Direct Electrical Stimulation in Electrocorticographic Brain-Computer Interfaces: Enabling Technologies for Input to Cortex

The "state of the art" of intraoperative neurophysiological monitoring: An Italian neurosurgical survey

Introduction

Intraoperative Neurophysiological Monitoring (IOM) is widely used in neurosurgery but specific guidelines are lacking. Therefore, we can assume differences in IOM application between Neurosurgical centers.

Research question

The section of Functional Neurosurgery of the Italian Society of Neurosurgery realized a survey aiming to obtain general data on the current practice of IOM in Italy.

Materials and methods

A 22-item questionnaire was designed focusing on: volume procedures, indications, awake surgery, experience, organization and equipe. The questionnaire has been sent to Italian Neurosurgery centers.

Results

A total of 54 centers completed the survey. The annual volume of surgeries range from 300 to 2000, and IOM is used in 10-20% of the procedures. In 46% of the cases is a neurologist or a neurophysiologist who performs IOM. For supra-tentorial pathology, almost all perform MEPs (94%) SSEPs (89%), direct cortical stimulation (85%). All centers perform IOM in spinal surgery and 95% in posterior fossa surgery. Among the 50% that perform peripheral nerve surgery, all use IOM. Awake surgery is performed by 70% of centers. The neurosurgeon is the only responsible for IOM in 35% of centers. In 83% of cases IOM implementation is adequate to the request.

Discussion and conclusions

The Italian Neurosurgical centers perform IOM with high level of specialization, but differences exist in organization, techniques, and expertise. Our survey provides a snapshot of the state of the art in Italy and it could be a starting point to implement a consensus on the practice of IOM.

Nanoporous graphene-based thin-film microelectrodes for in vivo high-resolution neural recording and stimulation

One of the critical factors determining the performance of neural interfaces is the electrode material used to establish electrical communication with the neural tissue, which needs to meet strict electrical, electrochemical, mechanical, biological and microfabrication compatibility requirements. This work presents a nanoporous graphene-based thin-film technology and its engineering to form flexible neural interfaces. The developed technology allows the fabrication of small microelectrodes (25 µm diameter) while achieving low impedance (∼25 kΩ) and high charge injection (3-5 mC cm-2). In vivo brain recording performance assessed in rodents reveals high-fidelity recordings (signal-to-noise ratio >10 dB for local field potentials), while stimulation performance assessed with an intrafascicular implant demonstrates low current thresholds (<100 µA) and high selectivity (>0.8) for activating subsets of axons within the rat sciatic nerve innervating tibialis anterior and plantar interosseous muscles. Furthermore, the tissue biocompatibility of the devices was validated by chronic epicortical (12 week) and intraneural (8 week) implantation. This work describes a graphene-based thin-film microelectrode technology and demonstrates its potential for high-precision and high-resolution neural interfacing.

Optogenetic Inhibition of Glutamatergic Neurons in the Dysgranular Posterior Insular Cortex Modulates Trigeminal Neuropathic Pain in CCI-ION Rat

In individuals with chronic neuropathic pain, the posterior insular cortex (PIC) has been found to exhibit increased glutamatergic activity, and the dysgranular portion of PIC (DPIC) has been investigated as a novel cortical target for pain modulation. However, the role of DPIC glutamatergic neurons (DPICg) in trigeminal neuropathic pain (TNP) remains unclear. Here, we examined the outcomes of DPICg inhibition in a rat model of chronic constriction injury of the infraorbital nerve (CCI-ION). Animals were randomly divided into TNP, sham, and control groups. TNP animals underwent CCI-ION surgery. Either optogenetic or null viruses were delivered to the contralateral DPICg of TNP and sham animals. In vivo single-unit extracellular recordings from the ipsilateral spinal trigeminal nucleus caudalis (TNC) and contralateral ventral posteromedial (VPM) thalamus were obtained under both "ON" and "OFF" stimulation states. Behavioral responses during the stimulation-OFF and stimulation-ON phases were examined. Expression of c-Fos, pERK, and CREB immunopositive neurons were also observed. Optogenetic inhibition of contralateral DPICg decreased the neural firing rate in both TNC and VPM thalamus, the expression of sensory-responsive cell bodies, and transcriptional factors in the DPIC of TNP group. Improvements in hyperalgesia, allodynia, and anxiety-like responses in TNP animals were also observed during stimulation-ON condition. In fine, descending pain processing is influenced by neuroanatomical projections from the DPIC to the pain matrix areas, and DPICg could play a necessary role in this neural circuitry. Therefore, the antinociceptive effect of DPICg inhibition in this study may provide evidence for the therapeutic potential of DPICg in TNP.

Direct laser writing of 3D electrodes on flexible substrates

This report describes a 3D microelectrode array integrated on a thin-film flexible cable for neural recording in small animals. The fabrication process combines traditional silicon thin-film processing techniques and direct laser writing of 3D structures at micron resolution via two-photon lithography. Direct laser-writing of 3D-printed electrodes has been described before, but this report is the first to provide a method for producing high-aspect-ratio structures. One prototype, a 16-channel array with 300 µm pitch, demonstrates successful electrophysiological signal capture from bird and mouse brains. Additional devices include 90 µm pitch arrays, biomimetic mosquito needles that penetrate through the dura of birds, and porous electrodes with enhanced surface area. The rapid 3D printing and wafer-scale methods described here will enable efficient device fabrication and new studies examining the relationship between electrode geometry and electrode performance. Applications include small animal models, nerve interfaces, retinal implants, and other devices requiring compact, high-density 3D electrodes.

Neurotechnology and international security: Predicting commercial and military adoption of brain-computer interfaces (BCIs) in the United States and China

In the past decade, international actors have launched "brain projects" or "brain initiatives." One of the emerging technologies enabled by these publicly funded programs is brain-computer interfaces (BCIs), which are devices that allow communication between the brain and external devices like a prosthetic arm or a keyboard. BCIs are poised to have significant impacts on public health, society, and national security. This research presents the first analytical framework that attempts to predict the dissemination of neurotechnologies to both the commercial and military sectors in the United States and China. While China started its project later with less funding, we find that it has other advantages that make earlier adoption more likely. We also articulate national security risks implicit in later adoption, including the inability to set international ethical and legal norms for BCI use, especially in wartime operating environments, and data privacy risks for citizens who use technology developed by foreign actors.

Nine decades of electrocorticography: A comparison between epidural and subdural recordings

In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.

Neural Bypasses: Literature Review and Future Directions in Developing Artificial Neural Connections

Reported neuro-modulation schemes in the literature are typically classified as closed-loop or open-loop. A novel group of recently developed neuro-modulation devices may be better described as a neural bypass, which attempts to transmit neural data from one location of the nervous system to another. The most common form of neural bypasses in the literature utilize EEG recordings of cortical information paired with functional electrical stimulation for effector muscle output, most commonly for assistive applications and rehabilitation in spinal cord injury or stroke. Other neural bypass locations that have also been described, or may soon be in development, include cortical-spinal bypasses, cortical-cortical bypasses, autonomic bypasses, peripheral-central bypasses, and inter-subject bypasses. The most common recording devices include EEG, ECoG, and microelectrode arrays, while stimulation devices include both invasive and noninvasive electrodes. Several devices are in development to improve the temporal and spatial resolution and biocompatibility for neuronal recording and stimulation. A major barrier to entry includes neuroplasticity and current decoding mechanisms that regularly require retraining. Neural bypasses are a unique class of neuro-modulation. Continued advancement of neural recording and stimulating devices with high spatial and temporal resolution, combined with decoding mechanisms uninhibited by neuroplasticity, can expand the therapeutic capability of neural bypassing. Overall, neural bypasses are a promising modality to improve the treatment of common neurologic disorders, including stroke, spinal cord injury, peripheral nerve injury, brain injury and more.

Innovations in Neuropsychology: Future Applications in Neurosurgical Patient Care

Over the last century, collaboration between clinical neuropsychologists and neurosurgeons has advanced the state of the science in both disciplines. These advances have provided the field of neuropsychology with many opportunities for innovation in the care of patients prior to, during, and following neurosurgical intervention. Beyond giving a general overview of how present-day advances in technology are being applied in the practice of neuropsychology within a neurological surgery department, this article outlines new developments that are currently unfolding. Improvements in remote platform, computer interface, "real-time" analytics, mobile devices, and immersive virtual reality have the capacity to increase the customization, precision, and accessibility of neuropsychological services. In doing so, such innovations have the potential to improve outcomes and ameliorate health care disparities.

Modulation of trigeminal neuropathic pain by optogenetic inhibition of posterior hypothalamus in CCI-ION rat

Posterior hypothalamus (PH), an important part of the descending pain processing pathway, has been found to be activated in trigeminal autonomic cephalalgias. However, there are very few studies conducted and information regarding its implications in trigeminal neuropathic pain (TNP). Therefore, we aimed to ascertain whether optogenetic inhibition of PH could affect the outcomes of a chronic constriction injury in the infraorbital nerve (CCI-ION) rat model. Animals were divided into the TNP animal, sham, and naive-control groups. CCI-ION surgery was performed to mimic TNP symptoms, and the optogenetic or null virus was injected into the ipsilateral PH. In vivo single-unit extracellular recordings were obtained from both the ipsilateral ventrolateral periaqueductal gray (vlPAG) and contralateral ventral posteromedial (VPM) thalamus in stimulation "OFF" and "ON" conditions. Alterations in behavioral responses during the stimulation-OFF and stimulation-ON states were examined. We observed that optogenetic inhibition of the PH considerably improved behavioral responses in TNP animals. We found increased and decreased firing activity in the vlPAG and VPM thalamus, respectively, during optogenetic inhibition of the PH. Inhibiting PH attenuates trigeminal pain signal transmission by modulating the vlPAG and trigeminal nucleus caudalis, thereby providing evidence of the therapeutic potential of PH in TNP management.

Recent developments in multifunctional neural probes for simultaneous neural recording and modulation

Neural probes are among the most widely applied tools for studying neural circuit functions and treating neurological disorders. Given the complexity of the nervous system, it is highly desirable to monitor and modulate neural activities simultaneously at the cellular scale. In this review, we provide an overview of recent developments in multifunctional neural probes that allow simultaneous neural activity recording and modulation through different modalities, including chemical, electrical, and optical stimulation. We will focus on the material and structural design of multifunctional neural probes and their interfaces with neural tissues. Finally, future challenges and prospects of multifunctional neural probes will be discussed.

A New Nonlinear Autoregressive Exogenous (NARX)-based Intra-spinal Stimulation Approach to Decode Brain Electrical Activity for Restoration of Leg Movement in Spinally-injured Rabbits

Introduction

This study aimed at investigating the stimulation by intra-spinal signals decoded from electrocorticography (ECoG) assessments to restore the movements of the leg in an animal model of spinal cord injury (SCI).

Methods

The present work is comprised of three steps. First, ECoG signals and the associated leg joint changes (hip, knee, and ankle) in sedated healthy rabbits were recorded in different trials. Second, an appropriate set of intra-spinal electric stimuli was discovered to restore natural leg movements, using the three leg joint movements under a fuzzy-controlled strategy in spinally-injured rabbits under anesthesia. Third, a nonlinear autoregressive exogenous (NARX) neural network model was developed to produce appropriate intra-spinal stimulation developed from decoded ECoG information. The model was able to correlate the ECoG signal data to the intra-spinal stimulation data and finally, induced desired leg movements. In this study, leg movements were also developed from offline ECoG signals (deciphered from rabbits that were not injured) as well as online ECoG data (extracted from the same rabbit after SCI induction).

Results

Based on our data, the correlation coefficient was 0.74±0.15 and the normalized root means square error of the brain-spine interface was 0.22±0.10.

Conclusion

Overall, we found that using NARX, appropriate information from ECoG recordings can be extracted and used for the generation of proper intra-spinal electric stimulations for restoration of natural leg movements lost due to SCI.

Restored vision-augmented vision: arguments for a cybernetic vision

In this paper, we present some thoughts about the recent developments, made possible by technological advances and miniaturisation of connected visual prostheses, linked to the visual system, operating at different level of this one, on the retina as well as in the visual cortex. While these objects represent a great hope for people with impaired vision to recover partial vision, we show how this technology could also act on the functional vision of well sighted persons to improve or increase their visual performance. In addition to the impact on our cognitive and attentional mechanisms, such an operation when it originates outside the natural real visual field (e.g. cybernetics) raises a number of questions about the development and use of such implants or prostheses in the future.

Deep brain-machine interfaces: sensing and modulating the human deep brain

Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.

An Immersive Virtual Reality Platform Integrating Human ECOG & sEEG: Implementation & Noise Analysis

Virtual reality (VR) offers a robust platform for human behavioral neuroscience, granting unprecedented experimental control over every aspect of an immersive and interactive visual environment. VR experiments have already integrated non-invasive neural recording modalities such as EEG and functional MRI to explore the neural correlates of human behavior and cognition. Integration with implanted electrodes would enable significant increase in spatial and temporal resolution of recorded neural signals and the option of direct brain stimulation for neurofeedback. In this paper, we discuss the first such implementation of a VR platform with implanted electrocorticography (ECoG) and stereo-electroencephalography ( sEEG) electrodes in human, in-patient subjects. Noise analyses were performed to evaluate the effect of the VR headset on neural data collected in two VR-naive subjects, one child and one adult, including both ECOG and sEEG electrodes. Results demonstrate an increase in line noise power (57-63Hz) while wearing the VR headset that is mitigated effectively by common average referencing (CAR), and no significant change in the noise floor bandpower (125-240Hz). To our knowledge, this study represents first demonstrations of VR immersion during invasive neural recording with in-patient human subjects. Clinical Relevance- Immersive virtual reality tasks were well-tolerated and the quality of clinical neural signals preserved during VR immersion with two in-patient invasive neural recording subjects.

Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience

OBJECTIVE

To provide a design analysis and guidance framework for the implementation of concurrent stimulation and sensing during adaptive deep brain stimulation (aDBS) with particular emphasis on artifact mitigations.

APPROACH

We defined a general architecture of feedback-enabled devices, identified key components in the signal chain which might result in unwanted artifacts and proposed methods that might ultimately enable improved aDBS therapies. We gathered data from research subjects chronically-implanted with an investigational aDBS system, Summit RC+S, to characterize and explore artifact mitigations arising from concurrent stimulation and sensing. We then used a prototype investigational implantable device, DyNeuMo, and a bench-setup that accounts for tissue-electrode properties, to confirm our observations and verify mitigations. The strategies to reduce transient stimulation artifacts and improve performance during aDBS were confirmed in a chronic implant using updated configuration settings.

MAIN RESULTS

We derived and validated a "checklist" of configuration settings to improve system performance and areas for future device improvement. Key considerations for the configuration include 1) active instead of passive recharge, 2) sense-channel blanking in the amplifier, 3) high-pass filter settings, 4) tissue-electrode impedance mismatch management, 5) time-frequency trade-offs in the classifier, 6) algorithm blanking and transition rate limits. Without proper channel configuration, the aDBS algorithm was susceptible to limit-cycles of oscillating stimulation independent of physiological state. By applying the checklist, we could optimize each block's performance characteristics within the overall system. With system-level optimization, a 'fast' aDBS prototype algorithm was demonstrated to be feasible without reentrant loops, and with noise performance suitable for subcortical brain circuits.

SIGNIFICANCE

We present a framework to study sources and propose mitigations of artifacts in devices that provide chronic aDBS. This work highlights the trade-offs in performance as novel sensing devices translate to the clinic. Finding the appropriate balance of constraints is imperative for successful translation of aDBS therapies.

Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters

BACKGROUND

Electrical neuromodulation via direct electrical stimulation (DES) is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, likely due to our limited understanding of the relationship between the massive stimulation parameter space and brain tissue responses.

OBJECTIVE

To better understand how different parameters induce varied neural responses, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration, brain region, and whether grey or white matter was stimulated.

METHODS

We measured voltage peak amplitudes and area under the curve (AUC) of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N = 52, n = 719 stimulation sites).

RESULTS

Increasing stimulation pulse width increased responses near the stimulation location. Increasing stimulation amplitude (current) increased both evoked amplitudes and AUC nonlinearly. Locally (<15 mm), stimulation at the boundary between grey and white matter induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. The stimulation location-response curves followed different trends for cingulate, lateral frontal, and lateral temporal cortical stimulation.

CONCLUSION

These results demonstrate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter could be needed for network activation. Thus, stimulation parameters tailored for a specific anatomical-functional outcome may be key to advancing neuromodulatory therapy.

Brain-Computer Interface: Applications to Speech Decoding and Synthesis to Augment Communication

Damage or degeneration of motor pathways necessary for speech and other movements, as in brainstem strokes or amyotrophic lateral sclerosis (ALS), can interfere with efficient communication without affecting brain structures responsible for language or cognition. In the worst-case scenario, this can result in the locked in syndrome (LIS), a condition in which individuals cannot initiate communication and can only express themselves by answering yes/no questions with eye blinks or other rudimentary movements. Existing augmentative and alternative communication (AAC) devices that rely on eye tracking can improve the quality of life for people with this condition, but brain-computer interfaces (BCIs) are also increasingly being investigated as AAC devices, particularly when eye tracking is too slow or unreliable. Moreover, with recent and ongoing advances in machine learning and neural recording technologies, BCIs may offer the only means to go beyond cursor control and text generation on a computer, to allow real-time synthesis of speech, which would arguably offer the most efficient and expressive channel for communication. The potential for BCI speech synthesis has only recently been realized because of seminal studies of the neuroanatomical and neurophysiological underpinnings of speech production using intracranial electrocorticographic (ECoG) recordings in patients undergoing epilepsy surgery. These studies have shown that cortical areas responsible for vocalization and articulation are distributed over a large area of ventral sensorimotor cortex, and that it is possible to decode speech and reconstruct its acoustics from ECoG if these areas are recorded with sufficiently dense and comprehensive electrode arrays. In this article, we review these advances, including the latest neural decoding strategies that range from deep learning models to the direct concatenation of speech units. We also discuss state-of-the-art vocoders that are integral in constructing natural-sounding audio waveforms for speech BCIs. Finally, this review outlines some of the challenges ahead in directly synthesizing speech for patients with LIS.

CyberEye: New Eye-Tracking Interfaces for Assessment and Modulation of Cognitive Functions beyond the Brain

The emergence of innovative neurotechnologies in global brain projects has accelerated research and clinical applications of BCIs beyond sensory and motor functions. Both invasive and noninvasive sensors are developed to interface with cognitive functions engaged in thinking, communication, or remembering. The detection of eye movements by a camera offers a particularly attractive external sensor for computer interfaces to monitor, assess, and control these higher brain functions without acquiring signals from the brain. Features of gaze position and pupil dilation can be effectively used to track our attention in healthy mental processes, to enable interaction in disorders of consciousness, or to even predict memory performance in various brain diseases. In this perspective article, we propose the term ‘CyberEye’ to encompass emerging cognitive applications of eye-tracking interfaces for neuroscience research, clinical practice, and the biomedical industry. As CyberEye technologies continue to develop, we expect BCIs to become less dependent on brain activities, to be less invasive, and to thus be more applicable.

A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders

Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.

Decoding pain from brain activity

Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.

Advances in Neural Recording and Stimulation Integrated Circuits

In the past few decades, driven by the increasing demands in the biomedical field aiming to cure neurological diseases and improve the quality of daily lives of the patients, researchers began to take advantage of the semiconductor technology to develop miniaturized and power-efficient chips for implantable applications. The emergence of the integrated circuits for neural prosthesis improves the treatment process of epilepsy, hearing loss, retinal damage, and other neurological diseases, which brings benefits to many patients. However, considering the safety and accuracy in the neural prosthesis process, there are many research directions. In the process of chip design, designers need to carefully analyze various parameters, and investigate different design techniques. This article presents the advances in neural recording and stimulation integrated circuits, including (1) a brief introduction of the basics of neural prosthesis circuits and the repair process in the bionic neural link, (2) a systematic introduction of the basic architecture and the latest technology of neural recording and stimulation integrated circuits, (3) a summary of the key issues of neural recording and stimulation integrated circuits, and (4) a discussion about the considerations of neural recording and stimulation circuit architecture selection and a discussion of future trends. The overview would help the designers to understand the latest performances in many aspects and to meet the design requirements better.

Cognitive and Emotional Mapping With SEEG

Mapping of cortical functions is critical for the best clinical care of patients undergoing epilepsy and tumor surgery, but also to better understand human brain function and connectivity. The purpose of this review is to explore existing and potential means of mapping higher cortical functions, including stimulation mapping, passive mapping, and connectivity analyses. We examine the history of mapping, differences between subdural and stereoelectroencephalographic approaches, and some risks and safety aspects, before examining different types of functional mapping. Much of this review explores the prospects for new mapping approaches to better understand other components of language, memory, spatial skills, executive, and socio-emotional functions. We also touch on brain-machine interfaces, philosophical aspects of aligning tasks to brain circuits, and the study of consciousness. We end by discussing multi-modal testing and virtual reality approaches to mapping higher cortical functions.

Virtual Reality as a Technological-Aided Solution to Support Communication in Persons With Neurodegenerative Diseases and Acquired Brain Injury During COVID-19 Pandemic

The COVID-19 poses an ongoing threat to lives around the world and challenges the existing public health and medical service delivery. The lockdown or quarantine measures adopted to prevent the spread of COVID-19 has caused the interruption in ongoing care and access to medical care including to patients with existing neurological conditions. Besides the passivity, isolation, and withdrawal, patients with neurodegenerative diseases experience difficulties in communication due to a limited access to leisure opportunities and interaction with friends and relatives. The communication difficulties may exacerbate the burden on the caregivers. Therefore, assistive-technologies may be a useful strategy in mitigating challenges associated with remote communication. The current paper presents an overview of the use of assistive technologies using virtual reality and virtual body ownership in providing communication opportunities to isolated patients, during COVID-19, with neurological diseases and moderate-to-severe communication difficulties. We postulate that the assistive technologies-based intervention may improve social interactions in patients with neurodegenerative diseases and acquired brain injury-thereby reducing isolation and improving their quality of life and mental well-being.

Toward Closed-Loop Electrical Stimulation of Neuronal Systems: A Review

Biological neuronal cells communicate using neurochemistry and electrical signals. The same phenomena also allow us to probe and manipulate neuronal systems and communicate with them. Neuronal system malfunctions cause a multitude of symptoms and functional deficiencies that can be assessed and sometimes alleviated by electrical stimulation. Our working hypothesis is that real-time closed-loop full-duplex measurement and stimulation paradigms can provide more in-depth insight into neuronal networks and enhance our capability to control diseases of the nervous system. In this study, we review extracellular electrical stimulation methods used in in vivo, in vitro, and in silico neuroscience research and in the clinic (excluding methods mainly aimed at neuronal growth and other similar effects) and highlight the potential of closed-loop measurement and stimulation systems. A multitude of electrical stimulation and measurement-based methods are widely used in research and the clinic. Closed-loop methods have been proposed, and some are used in the clinic. However, closed-loop systems utilizing more complex measurement analysis and adaptive stimulation systems, such as artificial intelligence systems connected to biological neuronal systems, do not yet exist. Our review promotes the research and development of intelligent paradigms aimed at meaningful communications between neuronal and information and communications technology systems, "dialogical paradigms," which have the potential to take neuroscience and clinical methods to a new level.

CLoSES: A platform for closed-loop intracranial stimulation in humans

Targeted interrogation of brain networks through invasive brain stimulation has become an increasingly important research tool as well as therapeutic modality. The majority of work with this emerging capability has been focused on open-loop approaches. Closed-loop techniques, however, could improve neuromodulatory therapies and research investigations by optimizing stimulation approaches using neurally informed, personalized targets. Implementing closed-loop systems is challenging particularly with regard to applying consistent strategies considering inter-individual variability. In particular, during intracranial epilepsy monitoring, where much of this research is currently progressing, electrodes are implanted exclusively for clinical reasons. Thus, detection and stimulation sites must be participant- and task-specific. The system must run in parallel with clinical systems, integrate seamlessly with existing setups, and ensure safety features are in place. In other words, a robust, yet flexible platform is required to perform different tests with a single participant and to comply with clinical requirements. In order to investigate closed-loop stimulation for research and therapeutic use, we developed a Closed-Loop System for Electrical Stimulation (CLoSES) that computes neural features which are then used in a decision algorithm to trigger stimulation in near real-time. To summarize CLoSES, intracranial electroencephalography (iEEG) signals are acquired, band-pass filtered, and local and network features are continuously computed. If target features are detected (e.g. above a preset threshold for a certain duration), stimulation is triggered. Not only could the system trigger stimulation while detecting real-time neural features, but we incorporated a pipeline wherein we used an encoder/decoder model to estimate a hidden cognitive state from the neural features. CLoSES provides a flexible platform to implement a variety of closed-loop experimental paradigms in humans. CLoSES has been successfully used with twelve patients implanted with depth electrodes in the epilepsy monitoring unit. During cognitive tasks (N=5), stimulation in closed loop modified a cognitive hidden state on a trial by trial basis. Sleep spindle oscillations (N=6) and sharp transient epileptic activity (N=9) were detected in near real-time, and stimulation was applied during the event or at specified delays (N=3). In addition, we measured the capabilities of the CLoSES system. Total latency was related to the characteristics of the event being detected, with tens of milliseconds for epileptic activity and hundreds of milliseconds for spindle detection. Stepwise latency, the actual duration of each continuous step, was within the specified fixed-step duration and increased linearly with the number of channels and features. We anticipate that probing neural dynamics and interaction between brain states and stimulation responses with CLoSES will lead to novel insights into the mechanism of normal and pathological brain activity, the discovery and evaluation of potential electrographic biomarkers of neurological and psychiatric disorders, and the development and testing of patient-specific stimulation targets and control signals before implanting a therapeutic device.

Models of communication and control for brain networks: distinctions, convergence, and future outlook

Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white-matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.

Application of a neural interface for restoration of leg movements: Intra-spinal stimulation using the brain electrical activity in spinally injured rabbits

This study aimed to design a neural interface that extracts movement commands from the brain to generate appropriate intra-spinal stimulation to restore leg movement. This study comprised four steps: (1) Recording electrocorticographic (ECoG) signals and corresponding leg movements in different trials. (2) Partial laminectomy to induce spinal cord injury (SCI) and detect motor modules in the spinal cord. (3) Delivering appropriate intra-spinal stimulation to the motor modules for restoration of the movements to those documented before SCI. (4) Development of a neural interface created by sparse linear regression (SLiR) model to detect movement commands transmitted from the brain to the modules. Correlation coefficient (CC) and normalized root mean square (NRMS) error was calculated to evaluate the neural interface effectiveness. It was found that by stimulating detected spinal cord modules, joint angle evaluated before SCI was not significantly different from that of post-SCI (P > 0.05). Based on results of SLiR model, overall CC and NRMS values were 0.63 ± 0.14 and 0.34 ± 0.16 (mean ± SD), respectively. These results indicated that ECoG data contained information about intra-spinal stimulations and the developed neural interface could produce intra-spinal stimulation based on ECoG data, for restoration of leg movements after SCI.

The combination of brain-computer interfaces and artificial intelligence: applications and challenges

Brain-computer interfaces (BCIs) have shown great prospects as real-time bidirectional links between living brains and actuators. Artificial intelligence (AI), which can advance the analysis and decoding of neural activity, has turbocharged the field of BCIs. Over the past decade, a wide range of BCI applications with AI assistance have emerged. These "smart" BCIs including motor and sensory BCIs have shown notable clinical success, improved the quality of paralyzed patients' lives, expanded the athletic ability of common people and accelerated the evolution of robots and neurophysiological discoveries. However, despite technological improvements, challenges remain with regard to the long training periods, real-time feedback, and monitoring of BCIs. In this article, the authors review the current state of AI as applied to BCIs and describe advances in BCI applications, their challenges and where they could be headed in the future.

A stacked sparse auto-encoder and back propagation network model for sensory event detection via a flexible ECoG

Current prostheses are limited in their ability to provide direct sensory feedback to users with missing limb. Several efforts have been made to restore tactile sensation to amputees but the somatotopic tactile feedback often results in unnatural sensations, and it is yet unclear how and what information the somatosensory system receives during voluntary movement. The present study proposes an efficient model of stacked sparse autoencoder and back propagation neural network for detecting sensory events from a highly flexible electrocorticography (ECoG) electrode. During the mechanical stimulation with Von Frey (VF) filament on the plantar surface of rats' foot, simultaneous recordings of tactile afferent signals were obtained from primary somatosensory cortex (S1) in the brain. In order to achieve a model with optimal performance, Particle Swarm Optimization and Adaptive Moment Estimation (Adam) were adopted to select the appropriate number of neurons, hidden layers and learning rate of each sparse auto-encoder. We evaluated the stimulus-evoked sensation by using an automated up-down (UD) method otherwise called UDReader. The assessment of tactile thresholds with VF shows that the right side of the hind-paw was significantly more sensitive at the tibia-(p = 6.50 × 10^-4), followed by the saphenous-(p = 7.84 × 10^-4), and sural-(p = 8.24 × 10^-4). We then validated our proposed model by comparing with the state-of-the-art methods, and recorded accuracy of 98.8%, sensitivity of 96.8%, and specificity of 99.1%. Hence, we demonstrated the effectiveness of our algorithms in detecting sensory events through flexible ECoG recordings which could be a viable option in restoring somatosensory feedback.

Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning

OBJECTIVE

Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available.

APPROACH

We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings.

MAIN RESULTS

We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation.

SIGNIFICANCE

Our work will enable future advances in neural engineering with simultaneous stimulation and recording.

Biomarkers and neuromodulation techniques in substance use disorders

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders.