Operant conditioning of specific patterns of neural and muscular activity

Applying the IEEE BRAIN neuroethics framework to intra-cortical brain-computer interfaces

Objective. Brain-computer interfaces (BCIs) are neuroprosthetic devices that allow for direct interaction between brains and machines. These types of neurotechnologies have recently experienced a strong drive in research and development, given, in part, that they promise to restore motor and communication abilities in individuals experiencing severe paralysis. While a rich literature analyzes the ethical, legal, and sociocultural implications (ELSCI) of these novel neurotechnologies, engineers, clinicians and BCI practitioners often do not have enough exposure to these topics.Approach. Here, we present the IEEE Neuroethics Framework, an international, multiyear, iterative initiative aimed at developing a robust, accessible set of considerations for diverse stakeholders.Main results. Using the framework, we provide practical examples of ELSCI considerations for BCI neurotechnologies. We focus on invasive technologies, and in particular, devices that are implanted intra-cortically for medical research applications.Significance. We demonstrate the utility of our framework in exposing a wide range of implications across different intra-cortical BCI technology modalities and conclude with recommendations on how to utilize this knowledge in the development and application of ethical guidelines for BCI neurotechnologies.

The Corticospinal System and Amyotrophic Lateral Sclerosis: IFCN handbook chapter

Corticospinal neurons located in motor areas of the cerebral neocortex project corticospinal axons which synapse with the spinal network; a parallel corticobulbar system projects to the cranial motor network and to brainstem motor pathways. The primate corticospinal system has a widespread cortical origin and an extensive range of different fibre diameters, including thick, fast-conducting axons. Direct cortico-motoneuronal (CM) projections from the motor cortex to arm and hand alpha motoneurons are a recent evolutionary feature, that is well developed in dexterous primates and particularly in humans. Many of these projections originate from the caudal subdivision of area 4 ('new' M1: primary motor cortex). They arise from corticospinal neurons of varied soma size, including those with fast- and relatively slow-conducting axons. This CM system has been shown to be involved in the control of skilled movements, carried out with fractionation of the distal extremities and at low force levels. During movement, corticospinal neurons are activated quite differently from 'lower' motoneurons, and there is no simple or fixed functional relationship between a so-called 'upper' motoneuron and its target lower motoneuron. There are key differences in the organisation and function of the corticospinal and CM system in primates versus non-primates, such as rodents. These differences need to be recognized when making the choice of animal model for understanding disorders such as amyotrophic lateral sclerosis (ALS). In this neurodegenerative brain disease there is a selective loss of fast-conducting corticospinal axons, and their synaptic connections, and this is reflected in responses to non-invasive cortical stimuli and measures of cortico-muscular coherence. The loss of CM connections influencing distal limb muscles results in a differential loss of muscle strength or 'split-hand' phenotype. Importantly, there is also a unique impairment in the coordination of skilled hand tasks that require fractionation of digit movement. Scores on validated tests of skilled hand function could be used to assess disease progression.

Automated system for training and assessing reaching and grasping behaviors in rodents

BACKGROUND

Reaching, grasping, and pulling behaviors are studied across species to investigate motor control and problem solving. String pulling is a distinct reaching and grasping behavior that is rapidly learned, requires bimanual coordination, is ethologically grounded, and has been applied across species and disease conditions.

NEW METHOD

Here we describe the PANDA system (Pulling And Neural Data Analysis), a hardware and software system that integrates a continuous string loop connected to a rotary encoder, feeder, microcontroller, high-speed camera, and analysis software for the assessment and training of reaching, grasping, and pulling behaviors and synchronization with neural data.

RESULTS

We demonstrate this system in rats implanted with electrodes in motor cortex and hippocampus and show how it can be used to assess relationships between reaching, pulling, and grasping movements and single-unit and local-field activity. Furthermore, we found that automating the shaping procedure significantly improved performance over manual training, with rats pulling > 100 m during a 15-minute session.

COMPARISON WITH EXISTING METHODS

String-pulling is typically shaped by tying food reward to the string and visually scoring behavior. The system described here automates training, streamlines video assessment with deep learning, and automatically segments reaching movements into distinct reach/pull phases. No system, to our knowledge, exists for the automated shaping and assessment of this behavior.

CONCLUSIONS

This system will be of general use to researchers investigating motor control, motivation, sensorimotor integration, and motor disorders such as Parkinson's disease and stroke.

Automated system for training and assessing string-pulling behaviors in rodents

String-pulling tasks have been used for centuries to study coordinated bimanual motor behavior and problem solving. String pulling is rapidly learned, ethologically grounded, and has been applied to many species and disease conditions. Typically, training of string-pulling behaviors is achieved through manual shaping and baiting. Furthermore, behavioral assessment of reaching, grasping, and pulling is often performed through labor intensive manual video scoring. No system, to our knowledge, currently exists for the automated shaping and assessment of string-pulling behaviors. Here we describe the PANDA system (Pulling And Neural Data Analysis), an inexpensive hardware and software system that utilizes a continuous string loop connected to a rotary encoder, feeder, microcontroller, high-speed camera, and analysis software for assessment and training of string-pulling behaviors and synchronization with neural recording data. We demonstrate this system in unimplanted rats and rats implanted with electrodes in motor cortex and hippocampus and show how the PANDA system can be used to assess relationships between paw movements and single-unit and local-field activity. We also found that automating the shaping procedure significantly improved overall performance, with rats regularly pulling >100 meters during a 15-minute session. In conclusion, the PANDA system will be of general use to researchers investigating motor control, motivation, and motor disorders such as Parkinson's disease, Huntington's disease, and stroke. It will also support the investigation of neural mechanisms involved in sensorimotor integration.

Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering

Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system.

Neurocognitive and motor-control challenges for the realization of bionic augmentation

Robotic fingers and arms that augment the motor abilities of non-disabled individuals are increasingly feasible yet face neurocognitive barriers and hurdles in efferent motor control.

Principles of human movement augmentation and the challenges in making it a reality

Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.

Closed-loop neuromodulation for studying spontaneous activity and causality

Having established that spontaneous brain activity follows meaningful coactivation patterns and correlates with behavior, researchers have turned their attention to understanding its function and behavioral significance. We suggest closed-loop neuromodulation as a neural perturbation tool uniquely well suited for this task. Closed-loop neuromodulation has primarily been viewed as an interventionist tool to teach subjects to directly control their own brain activity. We examine an alternative operant conditioning model of closed-loop neuromodulation which, through implicit feedback, can manipulate spontaneous activity at the network level, without violating the spontaneous or endogenous nature of the signal, thereby providing a direct test of network causality.

Real-Time Functional MRI in the Treatment of Mental Health Disorders

Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health.

Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Long-Term Myoelectric Training with Delayed Feedback in the Home Environment

Myoelectric prosthesis users typically do not receive immediate feedback from their device. They must be able to consistently produce distinct muscle activations in the absence of augmented feedback. In previous experiments, abstract decoding has provided real-time visual feedback for closed loop control. It is unclear if the performance in those experiments was due to short-term adaptation or motor learning. To test if similar performance could be reached without short-term adaptation, we trained participants with a delayed feedback paradigm. Feedback was delayed until after the ~1.5 s trial was completed. Three participants trained for five days in their home environments, completing a cumulative total of 4920 trials. Participants became highly accurate while receiving no real-time feedback of their control input. They were also able to retain performance gains across days. This strongly suggests that abstract decoding with delayed feedback facilitates motor learning, enabling four class control without immediate feedback.

The Cortical "Upper Motoneuron" in Health and Disease

Upper motoneurons (UMNs) in motor areas of the cerebral cortex influence spinal and cranial motor mechanisms through the corticospinal tract (CST) and through projections to brainstem motor pathways. The primate corticospinal system has a diverse cortical origin and a wide spectrum of fibre diameters, including large diameter fibres which are unique to humans and other large primates. Direct cortico-motoneuronal (CM) projections from the motor cortex to arm and hand motoneurons are a late evolutionary feature only present in dexterous primates and best developed in humans. CM projections are derived from a more restricted cortical territory ('new' M1, area 3a) and arise not only from corticospinal neurons with large, fast axons but also from those with relatively slow-conducting axons. During movement, corticospinal neurons are organised and recruited quite differently from 'lower' motoneurons. Accumulating evidence strongly implicates the corticospinal system in the early stages of ALS, with particular involvement of CM projections to distal limb muscles, but also to other muscle groups influenced by the CM system. There are important species differences in the organisation and function of the corticospinal system, and appropriate animal models are needed to understand disorders involving the human corticospinal system.

Neurofeedback Training of Gamma Oscillations in Monkey Primary Visual Cortex

In humans, neurofeedback (NFB) training has been used extensively and successfully to manipulate brain activity. Feedback signals were derived from EEG, fMRI, MEG, and intracranial recordings and modifications were obtained of the BOLD signal, of the power of oscillatory activity in distinct frequency bands and of single unit activity. The purpose of the present study was to examine whether neuronal activity could also be controlled by NFB in early sensory cortices whose activity is thought to be influenced mainly by sensory input rather than volitional control. We trained 2 macaque monkeys to enhance narrow band gamma oscillations in the primary visual cortex by providing them with an acoustic signal that reflected the power of gamma oscillations in a preselected band and rewarding increases of the feedback signal. Oscillations were assessed from local field potentials recorded with chronically implanted microelectrodes. Both monkeys succeeded to raise gamma activity in the absence of visual stimulation in the selected frequency band and at the site from which the NFB signal was derived. This suggests that top-down signals are not confined to just modulate stimulus induced responses but can actually drive or facilitate the gamma generating microcircuits even in a primary sensory area.

Brain-computer interfaces: Definitions and principles

Throughout life, the central nervous system (CNS) interacts with the world and with the body by activating muscles and excreting hormones. In contrast, brain-computer interfaces (BCIs) quantify CNS activity and translate it into new artificial outputs that replace, restore, enhance, supplement, or improve the natural CNS outputs. BCIs thereby modify the interactions between the CNS and the environment. Unlike the natural CNS outputs that come from spinal and brainstem motoneurons, BCI outputs come from brain signals that represent activity in other CNS areas, such as the sensorimotor cortex. If BCIs are to be useful for important communication and control tasks in real life, the CNS must control these brain signals nearly as reliably and accurately as it controls spinal motoneurons. To do this, they might, for example, need to incorporate software that mimics the function of the subcortical and spinal mechanisms that participate in normal movement control. The realization of high reliability and accuracy is perhaps the most difficult and critical challenge now facing BCI research and development. The ongoing adaptive modifications that maintain effective natural CNS outputs take place primarily in the CNS. The adaptive modifications that maintain effective BCI outputs can also take place in the BCI. This means that the BCI operation depends on the effective collaboration of two adaptive controllers, the CNS and the BCI. Realization of this second adaptive controller, the BCI, and management of its interactions with concurrent adaptations in the CNS comprise another complex and critical challenge for BCI development. BCIs can use different kinds of brain signals recorded in different ways from different brain areas. Decisions about which signals recorded in which ways from which brain areas should be selected for which applications are empirical questions that can only be properly answered by experiments. BCIs, like other communication and control technologies, often face artifacts that contaminate or imitate their chosen signals. Noninvasive BCIs (e.g., EEG- or fNIRS-based) need to take special care to avoid interpreting nonbrain signals (e.g., cranial EMG) as brain signals. This typically requires comprehensive topographical and spectral evaluations. In theory, the outputs of BCIs can select a goal or control a process. In the future, the most effective BCIs will probably be those that combine goal selection and process control so as to distribute control between the BCI and the application in a fashion suited to the current action. Through such distribution, BCIs may most effectively imitate natural CNS operation. The primary measure of BCI development is the extent to which BCI systems benefit people with neuromuscular disorders. Thus, BCI clinical evaluation, validation, and dissemination is a key step. It is at the same time a complex and difficult process that depends on multidisciplinary collaboration and management of the demanding requirements of clinical studies. Twenty-five years ago, BCI research was an esoteric endeavor pursued in only a few isolated laboratories. It is now a steadily growing field that engages many hundreds of scientists, engineers, and clinicians throughout the world in an increasingly interconnected community that is addressing the key issues and pursuing the high potential of BCI technology.

From Novel Technology to Novel Applications: Comment on "An Integrated Brain-Machine Interface Platform With Thousands of Channels" by Elon Musk and Neuralink

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Starting and stopping movement by the primate brain

We review the current knowledge about the part that motor cortex plays in the preparation and generation of movement, and we discuss the idea that corticospinal neurons, and particularly those with cortico-motoneuronal connections, act as ‘command’ neurons for skilled reach-to-grasp movements in the primate. We also review the increasing evidence that it is active during processes such as action observation and motor imagery. This leads to a discussion about how movement is inhibited and stopped, and the role in these for disfacilitation of the corticospinal output. We highlight the importance of the non-human primate as a model for the human motor system. Finally, we discuss the insights that recent research into the monkey motor system has provided for translational approaches to neurological diseases such as stroke, spinal injury and motor neuron disease.

Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex

Although animal studies provided significant insights in understanding the neural basis of learning and adaptation, they often cannot dissociate between different learning mechanisms due to the lack of verbal communication. To overcome this limitation, we examined the mechanisms of learning and its limits in a human intracortical brain-machine interface (BMI) paradigm. A tetraplegic participant controlled a 2D computer cursor by modulating single-neuron activity in the anterior intraparietal area (AIP). By perturbing the neuron-to-movement mapping, the participant learned to modulate the activity of the recorded neurons to solve the perturbations by adopting a target re-aiming strategy. However, when no cognitive strategies were adequate to produce correct responses, AIP failed to adapt to perturbations. These findings suggest that learning is constrained by the pre-existing neuronal structure, although it is possible that AIP needs more training time to learn to generate novel activity patterns when cognitive re-adaptation fails to solve the perturbations.

Concurrent control of a brain-computer interface and natural overt movements

OBJECTIVE

A primary control signal in brain-computer interfaces (BCIs) have been cortical signals related to movement. However, in cases where natural motor function remains, BCI control signals may interfere with other possibly simultaneous activity for useful ongoing movement. We sought to determine if the brain could learn to control both a BCI and concurrent overt movement execution in such cases.

APPROACH

We designed experiments where BCI and overt movements must be used concurrently and in coordination to achieve a 2D centre out control. Power in the 70-90 Hz band of human electrocorticography (ECoG) signals, was used to generate BCI control commands for vertical movement of the cursor. These signals were deliberately recorded from the same human cortical site that produced the strongest movement related activity associated with the concurrent overt finger movements required for the horizontal movement of the cursor.

MAIN RESULTS

We demonstrate that three subjects were able to perform the concurrent BCI task, controlling BCI and natural movements simultaneously and to a large extent independently. We conclude that the brain is capable of dissociating the original control signal dependency on movement, producing specific BCI control signals in the presence of motor related responses from the ongoing overt behaviour with which the BCI signal was initially correlated.

SIGNIFICANCE

We demonstrate a novel human brain-computer interface (BCI) which can be used to control movement concurrently and in coordination with movements of the natural limbs. This demonstrates the dissociation of cortical activity from the behaviour with which it was originally associated despite the ongoing behaviour and shows the feasibility of achieving simultaneous BCI control of devices with natural movements.

Progress in Neuroengineering for brain repair: New challenges and open issues

Background

In recent years, biomedical devices have proven to be able to target also different neurological disorders. Given the rapid ageing of the population and the increase of invalidating diseases affecting the central nervous system, there is a growing demand for biomedical devices of immediate clinical use. However, to reach useful therapeutic results, these tools need a multidisciplinary approach and a continuous dialogue between neuroscience and engineering, a field that is named neuroengineering. This is because it is fundamental to understand how to read and perturb the neural code in order to produce a significant clinical outcome.

Results

In this review, we first highlight the importance of developing novel neurotechnological devices for brain repair and the major challenges expected in the next years. We describe the different types of brain repair strategies being developed in basic and clinical research and provide a brief overview of recent advances in artificial intelligence that have the potential to improve the devices themselves. We conclude by providing our perspective on their implementation to humans and the ethical issues that can arise.

Conclusions

Neuroengineering approaches promise to be at the core of future developments for clinical applications in brain repair, where the boundary between biology and artificial intelligence will become increasingly less pronounced.

Parsing learning in networks using brain-machine interfaces

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies.

A Cognition-Related Neural Oscillation Pattern, Generated in the Prelimbic Cortex, Can Control Operant Learning in Rats

The prelimbic (PrL) cortex constitutes one of the highest levels of cortical hierarchy dedicated to the execution of adaptive behaviors. We have identified a specific local field potential (LFP) pattern generated in the PrL cortex and associated with cognition-related behaviors. We used this pattern to trigger the activation of a visual display on a touch screen as part of an operant conditioning task. Rats learned to increase the presentation rate of the selected θ to β-γ (θ/β-γ) transition pattern across training sessions. The selected LFP pattern appeared to coincide with a significant decrease in the firing of PrL pyramidal neurons and did not seem to propagate to other cortical or subcortical areas. An indication of the PrL cortex's cognitive nature is that the experimental disruption of this θ/β-γ transition pattern prevented the proper performance of the acquired task without affecting the generation of other motor responses. The use of this LFP pattern to trigger an operant task evoked only minor changes in its electrophysiological properties. Thus, the PrL cortex has the capability of generating an oscillatory pattern for dealing with environmental constraints. In addition, the selected θ/β-γ transition pattern could be a useful tool to activate the presentation of external cues or to modify the current circumstances.SIGNIFICANCE STATEMENT Brain-machine interfaces represent a solution for physically impaired people to communicate with external devices. We have identified a specific local field potential pattern generated in the prelimbic cortex and associated with goal-directed behaviors. We used the pattern to trigger the activation of a visual display on a touch screen as part of an operant conditioning task. Rats learned to increase the presentation rate of the selected field potential pattern across training. The selected pattern was not modified when used to activate the touch screen. Electrical stimulation of the recording site prevented the proper performance of the task. Our findings show that the prelimbic cortex can generate oscillatory patterns that rats can use to control their environment for achieving specific goals.

Spatial co-adaptation of cortical control columns in a micro-ECoG brain-computer interface

OBJECTIVE

Electrocorticography (ECoG) has been used for a range of applications including electrophysiological mapping, epilepsy monitoring, and more recently as a recording modality for brain-computer interfaces (BCIs). Studies that examine ECoG electrodes designed and implanted chronically solely for BCI applications remain limited. The present study explored how two key factors influence chronic, closed-loop ECoG BCI: (i) the effect of inter-electrode distance on BCI performance and (ii) the differences in neural adaptation and performance when fixed versus adaptive BCI decoding weights are used.

APPROACH

The amplitudes of epidural micro-ECoG signals between 75 and 105 Hz with 300 μm diameter electrodes were used for one-dimensional and two-dimensional BCI tasks. The effect of inter-electrode distance on BCI control was tested between 3 and 15 mm. Additionally, the performance and cortical modulation differences between constant, fixed decoding using a small subset of channels versus adaptive decoding weights using the entire array were explored.

MAIN RESULTS

Successful BCI control was possible with two electrodes separated by 9 and 15 mm. Performance decreased and the signals became more correlated when the electrodes were only 3 mm apart. BCI performance in a 2D BCI task improved significantly when using adaptive decoding weights (80%-90%) compared to using constant, fixed weights (50%-60%). Additionally, modulation increased for channels previously unavailable for BCI control under the fixed decoding scheme upon switching to the adaptive, all-channel scheme.

SIGNIFICANCE

Our results clearly show that neural activity under a BCI recording electrode (which we define as a 'cortical control column') readily adapts to generate an appropriate control signal. These results show that the practical minimal spatial resolution of these control columns with micro-ECoG BCI is likely on the order of 3 mm. Additionally, they show that the combination and interaction between neural adaptation and machine learning are critical to optimizing ECoG BCI performance.

Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience

Brain-machine interfaces (BMIs) decode brain activity to control external devices. Over the past two decades, the BMI community has grown tremendously and reached some impressive milestones, including the first human clinical trials using chronically implanted intracortical electrodes. It has also contributed experimental paradigms and important findings to basic neuroscience. In this review, we discuss neuroscience achievements stemming from BMI research, specifically that based upon upper limb prosthetic control with intracortical microelectrodes. We will focus on three main areas: first, we discuss progress in neural coding of reaches in motor cortex, describing recent results linking high dimensional representations of cortical activity to muscle activation. Next, we describe recent findings on learning and plasticity in motor cortex on various time scales. Finally, we discuss how bidirectional BMIs have led to better understanding of somatosensation in and related to motor cortex.

How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI

The learning process involved in achieving brain self-regulation is presumed to be related to several factors, such as type of feedback, reward, mental imagery, duration of training, among others. Explicitly instructing participants to use mental imagery and monetary reward are common practices in real-time fMRI (rtfMRI) neurofeedback (NF), under the assumption that they will enhance and accelerate the learning process. However, it is still not clear what the optimal strategy is for improving volitional control. We investigated the differential effect of feedback, explicit instructions and monetary reward while training healthy individuals to up-regulate the blood-oxygen-level dependent (BOLD) signal in the supplementary motor area (SMA). Four groups were trained in a two-day rtfMRI-NF protocol: GF with NF only, GF,I with NF + explicit instructions (motor imagery), GF,R with NF + monetary reward, and GF,I,R with NF + explicit instructions (motor imagery) + monetary reward. Our results showed that GF increased significantly their BOLD self-regulation from day-1 to day-2 and GF,R showed the highest BOLD signal amplitude in SMA during the training. The two groups who were instructed to use motor imagery did not show a significant learning effect over the 2 days. The additional factors, namely motor imagery and reward, tended to increase the intersubject variability in the SMA during the course of training. Whole brain univariate and functional connectivity analyses showed common as well as distinct patterns in the four groups, representing the varied influences of feedback, reward, and instructions on the brain. Hum Brain Mapp 37:3153-3171, 2016. © 2016 Wiley Periodicals, Inc.

Biomimetic rehabilitation engineering: the importance of somatosensory feedback for brain-machine interfaces

OBJECTIVE

Brain-machine interfaces (BMIs) re-establish communication channels between the nervous system and an external device. The use of BMI technology has generated significant developments in rehabilitative medicine, promising new ways to restore lost sensory-motor functions. However and despite high-caliber basic research, only a few prototypes have successfully left the laboratory and are currently home-deployed.

APPROACH

The failure of this laboratory-to-user transfer likely relates to the absence of BMI solutions for providing naturalistic feedback about the consequences of the BMI's actions. To overcome this limitation, nowadays cutting-edge BMI advances are guided by the principle of biomimicry; i.e. the artificial reproduction of normal neural mechanisms.

MAIN RESULTS

Here, we focus on the importance of somatosensory feedback in BMIs devoted to reproducing movements with the goal of serving as a reference framework for future research on innovative rehabilitation procedures. First, we address the correspondence between users' needs and BMI solutions. Then, we describe the main features of invasive and non-invasive BMIs, including their degree of biomimicry and respective advantages and drawbacks. Furthermore, we explore the prevalent approaches for providing quasi-natural sensory feedback in BMI settings. Finally, we cover special situations that can promote biomimicry and we present the future directions in basic research and clinical applications.

SIGNIFICANCE

The continued incorporation of biomimetic features into the design of BMIs will surely serve to further ameliorate the realism of BMIs, as well as tremendously improve their actuation, acceptance, and use.

New Perspectives on Neuroengineering and Neurotechnologies: NSF-DFG Workshop Report

GOAL

To identify and overcome barriers to creating new neurotechnologies capable of restoring both motor and sensory function in individuals with neurological conditions.

METHODS

This report builds upon the outcomes of a joint workshop between the US National Science Foundation and the German Research Foundation on New Perspectives in Neuroengineering and Neurotechnology convened in Arlington, VA, USA, November 13-14, 2014.

RESULTS

The participants identified key technological challenges for recording and manipulating neural activity, decoding, and interpreting brain data in the presence of plasticity, and early considerations of ethical and social issues pertinent to the adoption of neurotechnologies.

CONCLUSIONS

The envisaged progress in neuroengineering requires tightly integrated hardware and signal processing efforts, advances in understanding of physiological adaptations to closed-loop interactions with neural devices, and an open dialog with stakeholders and potential end-users of neurotechnology.

SIGNIFICANCE

The development of new neurotechnologies (e.g., bidirectional brain-computer interfaces) could significantly improve the quality of life of people living with the effects of brain or spinal cord injury, or other neurodegenerative diseases. Focused efforts aimed at overcoming the remaining barriers at the electrode tissue interface, developing implantable hardware with on-board computation, and refining stimulation methods to precisely activate neural tissue will advance both our understanding of brain function and our ability to treat currently intractable disorders of the nervous system.

Recasting brain-machine interface design from a physical control system perspective

With the goal of improving the quality of life for people suffering from various motor control disorders, brain-machine interfaces provide direct neural control of prosthetic devices by translating neural signals into control signals. These systems act by reading motor intent signals directly from the brain and using them to control, for example, the movement of a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into the movement of the cursor? Most approaches have focused on this problem from an estimation standpoint, i.e., decoders are designed to return the best estimate of motor intent possible, under various sets of assumptions about how the recorded neural signals represent motor intent. Here we recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. These results have implications for understanding how motor neurons are recruited to perform various tasks, and may lend insight into the brain's ability to conceptualize artificial systems.

Neuroplasticity subserving the operation of brain-machine interfaces

Neuroplasticity is key to the operation of brain machine interfaces (BMIs)-a direct communication pathway between the brain and a man-made computing device. Whereas exogenous BMIs that associate volitional control of brain activity with neurofeedback have been shown to induce long lasting plasticity, endogenous BMIs that use prolonged activity-dependent stimulation--and thus may curtail the time scale that governs natural sensorimotor integration loops--have been shown to induce short lasting plasticity. Here we summarize recent findings from studies using both categories of BMIs, and discuss the fundamental principles that may underlie their operation and the longevity of the plasticity they induce. We draw comparison to plasticity mechanisms known to mediate natural sensorimotor skill learning and discuss principles of homeostatic regulation that may constrain endogenous BMI effects in the adult mammalian brain. We propose that BMIs could be designed to facilitate structural and functional plasticity for the purpose of re-organization of target brain regions and directed augmentation of sensorimotor maps, and suggest possible avenues for future work to maximize their efficacy and viability in clinical applications.

Hand-in-hand advances in biomedical engineering and sensorimotor restoration

BACKGROUND

Living in a multisensory world entails the continuous sensory processing of environmental information in order to enact appropriate motor routines. The interaction between our body and our brain is the crucial factor for achieving such sensorimotor integration ability. Several clinical conditions dramatically affect the constant body-brain exchange, but the latest developments in biomedical engineering provide promising solutions for overcoming this communication breakdown.

NEW METHOD

The ultimate technological developments succeeded in transforming neuronal electrical activity into computational input for robotic devices, giving birth to the era of the so-called brain-machine interfaces. Combining rehabilitation robotics and experimental neuroscience the rise of brain-machine interfaces into clinical protocols provided the technological solution for bypassing the neural disconnection and restore sensorimotor function.

RESULTS

Based on these advances, the recovery of sensorimotor functionality is progressively becoming a concrete reality. However, despite the success of several recent techniques, some open issues still need to be addressed.

COMPARISON WITH EXISTING METHOD(S)

Typical interventions for sensorimotor deficits include pharmaceutical treatments and manual/robotic assistance in passive movements. These procedures achieve symptoms relief but their applicability to more severe disconnection pathologies is limited (e.g. spinal cord injury or amputation).

CONCLUSIONS

Here we review how state-of-the-art solutions in biomedical engineering are continuously increasing expectances in sensorimotor rehabilitation, as well as the current challenges especially with regards to the translation of the signals from brain-machine interfaces into sensory feedback and the incorporation of brain-machine interfaces into daily activities.

Explicit memory creation during sleep demonstrates a causal role of place cells in navigation

Hippocampal place cells assemblies are believed to support the cognitive map, and their reactivations during sleep are thought to be involved in spatial memory consolidation. By triggering intracranial rewarding stimulations by place cell spikes during sleep, we induced an explicit memory trace, leading to a goal-directed behavior toward the place field. This demonstrates that place cells' activity during sleep still conveys relevant spatial information and that this activity is functionally significant for navigation.

Brain-Machine Interfaces for Motor Control: A Guide for Neuroscience Clinicians

With the growing interdependence between medicine and technology, the prospect of connecting machines to the human brain is rapidly being realized. The field of neuroprosthetics is transitioning from the proof of concept stage to the development of advanced clinical treatments. In one area of brain-machine interfaces (BMIs) related to the motor system, also termed ‘motor neuroprosthetics’, research successes with implanted microelectrodes in animals have demonstrated immense potential for restoring motor deficits. Early human trials have also begun, with some success but also highlighting several technical challenges. Here we review the concepts and anatomy underlying motor BMI designs, review their early use in clinical applications, and offer a framework to evaluate these technologies in order to predict their eventual clinical utility. Ultimately, we hope to help neuroscience clinicians understand and participate in this burgeoning field.

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, task-dependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals.

Bidirectional control of a one-dimensional robotic actuator by operant conditioning of a single unit in rat motor cortex

The design of efficient neuroprosthetic devices has become a major challenge for the long-term goal of restoring autonomy to motor-impaired patients. One approach for brain control of actuators consists in decoding the activity pattern obtained by simultaneously recording large neuronal ensembles in order to predict in real-time the subject's intention, and move the prosthesis accordingly. An alternative way is to assign the output of one or a few neurons by operant conditioning to control the prosthesis with rules defined by the experimenter, and rely on the functional adaptation of these neurons during learning to reach the desired behavioral outcome. Here, several motor cortex neurons were recorded simultaneously in head-fixed awake rats and were conditioned, one at a time, to modulate their firing rate up and down in order to control the speed and direction of a one-dimensional actuator carrying a water bottle. The goal was to maintain the bottle in front of the rat's mouth, allowing it to drink. After learning, all conditioned neurons modulated their firing rate, effectively controlling the bottle position so that the drinking time was increased relative to chance. The mean firing rate averaged over all bottle trajectories depended non-linearly on position, so that the mouth position operated as an attractor. Some modifications of mean firing rate were observed in the surrounding neurons, but to a lesser extent. Notably, the conditioned neuron reacted faster and led to a better control than surrounding neurons, as calculated by using the activity of those neurons to generate simulated bottle trajectories. Our study demonstrates the feasibility, even in the rodent, of using a motor cortex neuron to control a prosthesis in real-time bidirectionally. The learning process includes modifications of the activity of neighboring cortical neurons, while the conditioned neuron selectively leads the activity patterns associated with the prosthesis control.

Restoring sensorimotor function through intracortical interfaces: progress and looming challenges

The loss of a limb or paralysis resulting from spinal cord injury has devastating consequences on quality of life. One approach to restoring lost sensory and motor abilities in amputees and patients with tetraplegia is to supply them with implants that provide a direct interface with the CNS. Such brain-machine interfaces might enable a patient to exert voluntary control over a prosthetic or robotic limb or over the electrically induced contractions of paralysed muscles. A parallel interface could convey sensory information about the consequences of these movements back to the patient. Recent developments in the algorithms that decode motor intention from neuronal activity and in approaches to convey sensory feedback by electrically stimulating neurons, using biomimetic and adaptation-based approaches, have shown the promise of invasive interfaces with sensorimotor cortices, although substantial challenges remain.

What limits the performance of current invasive brain machine interfaces?

The concept of a brain-machine interface (BMI) or a computer-brain interface is simple: BMI creates a communication pathway for a direct control by brain of an external device. In reality BMIs are very complex devices and only recently the increase in computing power of microprocessors enabled a boom in BMI research that continues almost unabated to this date, the high point being the insertion of electrode arrays into the brains of 5 human patients in a clinical trial run by Cyberkinetics with few other clinical tests still in progress. Meanwhile several EEG-based BMI devices (non-invasive BMIs) were launched commercially. Modern electronics and dry electrode technology made possible to drive the cost of some of these devices below few hundred dollars. However, the initial excitement of the direct control by brain waves of a computer or other equipment is dampened by large efforts required for learning, high error rates and slow response speed. All these problems are directly related to low information transfer rates typical for such EEG-based BMIs. In invasive BMIs employing multiple electrodes inserted into the brain one may expect much higher information transfer rates than in EEG-based BMIs because, in theory, each electrode provides an independent information channel. However, although invasive BMIs require more expensive equipment and have ethical problems related to the need to insert electrodes in the live brain, such financial and ethical costs are often not offset by a dramatic improvement in the information transfer rate. Thus the main topic of this review is why in invasive BMIs an apparently much larger information content obtained with multiple extracellular electrodes does not translate into much higher rates of information transfer? This paper explores possible answers to this question by concluding that more research on what movement parameters are encoded by neurons in motor cortex is needed before we can enjoy the next generation BMIs.

A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields

We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field) applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

Creating new functional circuits for action via brain-machine interfaces

Brain-machine interfaces (BMIs) are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI systems can also actively engage learning and adaptation. These properties make BMIs uniquely suited to study learning of motor and non-physical, abstract skills. Recent work used motor BMIs to shed light on the neural representations of skill formation and motor adaptation. Emerging work in sensory BMIs, and other novel interface systems, also highlight the promise of using BMI systems to study fundamental questions in learning and sensorimotor control. This paper outlines the interpretation of BMIs as novel closed-loop systems and the benefits of these systems for studying learning. We review BMI learning studies, their relation to motor control, and propose future directions for this nascent field. Understanding learning in BMIs may both elucidate mechanisms of natural motor and abstract skill learning, and aid in developing the next generation of neuroprostheses.

Transfer of information by BMI

Brain machine interfaces (BMI) have become important in systems neuroscience with the goal to restore motor function in paralyzed patients. We assess the current ability of BMI devices to move objects. The topics discussed include: (1) the bits of information generated by a BMI signal, (2) the limitations of including more neurons for generating a BMI signal, (3) the superiority of a BMI signal using single cells versus electroencephalography, (4) plasticity and BMI, (5) the selection of a neural code for generating BMI, (6) the suppression of body movements during BMI, and (7) the role of vision in BMI. We conclude that further research on understanding how the brain generates movement is necessary before BMI can become a reasonable option for paralyzed patients.

"Master" neurons induced by operant conditioning in rat motor cortex during a brain-machine interface task

Operant control of a prosthesis by neuronal cortical activity is one of the successful strategies for implementing brain-machine interfaces (BMI), by which the subject learns to exert a volitional control of goal-directed movements. However, it remains unknown if the induced brain circuit reorganization affects preferentially the conditioned neurons whose activity controlled the BMI actuator during training. Here, multiple extracellular single-units were recorded simultaneously in the motor cortex of head-fixed behaving rats. The firing rate of a single neuron was used to control the position of a one-dimensional actuator. Each time the firing rate crossed a predefined threshold, a water bottle moved toward the rat, until the cumulative displacement of the bottle allowed the animal to drink. After a learning period, most (88%) conditioned neurons raised their activity during the trials, such that the time to reward decreased across sessions: the conditioned neuron fired strongly, reliably and swiftly after trial onset, although no explicit instruction in the learning rule imposed a fast neuronal response. Moreover, the conditioned neuron fired significantly earlier and more strongly than nonconditioned neighboring neurons. During the first training sessions, an increase in firing rate variability was seen only for the highly conditionable neurons. This variability then decreased while the conditioning effect increased. These findings suggest that modifications during training target preferentially the neuron chosen to control the BMI, which acts then as a "master" neuron, leading in time the reconfiguration of activity in the local cortical network.

The body-machine interface: a new perspective on an old theme

Body-machine interfaces establish a way to interact with a variety of devices, allowing their users to extend the limits of their performance. Recent advances in this field, ranging from computer interfaces to bionic limbs, have had important consequences for people with movement disorders. The authors provide an overview of the basic concepts underlying the body-machine interface with special emphasis on their use for rehabilitation and for operating assistive devices. They outline the steps involved in building such an interface and highlight the critical role of body-machine interfaces in addressing theoretical issues in motor control as well as their utility in movement rehabilitation.

Neurosurgery and the dawning age of Brain-Machine Interfaces

Brain–machine interfaces (BMIs) are on the horizon for clinical neurosurgery. Electrocorticography-based platforms are less invasive than implanted microelectrodes, however, the latter are unmatched in their ability to achieve fine motor control of a robotic prosthesis capable of natural human behaviors. These technologies will be crucial to restoring neural function to a large population of patients with severe neurologic impairment – including those with spinal cord injury, stroke, limb amputation, and disabling neuromuscular disorders such as amyotrophic lateral sclerosis. On the opposite end of the spectrum are neural enhancement technologies for specialized applications such as combat. An ongoing ethical dialogue is imminent as we prepare for BMI platforms to enter the neurosurgical realm of clinical management.

M1 corticospinal mirror neurons and their role in movement suppression during action observation

Evidence is accumulating that neurons in primary motor cortex (M1) respond during action observation, a property first shown for mirror neurons in monkey premotor cortex. We now show for the first time that the discharge of a major class of M1 output neuron, the pyramidal tract neuron (PTN), is modulated during observation of precision grip by a human experimenter. We recorded 132 PTNs in the hand area of two adult macaques, of which 65 (49%) showed mirror-like activity. Many (38 of 65) increased their discharge during observation (facilitation-type mirror neuron), but a substantial number (27 of 65) exhibited reduced discharge or stopped firing (suppression-type). Simultaneous recordings from arm, hand, and digit muscles confirmed the complete absence of detectable muscle activity during observation. We compared the discharge of the same population of neurons during active grasp by the monkeys. We found that facilitation neurons were only half as active for action observation as for action execution, and that suppression neurons reversed their activity pattern and were actually facilitated during execution. Thus, although many M1 output neurons are active during action observation, M1 direct input to spinal circuitry is either reduced or abolished and may not be sufficient to produce overt muscle activity.

Decoding hindlimb movement for a brain machine interface after a complete spinal transection

Stereotypical locomotor movements can be made without input from the brain after a complete spinal transection. However, the restoration of functional gait requires descending modulation of spinal circuits to independently control the movement of each limb. To evaluate whether a brain-machine interface (BMI) could be used to regain conscious control over the hindlimb, rats were trained to press a pedal and the encoding of hindlimb movement was assessed using a BMI paradigm. Off-line, information encoded by neurons in the hindlimb sensorimotor cortex was assessed. Next neural population functions, or weighted representations of the neuronal activity, were used to replace the hindlimb movement as a trigger for reward in real-time (on-line decoding) in three conditions: while the animal could still press the pedal, after the pedal was removed and after a complete spinal transection. A novel representation of the motor program was learned when the animals used neural control to achieve water reward (e.g. more information was conveyed faster). After complete spinal transection, the ability of these neurons to convey information was reduced by more than 40%. However, this BMI representation was relearned over time despite a persistent reduction in the neuronal firing rate during the task. Therefore, neural control is a general feature of the motor cortex, not restricted to forelimb movements, and can be regained after spinal injury.

Computers and neurosurgery

At the turn of the twentieth century, the only computational device used in neurosurgical procedures was the brain of the surgeon. Today, most neurosurgical procedures rely at least in part on the use of a computer to help perform surgeries accurately and safely. The techniques that revolutionized neurosurgery were mostly developed after the 1950s. Just before that era, the transistor was invented in the late 1940s, and the integrated circuit was invented in the late 1950s. During this time, the first automated, programmable computational machines were introduced. The rapid progress in the field of neurosurgery not only occurred hand in hand with the development of modern computers, but one also can state that modern neurosurgery would not exist without computers. The focus of this article is the impact modern computers have had on the practice of neurosurgery. Neuroimaging, neuronavigation, and neuromodulation are examples of tools in the armamentarium of the modern neurosurgeon that owe each step in their evolution to progress made in computer technology. Advances in computer technology central to innovations in these fields are highlighted, with particular attention to neuroimaging. Developments over the last 10 years in areas of sensors and robotics that promise to transform the practice of neurosurgery further are discussed. Potential impacts of advances in computers related to neurosurgery in developing countries and underserved regions are also discussed. As this article illustrates, the computer, with its underlying and related technologies, is central to advances in neurosurgery over the last half century.