novelty-induced hyperconnectivity phenotype. Using the detectability of coupling deficits in Gria1^−/− mice and bivariate correlations within animals as criteria, we found that across such measures, there is a considerable lack of functional redundancy. Except for three pairwise correlations—PLV with PPC, PDC with DTF and parametric with non-parametric Granger causality—almost none of the analysed metrics consistently co-varied with any of the other measures across the three connections and two genotypes analysed. Notable exceptions to this were the correlation of coherence with PPC and PLV that was found in most cases, and partial correspondence between these three measures and Granger causality. Perhaps most surprisingly, partial directed coherence and Granger causality—sometimes regarded as equivalent measures of directed influence—diverged profoundly. Also, amplitude cross-correlation, spike-phase coupling and theta-gamma phase-amplitude coupling each yielded distinct results compared to all other metrics.

Conclusions

Our analysis highlights the difficulty of quantifying real correlates of inter-regional information transfer, underscores the need to assess multiple coupling measures and provides some guidelines which metrics to choose for a comprehensive, yet non-redundant characterization of functional connectivity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-00950-4.

Theta, but Not Gamma Oscillations in Area V4 Depend on Input from Primary Visual Cortex

Theta (3-9 Hz) and gamma (30-100 Hz) oscillations have been observed at different levels along the hierarchy of cortical areas and across a wide set of cognitive tasks. In the visual system, the emergence of both rhythms in primary visual cortex (V1) and mid-level cortical areas V4 has been linked with variations in perceptual reaction times.1-5 Based on analytical methods to infer causality in neural activation patterns, it was concluded that gamma and theta oscillations might both reflect feedforward sensory processing from V1 to V4.6-10 Here, we report on experiments in macaque monkeys in which we experimentally assessed the presence of both oscillations in the neural activity recorded from multi-electrode arrays in V1 and V4 before and after a permanent V1 lesion. With intact cortex, theta and gamma oscillations could be reliably elicited in V1 and V4 when monkeys viewed a visual contour illusion and showed phase-to-amplitude coupling. Laminar analysis in V1 revealed that both theta and gamma oscillations occurred primarily in the supragranular layers, the cortical output compartment of V1. However, there was a clear dissociation between the two rhythms in V4 that became apparent when the major feedforward input to V4 was removed by lesioning V1: although V1 lesioning eliminated V4 theta, it had little effect on V4 gamma power except for delaying its emergence by >100 ms. These findings suggest that theta is more tightly associated with feedforward processing than gamma and pose limits on the proposed role of gamma as a feedforward mechanism.

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

OBJECTIVE

Brain-machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs.

APPROACH

We propose entire spiking activity (ESA) -an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique- as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks.

MAIN RESULTS

Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data.

SIGNIFICANCE

Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.

Decision Signals in the Local Field Potentials of Early and Mid-Level Macaque Visual Cortex

The neural basis of perceptual decision making has typically been studied using measurements of single neuron activity, though decisions are likely based on the activity of large neuronal ensembles. Local field potentials (LFPs) may, in some cases, serve as a useful proxy for population activity and thus be useful for understanding the neural basis of perceptual decision making. However, little is known about whether LFPs in sensory areas include decision-related signals. We therefore analyzed LFPs recorded using two 48-electrode arrays implanted in primary visual cortex (V1) and area V4 of macaque monkeys trained to perform a fine orientation discrimination task. We found significant choice information in low (0-30 Hz) and higher (70-500 Hz) frequency components of the LFP, but little information in gamma frequencies (30-70 Hz). Choice information was more robust in V4 than V1 and stronger in LFPs than in simultaneously measured spiking activity. LFP-based choice information included a global component, common across electrodes within an area. Our findings reveal the presence of robust choice-related signals in the LFPs recorded in V1 and V4 and suggest that LFPs may be a useful complement to spike-based analyses of decision making.

Stimulus Feature-Specific Information Flow Along the Columnar Cortical Microcircuit Revealed by Multivariate Laminar Spiking Analysis

Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

Impact of referencing scheme on decoding performance of LFP-based brain-machine interface

OBJECTIVE

There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs.

APPROACH

To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks.

MAIN RESULTS

Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions. Significance Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.

Local and Volume-Conducted Contributions to Cortical Field Potentials

Brain field potentials (FPs) can reach far from their sources, making difficult to know which waves come from where. We show that modern algorithms efficiently segregate the local and remote contributions to cortical FPs by recovering the generator-specific spatial voltage profiles. We investigated experimentally and numerically the local and remote origin of FPs in different cortical areas in anesthetized rats. All cortices examined show significant state, layer, and region dependent contribution of remote activity, while the voltage profiles help identify their subcortical or remote cortical origin. Co-activation of different cortical modules can be discriminated by the distinctive spatial features of the corresponding profiles. All frequency bands contain remote activity, thus influencing the FP time course, in cases drastically. The reach of different FP patterns is boosted by spatial coherence and curved geometry of the sources. For instance, slow cortical oscillations reached the entire brain, while hippocampal theta reached only some portions of the cortex. In anterior cortices, most alpha oscillations have a remote origin, while in the visual cortex the remote theta and gamma even surpass the local contribution. The quantitative approach to local and distant FP contributions helps to refine functional connectivity among cortical regions, and their relation to behavior.

The Neural Engine: A Reprogrammable Low Power Platform for Closed-Loop Optogenetics

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.

State-Dependent Cortical Unit Activity Reflects Dynamic Brain State Transitions in Anesthesia

Understanding the effects of anesthesia on cortical neuronal spiking and information transfer could help illuminate the neuronal basis of the conscious state. Recent investigations suggest that the brain state identified by local field potential spectrum is not stationary but changes spontaneously at a fixed level of anesthetic concentration. How cortical unit activity changes with dynamically transitioning brain states under anesthesia is unclear. Extracellular unit activity was measured with 64-channel silicon microelectrode arrays in cortical layers 5/6 of the primary visual cortex of chronically instrumented, freely moving male rats (n = 7) during stepwise reduction of the anesthetic desflurane (6%, 4%, 2%, and 0%). Unsupervised machine learning applied to multiunit spike patterns revealed five distinct brain states. A novel desynchronized brain state with increased spike rate variability, sample entropy, and EMG activity occurred in 6% desflurane with 40.0% frequency. The other four brain states reflected graded levels of anesthesia. As anesthesia deepened the spike rate of neurons decreased regardless of their spike rate profile at baseline conscious state. Actively firing neurons with wide-spiking pattern showed increased bursting activity along with increased spike timing variability, unit-to-population correlation, and unit-to-unit transfer entropy, despite the overall decrease in transfer entropy. The narrow-spiking neurons showed similar changes but to a lesser degree. These results suggest that (1) anesthetic effect on spike rate is distinct from sleep, (2) synchronously fragmented spiking pattern is a signature of anesthetic-induced unconsciousness, and (3) the paradoxical, desynchronized brain state in deep anesthesia contends the generally presumed monotonic, dose-dependent anesthetic effect on the brain.SIGNIFICANCE STATEMENT Recent studies suggest that spontaneous changes in brain state occur under anesthesia. However, the spiking behavior of cortical neurons associated with such state changes has not been investigated. We found that local brain states defined by multiunit activity had a nonunitary relationship with the current anesthetic level. A paradoxical brain state displaying asynchronous firing pattern and high EMG activity was found unexpectedly in deep anesthesia. In contrast, the synchronous fragmentation of neuronal spiking appeared to be a robust signature of the state of anesthesia. The findings challenge the assumption of monotonic, anesthetic dose-dependent behavior of cortical neuron populations. They enhance the interpretation of neuroscientific data obtained under anesthesia and the understanding of the neuronal basis of anesthetic-induced state of unconsciousness.

Neurovascular coupling and bilateral connectivity during NREM and REM sleep

To understand how arousal state impacts cerebral hemodynamics and neurovascular coupling, we monitored neural activity, behavior, and hemodynamic signals in un-anesthetized, head-fixed mice. Mice frequently fell asleep during imaging, and these sleep events were interspersed with periods of wake. During both NREM and REM sleep, mice showed large increases in cerebral blood volume ([HbT]) and arteriole diameter relative to the awake state, two to five times larger than those evoked by sensory stimulation. During NREM, the amplitude of bilateral low-frequency oscillations in [HbT] increased markedly, and coherency between neural activity and hemodynamic signals was higher than the awake resting and REM states. Bilateral correlations in neural activity and [HbT] were highest during NREM, and lowest in the awake state. Hemodynamic signals in the cortex are strongly modulated by arousal state, and changes during sleep are substantially larger than sensory-evoked responses.

Crossmodal Phase Reset and Evoked Responses Provide Complementary Mechanisms for the Influence of Visual Speech in Auditory Cortex

Natural conversation is multisensory: when we can see the speaker's face, visual speech cues improve our comprehension. The neuronal mechanisms underlying this phenomenon remain unclear. The two main alternatives are visually mediated phase modulation of neuronal oscillations (excitability fluctuations) in auditory neurons and visual input-evoked responses in auditory neurons. Investigating this question using naturalistic audiovisual speech with intracranial recordings in humans of both sexes, we find evidence for both mechanisms. Remarkably, auditory cortical neurons track the temporal dynamics of purely visual speech using the phase of their slow oscillations and phase-related modulations in broadband high-frequency activity. Consistent with known perceptual enhancement effects, the visual phase reset amplifies the cortical representation of concomitant auditory speech. In contrast to this, and in line with earlier reports, visual input reduces the amplitude of evoked responses to concomitant auditory input. We interpret the combination of improved phase tracking and reduced response amplitude as evidence for more efficient and reliable stimulus processing in the presence of congruent auditory and visual speech inputs.SIGNIFICANCE STATEMENT Watching the speaker can facilitate our understanding of what is being said. The mechanisms responsible for this influence of visual cues on the processing of speech remain incompletely understood. We studied these mechanisms by recording the electrical activity of the human brain through electrodes implanted surgically inside the brain. We found that visual inputs can operate by directly activating auditory cortical areas, and also indirectly by modulating the strength of cortical responses to auditory input. Our results help to understand the mechanisms by which the brain merges auditory and visual speech into a unitary perception.

A Minimal Biophysical Model of Neocortical Pyramidal Cells: Implications for Frontal Cortex Microcircuitry and Field Potential Generation

Ca²⁺ spikes initiated in the distal trunk of layer 5 pyramidal cells (PCs) underlie nonlinear dynamic changes in the gain of cellular response, critical for top-down control of cortical processing. Detailed models with many compartments and dozens of ionic channels can account for this Ca²⁺ spike-dependent gain and associated critical frequency. However, current models do not account for all known Ca²⁺-dependent features. Previous attempts to include more features have required increasing complexity, limiting their interpretability and utility for studying large population dynamics. We overcome these limitations in a minimal two-compartment biophysical model. In our model, a basal-dendrites/somatic compartment included fast-inactivating Na⁺ and delayed-rectifier K⁺ conductances, while an apical-dendrites/trunk compartment included persistent Na⁺, hyperpolarization-activated cation (I _h ), slow-inactivating K⁺, muscarinic K⁺, and Ca²⁺ L-type. The model replicated the Ca²⁺ spike morphology and its critical frequency plus three other defining features of layer 5 PC synaptic integration: linear frequency-current relationships, back-propagation-activated Ca²⁺ spike firing, and a shift in the critical frequency by blocking I _h Simulating 1000 synchronized layer 5 PCs, we reproduced the current source density patterns evoked by Ca²⁺ spikes and describe resulting medial-frontal EEG on a male macaque monkey. We reproduced changes in the current source density when I _h was blocked. Thus, a two-compartment model with five crucial ionic currents in the apical dendrites reproduces all features of these neurons. We discuss the utility of this minimal model to study the microcircuitry of agranular areas of the frontal lobe involved in cognitive control and responsible for event-related potentials, such as the error-related negativity.SIGNIFICANCE STATEMENT A minimal model of layer 5 pyramidal cells replicates all known features crucial for distal synaptic integration in these neurons. By redistributing voltage-gated and returning transmembrane currents in the model, we establish a theoretical framework for the investigation of cortical microcircuit contribution to intracranial local field potentials and EEG. This tractable model will enable biophysical evaluation of multiscale electrophysiological signatures and computational investigation of cortical processing.

Recent Advances in Electrical Neural Interface Engineering: Minimal Invasiveness, Longevity, and Scalability

Electrical neural interfaces serve as direct communication pathways that connect the nervous system with the external world. Technological advances in this domain are providing increasingly more powerful tools to study, restore, and augment neural functions. Yet, the complexities of the nervous system give rise to substantial challenges in the design, fabrication, and system-level integration of these functional devices. In this review, we present snapshots of the latest progresses in electrical neural interfaces, with an emphasis on advances that expand the spatiotemporal resolution and extent of mapping and manipulating brain circuits. We include discussions of large-scale, long-lasting neural recording; wireless, miniaturized implants; signal transmission, amplification, and processing; as well as the integration of interfaces with optical modalities. We outline the background and rationale of these developments and share insights into the future directions and new opportunities they enable.

Multichannel parallel processing of neural signals in memristor arrays

Fully implantable neural interfaces with massive recording channels bring the gospel to patients with motor or speech function loss. As the number of recording channels rapidly increases, conventional complementary metal-oxide semiconductor (CMOS) chips for neural signal processing face severe challenges on parallelism scalability, computational cost, and power consumption. In this work, we propose a previously unexplored approach for parallel processing of multichannel neural signals in memristor arrays, taking advantage of their rich dynamic characteristics. The critical information of neural signal waveform is extracted and encoded in the memristor conductance modulation. A signal segmentation scheme is developed to adapt to device variations. To verify the fidelity of the processed results, seizure prediction is further demonstrated, with high accuracy above 95% and also more than 1000× improvement in power efficiency compared with CMOS counterparts. This work suggests that memristor arrays could be a promising multichannel signal processing module for future implantable neural interfaces.

A methodological framework for inverse-modeling of propagating cortical activity using MEG/EEG

The prevailing view on the dynamics of large-scale electrical activity in the human cortex is that it constitutes a functional network of discrete and localized circuits. Within this view, a natural way to analyse magnetoencephalographic (MEG) and electroencephalographic (EEG) data is by adopting methods from network theory. Invasive recordings, however, demonstrate that cortical activity is spatially continuous, rather than discrete, and exhibits propagation behavior. Furthermore, human cortical activity is known to propagate under a variety of conditions such as non-REM sleep, general anesthesia, and coma. Although several MEG/EEG studies have investigated propagating cortical activity, not much is known about the conditions under which such activity can be successfully reconstructed from MEG/EEG sensor-data. This study provides a methodological framework for inverse-modeling of propagating cortical activity. Within this framework, cortical activity is represented in the spatial frequency domain, which is more natural than the dipole domain when dealing with spatially continuous activity. We define angular power spectra, which show how the power of cortical activity is distributed across spatial frequencies, angular gain/phase spectra, which characterize the spatial filtering properties of linear inverse operators, and angular resolution matrices, which summarize how linear inverse operators leak signal within and across spatial frequencies. We adopt the framework to provide insight into the performance of several linear inverse operators in reconstructing propagating cortical activity from MEG/EEG sensor-data. We also describe how prior spatial frequency information can be incorporated into the inverse-modeling to obtain better reconstructions.

Neuronal Encoding of Multisensory Motion Features in the Rat Associative Parietal Cortex

Motion perception is facilitated by the interplay of various sensory channels. In rodents, the cortical areas involved in multisensory motion coding remain to be identified. Using voltage-sensitive-dye imaging, we revealed a visuo-tactile convergent region that anatomically corresponds to the associative parietal cortex (APC). Single unit responses to moving visual gratings or whiskers deflections revealed a specific coding of motion characteristics strikingly found in both sensory modalities. The heteromodality of this region was further supported by a large proportion of bimodal neurons and by a classification procedure revealing that APC carries information about motion features, sensory origin and multisensory direction-congruency. Altogether, the results point to a central role of APC in multisensory integration for motion perception.

Deep brain stimulation and recordings: Insights into the contributions of subthalamic nucleus in cognition

Recent progress in targeted interrogation of basal ganglia structures and networks with deep brain stimulation in humans has provided insights into the complex functions the subthalamic nucleus (STN). Beyond the traditional role of the STN in modulating motor function, recognition of its role in cognition was initially fueled by side effects seen with STN DBS and later revealed with behavioral and electrophysiological studies. Anatomical, clinical, and electrophysiological data converge on the view that the STN is a pivotal node linking cognitive and motor processes. The goal of this review is to synthesize the literature to date that used DBS to examine the contributions of the STN to motor and non-motor cognitive functions and control. Multiple modalities of research have provided us with an enhanced understanding of the STN and reveal that it is critically involved in motor and non-motor inhibition, decision-making, motivation and emotion. Understanding the role of the STN in cognition can enhance the therapeutic efficacy and selectivity not only for existing applications of DBS, but also in the development of therapeutic strategies to stimulate aberrant circuits to treat non-motor symptoms of Parkinson's disease and other disorders.

Alterations of distributed neuronal network oscillations during acute pain in freely-moving mice

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Injection of capsaicine in mice causes prolonged acute pain and characteristic changes in neuronal network oscillations.

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Changes are most prominent in higher-order phenomena like interregional oscillation coherence.

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Power in standard frequency bands is largely unaltered.

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Behavioral states related to acute pain can be predicted from network activity by a logistic regression classifier.

The experience of pain involves the activation of multiple brain areas. Pain-specific activity patterns within and between these local networks remain, however, largely unknown. We measured neuronal network oscillations in different relevant regions of the mouse brain during acute pain, induced by subcutaneous injection of capsaicin into the left hind paw. Field potentials were recorded from primary somatosensory cortex, anterior cingulate cortex (ACC), posterior insula, ventral posterolateral thalamic nucleus, parietal cortex, central nucleus of the amygdala and olfactory bulb. Analysis included power spectra of local signals as well as interregional coherences and cross-frequency coupling (CFC). Capsaicin injection caused hypersensitivity to mechanical stimuli for at least one hour. At the same time, CFC between low (1−12 Hz) and fast frequencies (80−120 Hz) was increased in the ACC, as well as interregional coherence of low frequency oscillations (< 30 Hz) between several networks. However, these changes were not significant anymore after multiple comparison corrections. Using a variable selection method (elastic net) and a logistic regression classifier, however, the pain state was reliably predicted by combining parameters of power and coherence from various regions. Distinction between capsaicin and saline injection was also possible when data were restricted to frequencies <30 Hz, as used in clinical electroencephalography (EEG). Our findings indicate that changes of distributed brain oscillations may provide a functional signature of acute pain or pain-related alterations in activity.

Aberrant Cortical Ensembles and Schizophrenia-like Sensory Phenotypes in Setd1a+/- Mice

BACKGROUND

A breakdown of synchrony within neuronal ensembles leading to destabilization of network "attractors" could be a defining aspect of neuropsychiatric diseases such as schizophrenia, representing a common downstream convergence point for the diverse etiological pathways associated with the disease. Using a mouse genetic model, we demonstrated that altered ensembles are associated with pathological sensory cortical processing phenotypes resulting from loss of function mutations in the Setd1a gene, a recently identified rare risk genotype with very high penetrance for schizophrenia.

METHODS

We used fast two-photon calcium imaging of neuronal populations (calcium indicator GCaMP6s, 10 Hz, 100-250 cells, layer 2/3 of primary visual cortex, i.e., V1) in awake head-fixed mice (Setd1a^+/- vs. wild-type littermate control) during rest and visual stimulation with moving full-field square-wave gratings (0.04 cycles per degree, 2.0 cycles per second, 100% contrast, 12 directions). Multielectrode recordings were analyzed in the time-frequency domain to assess stimulus-induced oscillations and cross-layer phase synchrony.

RESULTS

Neuronal activity and orientation/direction selectivity were unaffected in Setd1a^+/- mice, but correlations between cell pairs in V1 showed altered distributions compared with wild-type mice, in both ongoing and visually evoked activity. Furthermore, population-wide "ensemble activations" in Setd1a^+/- mice were markedly less reliable over time during rest and visual stimulation, resulting in unstable encoding of basic visual information. This alteration of ensembles coincided with reductions in alpha and high-gamma band phase synchrony within and between cortical layers.

CONCLUSIONS

These results provide new evidence for an ensemble hypothesis of schizophrenia and highlight the utility of Setd1a^+/- mice for modeling sensory-processing phenotypes.

Dissociation of broadband high-frequency activity and neuronal firing in the neocortex

Broadband high-frequency activity (BHA; 70 to 150 Hz), also known as "high gamma," a key analytic signal in human intracranial (electrocorticographic) recordings, is often assumed to reflect local neural firing [multiunit activity (MUA)]. As the precise physiological substrates of BHA are unknown, this assumption remains controversial. Our analysis of laminar multielectrode data from V1 and A1 in monkeys outlines two components of stimulus-evoked BHA distributed across the cortical layers: an "early-deep" and "late-superficial" response. Early-deep BHA has a clear spatial and temporal overlap with MUA. Late-superficial BHA was more prominent and accounted for more of the BHA signal measured near the cortical pial surface. However, its association with local MUA is weak and often undetectable, consistent with the view that it reflects dendritic processes separable from local neuronal firing.

Multiple signals evoked by unisensory stimulation converge onto cerebellar granule and Purkinje cells in mice

The cerebellum receives signals directly from peripheral sensory systems and indirectly from the neocortex. Even a single tactile stimulus can activate both of these pathways. Here we report how these different types of signals are integrated in the cerebellar cortex. We used in vivo whole-cell recordings from granule cells and unit recordings from Purkinje cells in mice in which primary somatosensory cortex (S1) could be optogenetically inhibited. Tactile stimulation of the upper lip produced two-phase granule cell responses (with latencies of ~8 ms and 29 ms), for which only the late phase was S1 dependent. In Purkinje cells, complex spikes and the late phase of simple spikes were S1 dependent. These results indicate that individual granule cells combine convergent inputs from the periphery and neocortex and send their outputs to Purkinje cells, which then integrate those signals with climbing fiber signals from the neocortex.

Dynamic Modulation of Cortical Excitability during Visual Active Sensing

Visual physiology is traditionally investigated by presenting stimuli with gaze held constant. However, during active viewing of a scene, information is actively acquired using systematic patterns of fixations and saccades. Prior studies suggest that during such active viewing, both nonretinal, saccade-related signals and “extra-classical” receptive field inputs modulate visual processing. This study used a set of active viewing tasks that allowed us to compare visual responses with and without direct foveal input, thus isolating the contextual eye movement-related influences. Studying nonhuman primates, we find strong contextual modulation in primary visual cortex (V1): excitability and response amplification immediately after fixation onset, transiting to suppression leading up to the next saccade. Time-frequency decomposition suggests that this amplification and suppression cycle stems from a phase reset of ongoing neuronal oscillatory activity. The impact of saccade-related contextual modulation on stimulus processing makes active visual sensing fundamentally different from the more passive processes investigated in traditional paradigms.

By isolating contextual eye movement-related influences during active vision, Barczak et al. show that eye movements affect excitability in V1 such that responses are amplified immediately after fixation onset and suppressed as the next saccade approaches. This amplification and suppression cycle stems from a phase reset of ambient oscillatory activity in V1.

Task rule and choice are reflected by layer-specific processing in rodent auditory cortical microcircuits

The primary auditory cortex (A1) is an essential, integrative node that encodes the behavioral relevance of acoustic stimuli, predictions, and auditory-guided decision-making. However, the realization of this integration with respect to the cortical microcircuitry is not well understood. Here, we characterize layer-specific, spatiotemporal synaptic population activity with chronic, laminar current source density analysis in Mongolian gerbils (Meriones unguiculatus) trained in an auditory decision-making Go/NoGo shuttle-box task. We demonstrate that not only sensory but also task- and choice-related information is represented in the mesoscopic neuronal population code of A1. Based on generalized linear-mixed effect models we found a layer-specific and multiplexed representation of the task rule, action selection, and the animal's behavioral options as accumulating evidence in preparation of correct choices. The findings expand our understanding of how individual layers contribute to the integrative circuit in the sensory cortex in order to code task-relevant information and guide sensory-based decision-making.

Global sleep homeostasis reflects temporally and spatially integrated local cortical neuronal activity

Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define 'Process S', a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5-4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space.

Probing regional cortical excitability via input-output properties using transcranial magnetic stimulation and electroencephalography coupling

The modular organization of the cortex refers to subsets of highly interconnected nodes, sharing specific cytoarchitectural and dynamical properties. These properties condition the level of excitability of local pools of neurons. In this study, we described TMS evoked potentials (TEP) input-output properties to provide new insights into regional cortical excitability. We combined robotized TMS with EEG to disentangle region-specific TEP from threshold to saturation and describe their oscillatory contents. Twenty-two young healthy participants received robotized TMS pulses over the right primary motor cortex (M1), the right dorsolateral prefrontal cortex (DLPFC) and the right superior occipital lobe (SOL) at five stimulation intensities (40, 60, 80, 100, and 120% resting motor threshold) and one short-interval intracortical inhibition condition during EEG recordings. Ten additional subjects underwent the same experiment with a realistic sham TMS procedure. The results revealed interregional differences in the TEPs input-output functions as well as in the responses to paired-pulse conditioning protocols, when considering early local components (<80 ms). Each intensity in the three regions was associated with complex patterns of oscillatory activities. The quality of the regression of TEPs over stimulation intensity was used to derive a new readout for cortical excitability and dynamical properties, revealing lower excitability in the DLPFC, followed by SOL and M1. The realistic sham experiment confirmed that these early local components were not contaminated by multisensory stimulations. This study provides an entirely new analytic framework to characterize input-output relations throughout the cortex, paving the way to a more accurate definition of local cortical excitability.

Intracranial-EEG evidence for medial temporal pole driving amygdala activity induced by multi-modal emotional stimuli

The temporal pole (TP) is an associative cortical region required for complex cognitive functions such as social and emotional cognition. However, mapping the TP with functional magnetic resonance imaging is technically challenging and thus understanding its interaction with other key emotional circuitry, such as the amygdala, remains elusive. We exploited the unique advantages of stereo-electroencephalography (sEEG) to assess the responses of the TP and the amygdala during the perception of emotionally salient stimuli of pictures, music and movies. These stimuli consistently elicited high gamma responses (70-140 Hz) in both the TP and the amygdala, accompanied by functional connectivity in the low frequency range (2-12 Hz). Computational analyses suggested that the TP drove this effect in the theta frequency range, modulated by the emotional valence of the stimuli. Notably, cross-frequency analysis indicated the phase of theta oscillations in the TP modulated the amplitude of high gamma activity in the amygdala. These results were reproducible across three types of sensory inputs including naturalistic stimuli. Our results suggest that multimodal emotional stimuli induce a hierarchical influence of the TP over the amygdala.

Significance and Translational Value of High-Frequency Cortico-Basal Ganglia Oscillations in Parkinson's Disease

The mechanisms and significance of basal ganglia oscillations is a fundamental research question engaging both clinical and basic investigators. In Parkinson’s disease (PD), neural activity in basal ganglia nuclei is characterized by oscillatory patterns that are believed to disrupt the dynamic processing of movement-related information and thus generate motor symptoms. Beta-band oscillations associated with hypokinetic states have been reviewed in several excellent previous articles. Here we focus on faster oscillatory phenomena that have been reported in association with a diverse range of motor states. We review the occurrence of different types of fast oscillations and the evidence supporting their pathophysiological role. We also provide a general discussion on the definition, possible mechanisms, and translational value of synchronized oscillations of different frequencies in cortico-basal ganglia structures. Revealing how oscillatory phenomena are caused and spread in cortico-basal ganglia-thalamocortical networks will offer a key to unlock the neural codes of both motor and non-motor symptoms in PD. In preclinical therapeutic research, recording of oscillatory neural activities holds the promise to unravel mechanisms of action of current and future treatments.

Synchronicity of excitatory inputs drives hippocampal networks to distinct oscillatory patterns

The rodent hippocampus expresses a variety of neuronal network oscillations depending on the behavioral state of the animal. Locomotion and active exploration are accompanied by theta-nested gamma oscillations while resting states and slow-wave sleep are dominated by intermittent sharp wave-ripple complexes. It is believed that gamma rhythms create a framework for efficient acquisition of information whereas sharp wave-ripples are thought to be involved in consolidation and retrieval of memory. While not strictly mutually exclusive, one of the two patterns usually dominates in a given behavioral state. Here we explore how different input patterns induce either of the two network states, using an optogenetic stimulation approach in hippocampal brain slices of mice. We report that the pattern of the evoked oscillation depends strongly on the initial synchrony of activation of excitatory cells within CA3. Short, synchronous activation favors the emergence of sharp wave-ripple complexes while persistent but less synchronous activity-as typical for sensory input during exploratory behavior-supports the generation of gamma oscillations. This dichotomy is reflected by different degrees of synchrony of excitatory and inhibitory synaptic currents within these two states. Importantly, the induction of these two fundamental network patterns does not depend on the presence of any neuromodulatory transmitter like acetylcholine, but is merely based on a different synchrony in the initial activation pattern.

Is there an Intrinsic Relationship between LFP Beta Oscillation Amplitude and Firing Rate of Individual Neurons in Macaque Motor Cortex?

The properties of motor cortical local field potential (LFP) beta oscillations have been extensively studied. Their relationship to the local neuronal spiking activity was also addressed. Yet, whether there is an intrinsic relationship between the amplitude of beta oscillations and the firing rate of individual neurons remains controversial. Some studies suggest a mapping of spike rate onto beta amplitude, while others find no systematic relationship. To help resolve this controversy, we quantified in macaque motor cortex the correlation between beta amplitude and neuronal spike count during visuomotor behavior. First, in an analysis termed “task-related correlation”, single-trial data obtained across all trial epochs were included. These correlations were significant in up to 32% of cases and often strong. However, a trial-shuffling control analysis recombining beta amplitudes and spike counts from different trials revealed these task-related correlations to reflect systematic, yet independent, modulations of the 2 signals with the task. Second, in an analysis termed “trial-by-trial correlation”, only data from fixed trial epochs were included, and correlations were calculated across trials. Trial-by-trial correlations were weak and rarely significant. We conclude that there is no intrinsic relationship between the firing rate of individual neurons and LFP beta oscillation amplitude in macaque motor cortex.

Volumetric mapping of the functional neuroanatomy of the respiratory network in the perfused brainstem preparation of rats

KEY POINTS

The functional neuroanatomy of the mammalian respiratory network is far from being understood since experimental tools that measure neural activity across this brainstem-wide circuit are lacking. Here, we use silicon multi-electrode arrays to record respiratory local field potentials (rLFPs) from 196-364 electrode sites within 8-10 mm³ of brainstem tissue in single arterially perfused brainstem preparations with respect to the ongoing respiratory motor pattern of inspiration (I), post-inspiration (PI) and late-expiration (E2). rLFPs peaked specifically at the three respiratory phase transitions, E2-I, I-PI and PI-E2. We show, for the first time, that only the I-PI transition engages a brainstem-wide network, and that rLFPs during the PI-E2 transition identify a hitherto unknown role for the dorsal respiratory group. Volumetric mapping of pontomedullary rLFPs in single preparations could become a reliable tool for assessing the functional neuroanatomy of the respiratory network in health and disease.

ABSTRACT

While it is widely accepted that inspiratory rhythm generation depends on the pre-Bötzinger complex, the functional neuroanatomy of the neural circuits that generate expiration is debated. We hypothesized that the compartmental organization of the brainstem respiratory network is sufficient to generate macroscopic local field potentials (LFPs), and if so, respiratory (r) LFPs could be used to map the functional neuroanatomy of the respiratory network. We developed an approach using silicon multi-electrode arrays to record spontaneous LFPs from hundreds of electrode sites in a volume of brainstem tissue while monitoring the respiratory motor pattern on phrenic and vagal nerves in the perfused brainstem preparation. Our results revealed the expression of rLFPs across the pontomedullary brainstem. rLFPs occurred specifically at the three transitions between respiratory phases: (1) from late expiration (E2) to inspiration (I), (2) from I to post-inspiration (PI), and (3) from PI to E2. Thus, respiratory network activity was maximal at respiratory phase transitions. Spatially, the E2-I, and PI-E2 transitions were anatomically localized to the ventral and dorsal respiratory groups, respectively. In contrast, our data show, for the first time, that the generation of controlled expiration during the post-inspiratory phase engages a distributed neuronal population within ventral, dorsal and pontine network compartments. A group-wise independent component analysis demonstrated that all preparations exhibited rLFPs with a similar temporal structure and thus share a similar functional neuroanatomy. Thus, volumetric mapping of rLFPs could allow for the physiological assessment of global respiratory network organization in health and disease.

Cortical Responses to Input From Distant Areas are Modulated by Local Spontaneous Alpha/Beta Oscillations

Any given area in human cortex may receive input from multiple, functionally heterogeneous areas, potentially representing different processing threads. Alpha (8-13 Hz) and beta oscillations (13-20 Hz) have been hypothesized by other investigators to gate local cortical processing, but their influence on cortical responses to input from other cortical areas is unknown. To study this, we measured the effect of local oscillatory power and phase on cortical responses elicited by single-pulse electrical stimulation (SPES) at distant cortical sites, in awake human subjects implanted with intracranial electrodes for epilepsy surgery. In 4 out of 5 subjects, the amplitudes of corticocortical evoked potentials (CCEPs) elicited by distant SPES were reproducibly modulated by the power, but not the phase, of local oscillations in alpha and beta frequencies. Specifically, CCEP amplitudes were higher when average oscillatory power just before distant SPES (-110 to -10 ms) was high. This effect was observed in only a subset (0-33%) of sites with CCEPs and, like the CCEPs themselves, varied with stimulation at different distant sites. Our results suggest that although alpha and beta oscillations may gate local processing, they may also enhance the responsiveness of cortex to input from distant cortical sites.

Stimulus-specific adaptation to behaviorally-relevant sounds in awake rats

Stimulus-specific adaptation (SSA) is the reduction in responses to a common stimulus that does not generalize, or only partially generalizes, to other stimuli. SSA has been studied mainly with sounds that bear no behavioral meaning. We hypothesized that the acquisition of behavioral meaning by a sound should modify the amount of SSA evoked by that sound. To test this hypothesis, we used fear conditioning in rats, using two word-like stimuli, derived from the English words "danger" and "safety", as well as pure tones. One stimulus (CS+) was associated with a foot shock whereas the other stimulus (CS-) was presented without a concomitant foot shock. We recorded neural responses to the auditory stimuli telemetrically, using chronically implanted multi-electrode arrays in freely moving animals before and after conditioning. Consistent with our hypothesis, SSA changed in a way that depended on the behavioral role of the sound: the contrast between standard and deviant responses remained the same or decreased for CS+ stimuli but increased for CS- stimuli, showing that SSA is shaped by experience. In most cases the sensory responses underlying these changes in SSA increased following conditioning. Unexpectedly, the responses to CS+ word-like stimuli showed a specific, large decrease, which we interpret as evidence for substantial inhibitory plasticity.

Neural oscillations in the fronto-striatal network predict vocal output in bats

The ability to vocalize is ubiquitous in vertebrates, but neural networks underlying vocal control remain poorly understood. Here, we performed simultaneous neuronal recordings in the frontal cortex and dorsal striatum (caudate nucleus, CN) during the production of echolocation pulses and communication calls in bats. This approach allowed us to assess the general aspects underlying vocal production in mammals and the unique evolutionary adaptations of bat echolocation. Our data indicate that before vocalization, a distinctive change in high-gamma and beta oscillations (50–80 Hz and 12–30 Hz, respectively) takes place in the bat frontal cortex and dorsal striatum. Such precise fine-tuning of neural oscillations could allow animals to selectively activate motor programs required for the production of either echolocation or communication vocalizations. Moreover, the functional coupling between frontal and striatal areas, occurring in the theta oscillatory band (4–8 Hz), differs markedly at the millisecond level, depending on whether the animals are in a navigational mode (that is, emitting echolocation pulses) or in a social communication mode (emitting communication calls). Overall, this study indicates that fronto-striatal oscillations could provide a neural correlate for vocal control in bats.

In bats, rhythmic activity in frontal and striatal areas of the brain provide a neural correlate for vocal control, which can be used to predict whether the ensuing vocalizations are for echolocation or social communication.

Electroencephalography

Electroencephalography (EEG) is the non-invasive measurement of the brain's electric fields. Electrodes placed on the scalp record voltage potentials resulting from current flow in and around neurons. EEG is nearly a century old: this long history has afforded EEG a rich and diverse spectrum of applications. On the one hand, foundations of EEG in clinical diagnostics have dovetailed more recently into brain-triggered neurorehabilitation treatments. On the other hand, EEG has not only been a workhorse for providing brain correlates of constructs in the field of experimental psychology, but has also been used as a true neuroimaging method with more recent extensions in translational as well as computational neuroscience. The versatility and accessibility of the technique, in combination with advances in signal processing, allow for this 'old dog' to still deliver new tricks and innovations.

Stereotactic electroencephalography in humans reveals multisensory signal in early visual and auditory cortices

Unequivocally demonstrating the presence of multisensory signals at the earliest stages of cortical processing remains challenging in humans. In our study, we relied on the unique spatio-temporal resolution provided by intracranial stereotactic electroencephalographic (SEEG) recordings in patients with drug-resistant epilepsy to characterize the signal extracted from early visual (calcarine and pericalcarine) and auditory (Heschl's gyrus and planum temporale) regions during a simple audio-visual oddball task. We provide evidences that both cross-modal responses (visual responses in auditory cortex or the reverse) and multisensory processing (alteration of the unimodal responses during bimodal stimulation) can be observed in intracranial event-related potentials (iERPs) and in power modulations of oscillatory activity at different temporal scales within the first 150 msec after stimulus onset. The temporal profiles of the iERPs are compatible with the hypothesis that MSI occurs by means of direct pathways linking early visual and auditory regions. Our data indicate, moreover, that MSI mainly relies on modulations of the low-frequency bands (foremost the theta band in the auditory cortex and the alpha band in the visual cortex), suggesting the involvement of feedback pathways between the two sensory regions. Remarkably, we also observed high-gamma power modulations by sounds in the early visual cortex, thus suggesting the presence of neuronal populations involved in auditory processing in the calcarine and pericalcarine region in humans.

Fast simulation of extracellular action potential signatures based on a morphological filtering approximation

Simulating extracellular recordings of neuronal populations is an important and challenging task both for understanding the nature and relationships between extracellular field potentials at different scales, and for the validation of methodological tools for signal analysis such as spike detection and sorting algorithms. Detailed neuronal multicompartmental models with active or passive compartments are commonly used in this objective. Although using such realistic NEURON models could lead to realistic extracellular potentials, it may require a high computational burden making the simulation of large populations difficult without a workstation. We propose in this paper a novel method to simulate extracellular potentials of firing neurons, taking into account the NEURON geometry and the relative positions of the electrodes. The simulator takes the form of a linear geometry based filter that models the shape of an action potential by taking into account its generation in the cell body / axon hillock and its propagation along the axon. The validity of the approach for different NEURON morphologies is assessed. We demonstrate that our method is able to reproduce realistic extracellular action potentials in a given range of axon/dendrites surface ratio, with a time-efficient computational burden.

The generation and propagation of the human alpha rhythm

The alpha rhythm is the longest-studied brain oscillation and has been theorized to play a key role in cognition. Still, its physiology is poorly understood. In this study, we used microelectrodes and macroelectrodes in surgical epilepsy patients to measure the intracortical and thalamic generators of the alpha rhythm during quiet wakefulness. We first found that alpha in both visual and somatosensory cortex propagates from higher-order to lower-order areas. In posterior cortex, alpha propagates from higher-order anterosuperior areas toward the occipital pole, whereas alpha in somatosensory cortex propagates from associative regions toward primary cortex. Several analyses suggest that this cortical alpha leads pulvinar alpha, complicating prevailing theories of a thalamic pacemaker. Finally, alpha is dominated by currents and firing in supragranular cortical layers. Together, these results suggest that the alpha rhythm likely reflects short-range supragranular feedback, which propagates from higher- to lower-order cortex and cortex to thalamus. These physiological insights suggest how alpha could mediate feedback throughout the thalamocortical system.

A Novel µECoG Electrode Interface for Comparison of Local and Common Averaged Referenced Signals

Micro-electrocorticography (µECoG) is a minimally invasive neural interface that allows for recording from the surface of the brain with high spatial and temporal resolution [1], [2]. However, discerning multi-unit and local field potential (LFP) activity with potentially highly-correlated signals across a dense µECoG array can be challenging. Here we describe a novel µECoG design to compare the effect of referencing recordings to a local reference electrode and common average referencing (CAR). The filtering effect and the significant increase in signal to noise ratio of the evoked response (ESNR) can be seen after re-referencing for both types of referencing. In a preliminary analysis, re-referencing the µECoG signals can increase recording performance at high contact densities in the auditory cortex. This also provides promising evidence for a versatile in-house fabricated µECoG electrode.

Origins of the Resting-State Functional MRI Signal: Potential Limitations of the "Neurocentric" Model

Resting-state functional connectivity (rsFC) is emerging as a research tool for systems and clinical neuroscience. The mechanism underlying resting-state functional MRI (rsfMRI) signal, however, remains incompletely understood. A widely held assumption is that the spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signal reflect ongoing neuronal processes (herein called “neurocentric” model). In support of this model, evidence from human and animal studies collectively reveals that the spatial synchrony of spontaneously occurring electrophysiological signal recapitulates BOLD rsFC networks. Two recent experiments from independent labs designed to specifically examine neuronal origins of rsFC, however, suggest that spontaneously occurring neuronal events, as assessed by multiunit activity or local field potential (LFP), although statistically significant, explain only a small portion (∼10%) of variance in resting-state BOLD fluctuations. These two studies, although each with its own limitations, suggest that the spontaneous fluctuations in rsfMRI, may have complex cellular origins, and the “neurocentric” model may not apply to all brain regions.

Approaches to closed-loop deep brain stimulation for movement disorders

OBJECTIVE Deep brain stimulation (DBS) is a safe and effective therapy for movement disorders, such as Parkinson's disease (PD), essential tremor (ET), and dystonia. There is considerable interest in developing "closed-loop" DBS devices capable of modulating stimulation in response to sensor feedback. In this paper, the authors review related literature and present selected approaches to signal sources and approaches to feedback being considered for deployment in closed-loop systems. METHODS A literature search using the keywords "closed-loop DBS" and "adaptive DBS" was performed in the PubMed database. The search was conducted for all articles published up until March 2018. An in-depth review was not performed for publications not written in the English language, nonhuman studies, or topics other than Parkinson's disease or essential tremor, specifically epilepsy and psychiatric conditions. RESULTS The search returned 256 articles. A total of 71 articles were primary studies in humans, of which 50 focused on treatment of movement disorders. These articles were reviewed with the aim of providing an overview of the features of closed-loop systems, with particular attention paid to signal sources and biomarkers, general approaches to feedback control, and clinical data when available. CONCLUSIONS Closed-loop DBS seeks to employ biomarkers, derived from sensors such as electromyography, electrocorticography, and local field potentials, to provide real-time, patient-responsive therapy for movement disorders. Most studies appear to focus on the treatment of Parkinson's disease. Several approaches hold promise, but additional studies are required to determine which approaches are feasible, efficacious, and efficient.

Electrophysiological and Neurochemical Considerations of Distinct Neuronal Populations in the Rat Pedunculopontine Nucleus and Their Responsiveness Following 6-Hydroxydopamine Lesions

The pedunculopontine nucleus (PPN) is composed of a morphologically and neurochemically heterogeneous population of neurons, which is severely affected by Parkinson’s disease (PD). However, the role of each subtype of neurons within the PPN in the pathophysiology of PD has not been completely elucidated. In this study, we present the discharge profiles of three classified subtypes of PPN neurons and their alterations after 6-hydroxydopamine (6-OHDA) lesion. Following 6-OHDA lesion, the spike timing of the Type II (GABAergic) and Type III (glutamatergic) neurons had phase-lock with the oscillations in the delta and beta band frequency range in the PPN, respectively. Morphological evidence has shown distinct alteration in three kinds of neurons after 6-OHDA lesion. These findings revealed that the changes in the firing characteristics of neurons in PPN in hemi-parkinsonism rats are closely associated with damaged neuronal morphology, which would make contributions to the divergence of dysfunctions in Parkinsonism.