Recording extracellular neural activity in the behaving monkey using a semichronic and high-density electrode system

Investigation of a chondroitin sulfate-based bioactive coating for neural interface applications

Invasive neural implants allow for high-resolution bidirectional communication with the nervous tissue and have demonstrated the ability to record neural activity, stimulate neurons, and sense neurochemical species with high spatial selectivity and resolution. However, upon implantation, they are exposed to a foreign body response which can disrupt the seamless integration of the device with the native tissue and lead to deterioration in device functionality for chronic implantation. Modifying the device surface by incorporating bioactive coatings has been a promising approach to camouflage the device and improve integration while maintaining device performance. In this work, we explored the novel application of a chondroitin sulfate (CS) based hydrophilic coating, with anti-fouling and neurite-growth promoting properties for neural recording electrodes. CS-coated samples exhibited significantly reduced protein-fouling in vitro which was maintained for up to 4-weeks. Cell culture studies revealed a significant increase in neurite attachment and outgrowth and a significant decrease in microglia attachment and activation for the CS group as compared to the control. After 1-week of in vivo implantation in the mouse cortex, the coated probes demonstrated significantly lower biofouling as compared to uncoated controls. Like the in vitro results, increased neuronal population (neuronal nuclei and neurofilament) and decreased microglial activation were observed. To assess the coating's effect on the recording performance of silicon microelectrodes, we implanted coated and uncoated electrodes in the mouse striatum for 1 week and performed impedance and recording measurements. We observed significantly lower impedance in the coated group, likely due to the increased wettability of the coated surface. The peak-to-peak amplitude and the noise floor levels were both lower in the CS group compared to the controls, which led to a comparable signal-to-noise ratio between the two groups. The overall single unit yield (% channels recording a single unit) was 74% for the CS and 67% for the control group on day 1. Taken together, this study demonstrates the effectiveness of the polysaccharide-based coating in reducing biofouling and improving biocompatibility for neural electrode devices.

A Second Introduction to the Neurobiology of Interval Timing

Time is a critical variable that organisms must be able to measure in order to survive in a constantly changing environment. Initially, this paper describes the myriad of contexts where time is estimated or predicted and suggests that timing is not a single process and probably depends on a set of different neural mechanisms. Consistent with this hypothesis, the explosion of neurophysiological and imaging studies in the last 10 years suggests that different brain circuits and neural mechanisms are involved in the ability to tell and use time to control behavior across contexts. Then, we develop a conceptual framework that defines time as a family of different phenomena and propose a taxonomy with sensory, perceptual, motor, and sensorimotor timing as the pillars of temporal processing in the range of hundreds of milliseconds.

Diverse Time Encoding Strategies Within the Medial Premotor Areas of the Primate

The measurement of time in the subsecond scale is critical for many sophisticated behaviors, yet its neural underpinnings are largely unknown. Recent neurophysiological experiments from our laboratory have shown that the neural activity in the medial premotor areas (MPC) of macaques can represent different aspects of temporal processing. During single interval categorization, we found that preSMA encodes a subjective category limit by reaching a peak of activity at a time that divides the set of test intervals into short and long. We also observed neural signals associated with the category selected by the subjects and the reward outcomes of the perceptual decision. On the other hand, we have studied the behavioral and neurophysiological basis of rhythmic timing. First, we have shown in different tapping tasks that macaques are able to produce predictively and accurately intervals that are cued by auditory or visual metronomes or when intervals are produced internally without sensory guidance. In addition, we found that the rhythmic timing mechanism in MPC is governed by different layers of neural clocks. Next, the instantaneous activity of single cells shows ramping activity that encodes the elapsed or remaining time for a tapping movement. In addition, we found MPC neurons that build neural sequences, forming dynamic patterns of activation that flexibly cover all the produced interval depending on the tapping tempo. This rhythmic neural clock resets on every interval providing an internal representation of pulse. Furthermore, the MPC cells show mixed selectivity, encoding not only elapsed time, but also the tempo of the tapping and the serial order element in the rhythmic sequence. Hence, MPC can map different task parameters, including the passage of time, using different cell populations. Finally, the projection of the time varying activity of MPC hundreds of cells into a low dimensional state space showed circular neural trajectories whose geometry represented the internal pulse and the tapping tempo. Overall, these findings support the notion that MPC is part of the core timing mechanism for both single interval and rhythmic timing, using neural clocks with different encoding principles, probably to flexibly encode and mix the timing representation with other task parameters.

Amodal population clock in the primate medial premotor system for rhythmic tapping

The neural substrate for beat extraction and response entrainment to rhythms is not fully understood. Here we analyze the activity of medial premotor neurons in monkeys performing isochronous tapping guided by brief flashing stimuli or auditory tones. The population dynamics shared the following properties across modalities: the circular dynamics of the neural trajectories form a regenerating loop for every produced interval; the trajectories converge in similar state space at tapping times resetting the clock; and the tempo of the synchronized tapping is encoded in the trajectories by a combination of amplitude modulation and temporal scaling. Notably, the modality induces displacement in the neural trajectories in the auditory and visual subspaces without greatly altering the time-keeping mechanism. These results suggest that the interaction between the medial premotor cortex's amodal internal representation of pulse and a modality-specific external input generates a neural rhythmic clock whose dynamics govern rhythmic tapping execution across senses.

Cortical Pyramidal and Parvalbumin Cells Exhibit Distinct Spatiotemporal Extracellular Electric Potentials

Brain circuits are composed of diverse cell types with distinct morphologies, connections, and distributions of ion channels. Modeling suggests that the spatial distribution of the extracellular voltage during a spike depends on cellular morphology, connectivity, and identity. However, experimental evidence from the intact brain is lacking. Here, we combined high-density recordings from hippocampal region CA1 and neocortex of freely moving mice with optogenetic tagging of parvalbumin-immunoreactive (PV) cells. We used ground truth tagging of the recorded pyramidal cells (PYR) and PV cells to construct binary classification models. Features derived from single-channel waveforms or from spike timing alone allowed near-perfect classification of PYR and PV cells. To determine whether there is unique information in the spatial distribution of the extracellular potentials, we removed all single-channel waveform information from the multichannel waveforms using an event-based delta-transformation. We found that spatiotemporal features derived from the transformed waveforms yield accurate classification. The extracellular analog of the spatial distribution of the initial depolarization phase provided the highest contribution to the spatially based prediction. Compared with PV cell spikes, PYR spikes exhibited higher spatial synchrony at the beginning of the extracellular spike and lower synchrony at the trough. The successful classification of PYR and PV cells based on purely spatial features provides direct experimental evidence that spikes of distinct cell types are associated with distinct spatial distributions of extracellular potentials.

Mapping between sound, brain and behaviour: four-level framework for understanding rhythm processing in humans and non-human primates

Humans perceive and spontaneously move to one or several levels of periodic pulses (a meter, for short) when listening to musical rhythm, even when the sensory input does not provide prominent periodic cues to their temporal location. Here, we review a multi-levelled framework to understanding how external rhythmic inputs are mapped onto internally represented metric pulses. This mapping is studied using an approach to quantify and directly compare representations of metric pulses in signals corresponding to sensory inputs, neural activity and behaviour (typically body movement). Based on this approach, recent empirical evidence can be drawn together into a conceptual framework that unpacks the phenomenon of meter into four levels. Each level highlights specific functional processes that critically enable and shape the mapping from sensory input to internal meter. We discuss the nature, constraints and neural substrates of these processes, starting with fundamental mechanisms investigated in macaque monkeys that enable basic forms of mapping between simple rhythmic stimuli and internally represented metric pulse. We propose that human evolution has gradually built a robust and flexible system upon these fundamental processes, allowing more complex levels of mapping to emerge in musical behaviours. This approach opens promising avenues to understand the many facets of rhythmic behaviours across individuals and species. This article is part of the theme issue 'Synchrony and rhythm interaction: from the brain to behavioural ecology'.

Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform

Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species.

PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface

Accurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers a robust, efficient and generalizable pipeline for the automatic standardization of MRI surface analysis on NHP.

Deviance detection in physiologically identified cell types in the rat auditory cortex

Auditory deviance detection is a function of the auditory system that allows reduction of the processing demand for repetitive stimuli while stressing unpredictable ones, which are potentially more informative. Deviance detection has been extensively studied in humans using the oddball paradigm, which evokes an event-related potential known as mismatch negativity (MMN). The same stimulation paradigms are used in animal studies that aim to elucidate the neuronal mechanisms underlying deviance detection. In order to understand the circuitry responsible for deviance detection in the auditory cortex (AC), it is necessary to determine the properties of excitatory and inhibitory neurons separately. Measuring the spike widths of neurons recorded extracellularly from the anaesthetized rat AC, we classified them as fast spiking or regular spiking units. These two neuron types are generally considered as putative inhibitory or excitatory, respectively. In response to an oddball paradigm, we found that both types of units showed similar amounts of deviance detection overall. When considering each AC field separately, we found that only in A1 fast spiking neurons showed higher deviance detection levels than regular spiking neurons, while in the rest of the fields there was no such distinction. Interpreting these responses in the context of the predictive coding framework, we found that the responses of both types of units reflect mainly prediction error signaling (i.e., genuine deviance detection) rather than repetition suppression.

Localization of movable electrodes in a multi-electrode microdrive in nonhuman primates

BACKGROUND

Recently, large-scale semi-chronic recording systems have been developed, unique in their capability to record simultaneously from multiple individually moveable electrodes. As these recording systems can cover a large area, knowledge of the exact location of each individual electrode is crucial. Currently, the only method of keeping track of electrode depth and thus location is through detailed notebook keeping on neural activity.

NEW METHOD

We have improved the electrode localization by combining pre- and postoperative anatomical magnetic resonance imaging (MRI) scans with high resolution computed tomography (CT) scans throughout the experiment, and validated our method by comparing the resulting location estimates with traditional notebook-keeping. Finally, the actual location of a selection of electrodes was marked at the end of the experiment by creating small metallic depositions using electrical stimulation, and thereby made visible on MRI.

RESULTS

Combining CT scans with a high resolution, artefact reducing sequence during the experiment with a preoperative MRI scan provides crucial information about the exact electrode location of multielectrode arrays with individually moveable electrodes.

COMPARISON WITH EXISTING METHODS

The information obtained from the hybrid CT-MR image and the notes on spiking activity showed a similar pattern, with the clear advantage of the visualization of the exact position of the electrodes using our method.

CONCLUSIONS

The described technique allows for a precise anatomical identification of the recorded brain areas and thus to draw strong conclusions about the role of each targeted cortical area in the behavior under study.

Electrophysiological monitoring of inhibition in mammalian species, from rodents to humans

GABAergic interneurons constitute a highly diverse family of neurons that play a critical role in cortical functions. Due to their prominent role in cortical network dynamics, genetic, developmental, or other dysfunctions in GABAergic neurons have been linked to neurological disorders such as epilepsy. Thus, it is crucial to investigate the interaction of these various neurons and to develop methods to specifically and directly monitor inhibitory activity in vivo. While research in small mammals has benefited from a wealth of recent technological development, bridging the gap to large mammals and humans remains a challenge. This is of particular interest since single neuron monitoring with intracranial electrodes in epileptic patients is developing quickly, opening new avenues for understanding the role of different cell types in epilepsy. Here, we review currently available techniques that monitor inhibitory activity in the brain and the respective validations in rodents. Finally, we discuss the future developments of these techniques and how knowledge from animal research can be translated to the study of neuronal circuit dynamics in the human brain.

The amplitude in periodic neural state trajectories underlies the tempo of rhythmic tapping

Our motor commands can be exquisitely timed according to the demands of the environment, and the ability to generate rhythms of different tempos is a hallmark of musical cognition. Yet, the neuronal underpinnings behind rhythmic tapping remain elusive. Here, we found that the activity of hundreds of primate medial premotor cortices (MPCs; pre-supplementary motor area [preSMA] and supplementary motor area [SMA]) neurons show a strong periodic pattern that becomes evident when their responses are projected into a state space using dimensionality reduction analysis. We show that different tapping tempos are encoded by circular trajectories that travelled at a constant speed but with different radii, and that this neuronal code is highly resilient to the number of participating neurons. Crucially, the changes in the amplitude of the oscillatory dynamics in neuronal state space are a signature of duration encoding during rhythmic timing, regardless of whether it is guided by an external metronome or is internally controlled and is not the result of repetitive motor commands. This dynamic state signal predicted the duration of the rhythmically produced intervals on a trial-by-trial basis. Furthermore, the increase in variability of the neural trajectories accounted for the scalar property, a hallmark feature of temporal processing across tasks and species. Finally, we found that the interval-dependent increments in the radius of periodic neural trajectories are the result of a larger number of neurons engaged in the production of longer intervals. Our results support the notion that rhythmic timing during tapping behaviors is encoded in the radial curvature of periodic MPC neural population trajectories.

The scalar property during isochronous tapping is disrupted by a D2-like agonist in the nonhuman primate

Dopamine, and specifically the D2 system, has been implicated in timing tasks where the absolute duration of individual time intervals is encoded discretely, yet the role of D2 during beat perception and entrainment remains largely unknown. In this type of timing, a beat is perceived as the pulse that marks equally spaced points in time and, once extracted, produces the tendency in humans to entrain or synchronize their movements to it. Hence, beat-based timing is crucial for musical execution. In this study we investigated the effects of systemic injections of quinpirole (0.005–0.05 mg/kg), a D2-like agonist, on the isochronous rhythmic tapping of rhesus monkeys, a classical task for the study of beat entrainment. We compared the rhythmic timing accuracy, precision, and the asynchronies of the monkeys with or without the effects of quinpirole, as well as their reaction times in a control serial reaction time task (SRTT). The results showed a dose-dependent disruption in the scalar property of rhythmic timing due to quinpirole administration. Specifically, we found similar temporal variabilities as a function of the metronome tempo at the largest dose, instead of the increase in variability across durations that is characteristic of the timing Weber law. Notably, these effects were not due to alterations in the basic sensorimotor mechanism for tapping to a sequence of flashing stimuli, because quinpirole did not change the reaction time of the monkeys during SRTT. These findings support the notion of a key role of the D2 system in the rhythmic timing mechanism, especially in the control of temporal precision.

NEW & NOTEWORTHY Perceiving and moving to the beat of music is a fundamental trait of musical cognition. We measured the effect of quinpirole, a D2-like agonist, on the precision and accuracy of rhythmic tapping to a metronome in two rhesus monkeys. Quinpirole produced a flattening of the temporal variability as a function of tempo duration, instead of the increase in variability across durations that is characteristic of the scalar property, a hallmark property of timing.

Optimizing the Yield of Multi-Unit Activity by Including the Entire Spiking Activity

Neurophysiological data acquisition using multi-electrode arrays and/or (semi-) chronic recordings frequently has to deal with low signal-to-noise ratio (SNR) of neuronal responses and potential failure of detecting evoked responses within random background fluctuations. Conventional methods to extract action potentials (spikes) from background noise often apply thresholds to the recorded signal, usually allowing reliable detection of spikes when data exhibit a good SNR, but often failing when SNR is poor. We here investigate a threshold-independent, fast, and automated procedure for analysis of low SNR data, based on fullwave-rectification and low-pass filtering the signal as a measure of the entire spiking activity (ESA). We investigate the sensitivity and reliability of the ESA-signal for detecting evoked responses by applying an automated receptive field (RF) mapping procedure to semi-chronically recorded data from primary visual cortex (V1) of five macaque monkeys. For recording sites with low SNR, the usage of ESA improved the detection rate of RFs by a factor of 2.5 in comparison to MUA-based detection. For recording sites with medium and high SNR, ESA delivered 30% more RFs than MUA. This significantly higher yield of ESA-based RF-detection still hold true when using an iterative procedure for determining the optimal spike threshold for each MUA individually. Moreover, selectivity measures for ESA-based RFs were quite compatible with MUA-based RFs. Regarding RF size, ESA delivered larger RFs than thresholded MUA, but size difference was consistent over all SNR fractions. Regarding orientation selectivity, ESA delivered more sites with significant orientation-dependent responses but with somewhat lower orientation indexes than MUA. However, preferred orientations were similar for both signal types. The results suggest that ESA is a powerful signal for applications requiring automated, fast, and reliable response detection, as e.g., brain-computer interfaces and neuroprosthetics, due to its high sensitivity and its independence from user-dependent intervention. Because the full information of the spiking activity is preserved, ESA also constitutes a valuable alternative for offline analysis of data with limited SNR.

Reusable Multielectrode Array Technique for Electroencephalography in Awake Freely Moving Mice

Translational comparison of rodent models of neurological and neuropsychiatric diseases to human electroencephalography (EEG) biomarkers in these conditions will require multisite rodent EEG on the skull surface, rather than local area electrocorticography (ECoG) or multisite local field potential (LFP) recording. We have developed a technique for planar multielectrode array (MEA) implantation on the mouse skull surface, which enables multisite EEG in awake and freely moving mice and reusability of the MEA probes. With this method, we reliably obtain 30-channel low-noise EEG from awake mice. Baseline and stimulus-evoked EEG recordings can be readily obtained and analyzed. For example, we have demonstrated EEG responses to auditory stimuli. Broadband noise elicits reliable 30-channel auditory event-related potentials (ERPs), and chirp stimuli induce phase-locked EEG responses just as in human sound presentation paradigms. This method is unique in achieving chronic implantation of novel MEA technology onto the mouse skull surface for chronic multisite EEG recordings. Furthermore, we demonstrate a reliable method for reusing MEA probes for multiple serial implantations without loss of EEG quality. This skull surface MEA methodology can be used to obtain simultaneous multisite EEG recordings and to test EEG biomarkers in diverse mouse models of human neurological and neuropsychiatric diseases. Reusability of the MEA probes makes it more cost-effective to deploy this system for various studies.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.