High γ power in ECoG reflects cortical electrical stimulation effects on unit activity in layers V/VI

Flexible modeling of large-scale neural network stimulation: electrical and optical extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX)

Background

Computational models that predict effects of neural stimulation can be used as a preliminary tool to inform in-vivo research, reducing the costs, time, and ethical considerations involved. However, current models do not support the diverse neural stimulation techniques used in-vivo, including the expanding selection of electrodes, stimulation modalities, and stimulation paradigms.

New Method

To develop a more comprehensive software, we created several extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX), the MATLAB-based neural stimulation tool from Newcastle University. VERTEX simulates input currents in a large population of multi-compartment neurons within a small cortical slice to model electric field stimulation, while recording local field potentials (LFPs) and spiking activity. Our extensions to its existing electric field stimulation framework include allowing multiple pairs of parametrically defined electrodes and biphasic, bipolar stimulation delivered at programmable delays. To support the growing use of optogenetic approaches for targeted neural stimulation, we introduced a feature that models optogenetic stimulation through an additional VERTEX input function that converts irradiance to currents at optogenetically responsive neurons. Finally, we added extensions to allow complex stimulation protocols including paired-pulse, spatiotemporal patterned, and closed-loop stimulation.

Results

We demonstrated our novel features using VERTEX's built-in functionalities, with results in alignment with other models and experimental work.

Conclusions

Our extensions provide an all in one platform to efficiently and systematically test diverse, targeted, and individualized stimulation patterns.

Data processing techniques impact quantification of cortico-cortical evoked potentials

BACKGROUND

Cortico-cortical evoked potentials (CCEPs) are a common tool for probing effective connectivity in intracranial human electrophysiology. As with all human electrophysiology data, CCEP data are highly susceptible to noise. To address noise, filters and re-referencing are often applied to CCEP data, but different processing strategies are used from study to study.

NEW METHOD

We systematically compare how common average re-referencing and filtering CCEP data impacts quantification.

RESULTS

We show that common average re-referencing and filters, particularly filters that cut out more frequencies, can significantly impact the quantification of CCEP magnitude and morphology. We identify that high cutoff high pass filters (> 0.5 Hz), low cutoff low pass filters (< 200 Hz), and common average re-referencing impact quantification across subjects. However, we also demonstrate that the presence of noise may impact CCEP quantification, and preprocessing is necessary to mitigate this. We show that filtering is more effective than re-referencing or averaging across trials for reducing most common types of noise.

COMPARISON WITH EXISTING METHODS

These results suggest that existing CCEP processing methods must be applied with care to maximize noise reduction and minimize changes to the data. We do not test every available processing strategy; rather we demonstrate that processing can influence the results of CCEP studies. We emphasize the importance of reporting all processing methods, particularly re-referencing methods.

CONCLUSIONS

We propose a general framework for choosing an appropriate processing pipeline for CCEP data, taking into consideration the noise levels of a specific dataset. We suggest that minimal gentle filtering is preferable.

Is There a Difference in EEG Characteristics in Acute, Chronic, and Experimentally Induced Musculoskeletal Pain States? a Systematic Review

Electroencephalographic (EEG) alterations have been demonstrated in acute, chronic, and experimentally induced musculoskeletal (MSK) pain conditions. However, there is no cumulative evidence on the associated EEG characteristics differentiating acute, chronic, and experimentally induced musculoskeletal pain states, especially compared to healthy controls. The present systematic review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines (PRISMA) to review and summarize available evidence for cortical brain activity and connectivity alterations in acute, chronic, and experimentally induced MSK pain states. Five electronic databases were systematically searched from their inception to 2022. A total of 3471 articles were screened, and 26 full articles (five studies on chronic pain and 21 studies on experimentally induced pain) were included for the final synthesis. Using the Downs and Black risk of assessment tool, 92% of the studies were assessed as low to moderate quality. The review identified a 'very low' level of evidence for the changes in EEG and subjective outcome measures for both chronic and experimentally induced MSK pain based on the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria. Overall, the findings of this review indicate a trend toward decreased alpha and beta EEG power in evoked chronic clinical pain conditions and increased theta and alpha power in resting-state EEG recorded from chronic MSK pain conditions. EEG characteristics are unclear under experimentally induced pain conditions.

Early Intervention with Electrical Stimulation Reduces Neural Damage After Stroke in Non-human Primates

Ischemic stroke is a neurological condition that results in significant mortality and long-term disability for adults, creating huge health burdens worldwide. For stroke patients, acute intervention offers the most critical therapeutic opportunity as it can reduce irreversible tissue injury and improve functional outcomes. However, currently available treatments within the acute window are highly limited. Although emerging neuromodulation therapies have been tested for chronic stroke patients, acute stimulation is rarely studied due to the risk of causing adverse effects related to ischemia-induced electrical instability. To address this gap, we combined electrophysiology and histology tools to investigate the effects of acute electrical stimulation on ischemic neural damage in non-human primates. Specifically, we induced photothrombotic lesions in the monkey sensorimotor cortex while collecting electrocorticography (ECoG) signals through a customized neural interface. Gamma activity in ECoG was used as an electrophysiological marker to track the effects of stimulation on neural activation. Meanwhile, histological analysis including Nissl, cFos, and microglial staining was performed to evaluate the tissue response to ischemic injury. Comparing stimulated monkeys to controls, we found that theta-burst stimulation administered directly adjacent to the ischemic infarct at 1 hour post-stroke briefly inhibits peri-infarct neuronal activation as reflected by decreased ECoG gamma power and cFos expression. Meanwhile, lower microglial activation and smaller lesion volumes were observed in animals receiving post-stroke stimulation. Together, these results suggest that acute electrical stimulation can be used safely and effectively as an early stroke intervention to reduce excitotoxicity and inflammation, thus mitigating neural damage and enhancing stroke outcomes.

Prefrontal PV interneurons facilitate attention and are linked to attentional dysfunction in a mouse model of absence epilepsy

Absence seizures are characterized by brief periods of unconsciousness accompanied by lapses in motor function that can occur hundreds of times throughout the day. Outside of these frequent moments of unconsciousness, approximately a third of people living with the disorder experience treatment-resistant attention impairments. Convergent evidence suggests prefrontal cortex (PFC) dysfunction may underlie attention impairments in affected patients. To examine this, we use a combination of slice physiology, fiber photometry, electrocorticography (ECoG), optogenetics, and behavior in the Scn8a+/-mouse model of absence epilepsy. Attention function was measured using a novel visual attention task where a light cue that varied in duration predicted the location of a food reward. In Scn8a+/-mice, we find altered parvalbumin interneuron (PVIN) output in the medial PFC (mPFC) in vitro and PVIN hypoactivity along with reductions in gamma power during cue presentation in vivo. This was associated with poorer attention performance in Scn8a+/-mice that could be rescued by gamma-frequency optogenetic stimulation of PVINs. This highlights cue-related PVIN activity as an important mechanism for attention and suggests PVINs may represent a therapeutic target for cognitive comorbidities in absence epilepsy.

Neuroprotective Effects of Electrical Stimulation Following Ischemic Stroke in Non-Human Primates

Brain stimulation has emerged as a novel therapy for ischemic stroke, a major cause of brain injury that often results in lifelong disability. Although past works in rodents have demonstrated protective effects of stimulation following stroke, few of these results have been replicated in humans due to the anatomical differences between rodent and human brains and a limited understanding of stimulation-induced network changes. Therefore, we combined electrophysiology and histology to study the neuroprotective mechanisms of electrical stimulation following cortical ischemic stroke in non-human primates. To produce controlled focal lesions, we used the photothrombotic method to induce targeted vasculature damage in the sensorimotor cortices of two macaques while collecting electrocorticography (ECoG) signals bilaterally. In another two monkeys, we followed the same lesioning procedures and applied repeated electrical stimulation via an ECoG electrode adjacent to the lesion. We studied the protective effects of stimulation on neural dynamics using ECoG signal power and coherence. In addition, we performed histological analysis to evaluate the differences in lesion volume. In comparison to controls, the ECoG signals showed decreased gamma power across the sensorimotor cortices in stimulated animals. Meanwhile, Nissl staining revealed smaller lesion volumes for the stimulated group, suggesting that electrical stimulation may exert neuroprotection by suppressing post-ischemic neural activity. With the similarity between NHP and human brains, this study paves the path for developing effective stimulation-based therapy for acute stroke in clinical studies.

Multi-scale neural decoding and analysis

Objective.

Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment.

Approach.

We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes.

Main results.

We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches.

Significance.

This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

Gamma rhythms and visual information in mouse V1 specifically modulated by somatostatin+ neurons in reticular thalamus

Visual perception in natural environments depends on the ability to focus on salient stimuli while ignoring distractions. This kind of selective visual attention is associated with gamma activity in the visual cortex. While the nucleus reticularis thalami (nRT) has been implicated in selective attention, its role in modulating gamma activity in the visual cortex remains unknown. Here, we show that somatostatin- (SST) but not parvalbumin-expressing (PV) neurons in the visual sector of the nRT preferentially project to the dorsal lateral geniculate nucleus (dLGN), and modulate visual information transmission and gamma activity in primary visual cortex (V1). These findings pinpoint the SST neurons in nRT as powerful modulators of the visual information encoding accuracy in V1 and represent a novel circuit through which the nRT can influence representation of visual information.

Phase relationship between micro-electrocorticography and cortical neurons

OBJECTIVE

Electrocorticography (ECoG) is commonly used to map epileptic foci and to implement brain-computer interfaces. Understanding the spatiotemporal correspondence between potentials recorded from the brain's surface and the firing patterns of neurons within the cortex would inform the interpretation of ECoG signals and the design of (microfabricated) micro-ECoG electrode arrays. Based on the theory that synaptic potentials generated by neurons firing in synchrony superimpose to generate local field potentials (LFPs), we hypothesized that neurons in the cortex would fire at preferential phases of the micro-ECoG signal in a spatially dependent way.

APPROACH

We custom fabricated micro-ECoG electrode arrays with a small opening for silicon arrays (NeuroNexus) to be inserted into the cortex.

MAIN RESULTS

We found that the spectral coherence between micro-ECoG signals and intracortical LFPs decreased with distance and frequency, but the coherence with spiking units did not simply decrease over distance, likely due to the structure of the cortex. The majority of sorted units spiked during a preferred phase (usually downward) and frequency (usually below 20 Hz) of the micro-ECoG signal. Their preferred frequency decreased with administration of dexmeditomidine, a sedative commonly used for cortical mapping in patients with epilepsy prior to surgical resection. Dexmedetomidine concomitantly shifted the micro-ECoG spectral density towards lower frequencies. Therefore, the phase relationship between micro-ECoG signals and cortical spiking depends on the state of the brain, and spectrum shifts towards lower frequencies in the electrocorticography signal are a signature of increased spike-phase coupling. However, spike-phase coupling is not a static property since visual stimuli were found to modulate the magnitude of phase coupling at gamma frequency ranges (30-80 Hz), providing empirical evidence that neurons transiently phase-lock.

SIGNIFICANCE

The phase relationship between intracortical spikes and micro-ECoG signals depends on brain state, site separation, cortical structure, and external stimuli.

Prolonged post-stimulation response induced by 980-nm infrared neural stimulation in the rat primary motor cortex

The post-stimulation response of neural activities plays an important role to evaluate the effectiveness and safety of neural modulation techniques. Previous studies have established the capability of infrared neural modulation (INM) on neural firing regulation in the central nervous system (CNS); however, the dynamic neural activity after the laser offset has not been well characterized yet. We applied 980-nm infrared diode laser light to irradiate the primary motor cortex of rats, and tungsten electrode was inserted to record the single-unit activity of neurons at the depth of 800-1000 μm (layer V of primary motor cortex). The neural activities were assessed through the change of neural firing rate and firing pattern pre- and post-stimulation with various radiant exposures. The results showed that the 980-nm laser could modulate the firing properties of neurons in the deep layer of the cortex. More neurons with post-stimulation response (78% vs. 83%) were observed at higher stimulation intensity (0.803 J/cm² vs. 1.071 J/cm², respectively). The change of firing rate also increased with radiant exposures increasing, and the response lasted up to 4.5 s at 1.071 J/cm², which was significantly longer than the theoretical thermal relaxation time. Moreover, the increasing Fano factors indicated the irregularity firing pattern of post-stimulation response. Our results confirmed that neural activity maintained a prolonged post-stimulation response after INM, which may provide necessary measurable data for optimization of INM applications in CNS.

The Potential for a Speech Brain-Computer Interface Using Chronic Electrocorticography

A brain-computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current state-of-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail.

Targeted cortical reorganization using optogenetics in non-human primates

Brain stimulation modulates the excitability of neural circuits and drives neuroplasticity. While the local effects of stimulation have been an active area of investigation, the effects on large-scale networks remain largely unexplored. We studied stimulation-induced changes in network dynamics in two macaques. A large-scale optogenetic interface enabled simultaneous stimulation of excitatory neurons and electrocorticographic recording across primary somatosensory (S1) and motor (M1) cortex (Yazdan-Shahmorad et al., 2016). We tracked two measures of network connectivity, the network response to focal stimulation and the baseline coherence between pairs of electrodes; these were strongly correlated before stimulation. Within minutes, stimulation in S1 or M1 significantly strengthened the gross functional connectivity between these areas. At a finer scale, stimulation led to heterogeneous connectivity changes across the network. These changes reflected the correlations introduced by stimulation-evoked activity, consistent with Hebbian plasticity models. This work extends Hebbian plasticity models to large-scale circuits, with significant implications for stimulation-based neurorehabilitation.

From riding a bike to reaching for a cup of coffee, all skilled actions rely on precise connections between the sensory and motor areas of the brain. While sensory areas receive and analyse input from the senses, motor areas plan and trigger muscle contractions. Precisely adjusting the connections between these and other areas enables us to learn new skills, and it also helps us to relearn skills lost as a result of brain injury or stroke.

About 70 years ago, a psychologist named Donald Hebb came up with an idea for how this process might occur. He proposed that whenever two neurons are active at the same time, the connection between them becomes stronger. This idea, that ‘cells that fire together, wire together’, became known as Hebb’s rule. Many studies have since shown that Hebb’s rule can explain changes in the strength of connections between pairs of neurons. But can it also explain how connections between entire brain regions become stronger or weaker?

New results show that it can. The data were obtained using a technique called optogenetics, in which viruses are used to introduce genes for light-sensitive proteins into neurons. Shining light onto the brain will then activate any cells within that area that contain the resulting proteins. Yazdan-Shahmorad, Silversmith et al. used this technique to activate small regions of either sensory or motor brain tissue in live macaque monkeys. Doing so strengthened the overall connectivity between the two areas. The effects were more variable at the level of smaller brain regions, with some connections becoming weaker rather than stronger. However, Yazdan-Shahmorad, Silversmith et al. show that Hebb’s rule explains most of the observed changes.

Many neurological and psychiatric disorders stem from abnormal brain connectivity. Simple forms of brain stimulation are already used to treat certain neurological disorders, such as Parkinson’s disease. Stimulating the brain to induce specific changes in connectivity may ultimately enable us to leverage the brain’s natural learning mechanisms to cure, instead of just treat, these conditions.

Multimodal Encoding of Novelty, Reward, and Learning in the Primate Nucleus Basalis of Meynert

Associative learning is crucial for daily function, involving a complex network of brain regions. One region, the nucleus basalis of Meynert (NBM), is a highly interconnected, largely cholinergic structure implicated in multiple aspects of learning. We show that single neurons in the NBM of nonhuman primates (NHPs; n = 2 males; Macaca mulatta) encode learning a new association through spike rate modulation. However, the power of low-frequency local field potential (LFP) oscillations decreases in response to novel, not-yet-learned stimuli but then increase as learning progresses. Both NBM and the dorsolateral prefrontal cortex encode confidence in novel associations by increasing low- and high-frequency LFP power in anticipation of expected rewards. Finally, NBM high-frequency power dynamics are anticorrelated with spike rate modulations. Therefore, novelty, learning, and reward anticipation are separately encoded through differentiable NBM signals. By signaling both the need to learn and confidence in newly acquired associations, NBM may play a key role in coordinating cortical activity throughout the learning process.SIGNIFICANCE STATEMENT Degradation of cells in a key brain region, the nucleus basalis of Meynert (NBM), correlates with Alzheimer's disease and Parkinson's disease progression. To better understand the role of this brain structure in learning and memory, we examined neural activity in the NBM in behaving nonhuman primates while they performed a learning and memory task. We found that single neurons in NBM encoded both salience and an early learning, or cognitive state, whereas populations of neurons in the NBM and prefrontal cortex encode learned state and reward anticipation. The NBM may thus encode multiple stages of learning. These multimodal signals might be leveraged in future studies to develop neural stimulation to facilitate different stages of learning and memory.

Electrical Neural Stimulation and Simultaneous in Vivo Monitoring with Transparent Graphene Electrode Arrays Implanted in GCaMP6f Mice

Electrical stimulation using implantable electrodes is widely used to treat various neuronal disorders such as Parkinson's disease and epilepsy and is a widely used research tool in neuroscience studies. However, to date, devices that help better understand the mechanisms of electrical stimulation in neural tissues have been limited to opaque neural electrodes. Imaging spatiotemporal neural responses to electrical stimulation with minimal artifact could allow for various studies that are impossible with existing opaque electrodes. Here, we demonstrate electrical brain stimulation and simultaneous optical monitoring of the underlying neural tissues using carbon-based, fully transparent graphene electrodes implanted in GCaMP6f mice. Fluorescence imaging of neural activity for varying electrical stimulation parameters was conducted with minimal image artifact through transparent graphene electrodes. In addition, full-field imaging of electrical stimulation verified more efficient neural activation with cathode leading stimulation compared to anode leading stimulation. We have characterized the charge density limitation of capacitive four-layer graphene electrodes as 116.07-174.10 μC/cm² based on electrochemical impedance spectroscopy, cyclic voltammetry, failure bench testing, and in vivo testing. This study demonstrates the transparent ability of graphene neural electrodes and provides a method to further increase understanding and potentially improve therapeutic electrical stimulation in the central and peripheral nervous systems.

Strategies for optical control and simultaneous electrical readout of extended cortical circuits

BACKGROUND

To dissect the intricate workings of neural circuits, it is essential to gain precise control over subsets of neurons while retaining the ability to monitor larger-scale circuit dynamics. This requires the ability to both evoke and record neural activity simultaneously with high spatial and temporal resolution.

NEW METHOD

In this paper we present approaches that address this need by combining micro-electrocorticography (μECoG) with optogenetics in ways that avoid photovoltaic artifacts.

RESULTS

We demonstrate that variations of this approach are broadly applicable across three commonly studied mammalian species - mouse, rat, and macaque monkey - and that the recorded μECoG signal shows complex spectral and spatio-temporal patterns in response to optical stimulation.

COMPARISON WITH EXISTING METHODS

While optogenetics provides the ability to excite or inhibit neural subpopulations in a targeted fashion, large-scale recording of resulting neural activity remains challenging. Recent advances in optical physiology, such as genetically encoded Ca(2+) indicators, are promising but currently do not allow simultaneous recordings from extended cortical areas due to limitations in optical imaging hardware.

CONCLUSIONS

We demonstrate techniques for the large-scale simultaneous interrogation of cortical circuits in three commonly used mammalian species.

Closed-Loop Optogenetic Brain Interface

This paper presents a new approach for implementation of closed-loop brain-machine interface algorithms by combining optogenetic neural stimulation with electrocorticography and fluorescence microscopy. We used a new generation of microfabricated electrocorticography (micro-ECoG) devices in which electrode arrays are embedded within an optically transparent biocompatible substrate that provides optical access to the brain tissue during electrophysiology recording. An optical setup was designed capable of projecting arbitrary patterns of light for optogenetic stimulation and performing fluorescence microscopy through the implant. For realization of a closed-loop system using this platform, the feedback can be taken from electrophysiology data or fluorescence imaging. In the closed-loop systems discussed in this paper, the feedback signal was taken from the micro-ECoG. In these algorithms, the electrophysiology data are continuously transferred to a computer and compared with some predefined spatial-temporal patterns of neural activity. The computer which processes the data also readjusts the duration and distribution of optogenetic stimulating pulses to minimize the difference between the recorded activity and the predefined set points so that after a limited period of transient response the recorded activity follows the set points. Details of the system design and implementation of typical closed-loop paradigms are discussed in this paper.

Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex

The relatively low invasiveness of electrocorticography (ECoG) has made it a promising candidate for the development of practical, high-performance neural prosthetics. Recent ECoG-based studies have shown success in decoding hand and finger movements and muscle activity in reaching and grasping tasks. However, decoding of force profiles is still lacking. Here, we demonstrate that lateral grasp force profile can be decoded using a sparse linear regression from 15 and 16 channel ECoG signals recorded from sensorimotor cortex in two non-human primates. The best average correlation coefficients of prediction after 10-fold cross validation were 0.82±0.09 and 0.79±0.15 for our monkeys A and B, respectively. These results show that grasp force profile was successfully decoded from ECoG signals in reaching and grasping tasks and may potentially contribute to the development of more natural control methods for grasping in neural prosthetics.