Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.

Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.

Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodent BMI has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguish between control types at the go cue and target acquisition, respectively. We also found effective connectivity from DLPFC→M1 throughout trials across both control types and Cd→M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

Invariant neural dynamics drive commands to control different movements.

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here, we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface (BMI) that transforms rhesus macaques' motor-cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural-activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low dimensional, and critically, they align with the BMI, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control (OFC) that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

Selective modulation of cortical population dynamics during neuroprosthetic skill learning.

Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.

Left hemisphere dominance for bilateral kinematic encoding in the human brain.

Neurophysiological studies in humans and nonhuman primates have revealed movement representations in both the contralateral and ipsilateral hemispheres. Inspired by clinical observations, we ask if this bilateral representation differs for the left and right hemispheres. Electrocorticography was recorded in human participants during an instructed-delay reaching task, with movements produced with either the contralateral or ipsilateral arm. Using a cross-validated kinematic encoding model, we found stronger bilateral encoding in the left hemisphere, an effect that was present during preparation and was amplified during execution. Consistent with this asymmetry, we also observed better across-arm generalization in the left hemisphere, indicating similar neural representations for right and left arm movements. Notably, these left hemisphere electrodes were centered over premotor and parietal regions. The more extensive bilateral encoding in the left hemisphere adds a new perspective to the pervasive neuropsychological finding that the left hemisphere plays a dominant role in praxis.

The brain is split into two hemispheres, each playing the leading role in coordinating movement for the opposite side of the body: lesions on the left hemisphere therefore often result in difficulties moving the right arm or leg, and vice versa. In fact, very few anatomical connections exist between a given hemisphere and the body parts on the same (or 'ipsilateral') side. Yet, movements produced with only one limb still engage both sides of the brain, with the hemisphere which does not control the action production, still encoding the direction and speed of the movement. Previous evidence also indicate that the two hemispheres may not have equal roles when coordinating ipsilateral movements. Merrick et al. aimed to shed light on these processes; to do so, they measured electrical activity from the surface of the brain of six patients as they moved their arms to reach a screen. The results revealed that, while the right hemisphere only encoded information about the opposite arm, the left hemisphere contained information about both arms. Finer analyses showed that, for both hemispheres, moving the opposite arm was strongly associated with activity in the primary motor cortex, a region which helps to execute movements. However, in the left hemisphere, movements from the ipsilateral arm were related to activity in brain areas involved in planning and integrating different types of sensory information. These findings contribute to a better understanding of how the motor system works, which could ultimately help with the development of brain-machine interfaces for patients who need a neuroprosthetic limb.

Diverse operant control of different motor cortex populations during learning.

During motor learning,1 as well as during neuroprosthetic learning,2-4 animals learn to control motor cortex activity in order to generate behavior. Two different populations of motor cortex neurons, intra-telencephalic (IT) and pyramidal tract (PT) neurons, convey the resulting cortical signals within and outside the telencephalon. Although a large amount of evidence demonstrates contrasting functional organization among both populations,5,6 it is unclear whether the brain can equally learn to control the activity of either class of motor cortex neurons. To answer this question, we used a calcium-imaging-based brain-machine interface (CaBMI)3 and trained different groups of mice to modulate the activity of either IT or PT neurons in order to receive a reward. We found that the animals learned to control PT neuron activity faster and better than IT neuron activity. Moreover, our findings show that the advantage of PT neurons is the result of characteristics inherent to this population as well as their local circuitry and cortical depth location. Taken together, our results suggest that the motor cortex is more efficient at controlling the activity of pyramidal tract neurons, which are embedded deep in the cortex, and relaying motor commands outside the telencephalon.

Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging.

Calcium imaging has great potential to be applied to online brain-machine interfaces (BMIs). As opposed to two-photon imaging settings, a one-photon microendoscopic imaging device can be chronically implanted and is subject to little motion artifacts. Traditionally, one-photon microendoscopic calcium imaging data are processed using the constrained nonnegative matrix factorization (CNMFe) algorithm, but this batched processing algorithm cannot be applied in real-time. An online analysis of calcium imaging data algorithm (or OnACIDe) has been proposed, but OnACIDe updates the neural components by repeatedly performing neuron identification frame-by-frame, which may decelerate the update speed if applying to online BMIs. For BMI applications, the ability to track a stable population of neurons in real-time has a higher priority over accurately identifying all the neurons in the field of view. By leveraging the fact that 1) microendoscopic recordings are rather stable with little motion artifacts and 2) the number of neurons identified in a short training period is sufficient for potential online BMI tasks such as cursor movements, we proposed the short-training CNMFe algorithm (stCNMFe) that skips motion correction and neuron identification processes to enable a more efficient BMI training program in a one-photon microendoscopic setting.

Skilled independent control of individual motor units via a non-invasive neuromuscular-machine interface.

Objective.Brain-machine interfaces (BMIs) have the potential to augment human functions and restore independence in people with disabilities, yet a compromise between non-invasiveness and performance limits their relevance.Approach.Here, we hypothesized that a non-invasive neuromuscular-machine interface providing real-time neurofeedback of individual motor units within a muscle could enable independent motor unit control to an extent suitable for high-performance BMI applications.Main results.Over 6 days of training, eight participants progressively learned to skillfully and independently control three biceps brachii motor units to complete a 2D center-out task. We show that neurofeedback enabled motor unit activity that largely violated recruitment constraints observed during ramp-and-hold isometric contractions thought to limit individual motor unit controllability. Finally, participants demonstrated the suitability of individual motor units for powering general applications through a spelling task.Significance.These results illustrate the flexibility of the sensorimotor system and highlight individual motor units as a promising source of control for BMI applications.

Hybrid dedicated and distributed coding in PMd/M1 provides separation and interaction of bilateral arm signals.

Pronounced activity is observed in both hemispheres of the motor cortex during preparation and execution of unimanual movements. The organizational principles of bi-hemispheric signals and the functions they serve throughout motor planning remain unclear. Using an instructed-delay reaching task in monkeys, we identified two components in population responses spanning PMd and M1. A "dedicated" component, which segregated activity at the level of individual units, emerged in PMd during preparation. It was most prominent following movement when M1 became strongly engaged, and principally involved the contralateral hemisphere. In contrast to recent reports, these dedicated signals solely accounted for divergence of arm-specific neural subspaces. The other "distributed" component mixed signals for each arm within units, and the subspace containing it did not discriminate between arms at any stage. The statistics of the population response suggest two functional aspects of the cortical network: one that spans both hemispheres for supporting preparatory and ongoing processes, and another that is predominantly housed in the contralateral hemisphere and specifies unilateral output.

Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque.

Microendoscopic calcium imaging with one-photon miniature microscopes enables unprecedented readout of neural circuit dynamics during active behavior in rodents. In this study, we describe successful application of this technology in the rhesus macaque, demonstrating plug-and-play, head-mounted recordings of cellular-resolution calcium dynamics from large populations of neurons simultaneously in bilateral dorsal premotor cortices during performance of a naturalistic motor reach task. Imaging is stable over several months, allowing us to longitudinally track individual neurons and monitor their relationship to motor behavior over time. We observe neuronal calcium dynamics selective for reach direction, which we could use to decode the animal's trial-by-trial motor behavior. This work establishes head-mounted microendoscopic calcium imaging in macaques as a powerful approach for studying the neural circuit mechanisms underlying complex and clinically relevant behaviors, and it promises to greatly advance our understanding of human brain function, as well as its dysfunction in neurological disease.

Recommendations for Responsible Development and Application of Neurotechnologies.

Advancements in novel neurotechnologies, such as brain computer interfaces (BCI) and neuromodulatory devices such as deep brain stimulators (DBS), will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose (1) democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, (2) new measures, including "Neurorights," for data privacy, security, and consent to empower neurotechnology users' control over their data, (3) new methods of identifying and preventing bias, and (4) the adoption of public guidelines for safe and equitable distribution of neurotechnological devices.

A wireless millimetre-scale implantable neural stimulator with ultrasonically powered bidirectional communication.

Clinically approved neural stimulators are limited by battery requirements, as well as by their large size compared with the stimulation targets. Here, we describe a wireless, leadless and battery-free implantable neural stimulator that is 1.7 mm3 and that incorporates a piezoceramic transducer, an energy-storage capacitor and an integrated circuit. An ultrasonic link and a hand-held external transceiver provide the stimulator with power and bidirectional communication. The stimulation protocols were wirelessly encoded on the fly, reducing power consumption and on-chip memory, and enabling protocol complexity with a high temporal resolution and low-latency feedback. Uplink data indicating whether stimulation occurs are encoded by the stimulator through backscatter modulation and are demodulated at the external transceiver. When embedded in ex vivo porcine tissue, the integrated circuit efficiently harvested ultrasonic power, decoded downlink data for the stimulation parameters and generated current-controlled stimulation pulses. When cuff-mounted and acutely implanted onto the sciatic nerve of anaesthetized rats, the device conferred repeatable stimulation across a range of physiological responses. The miniaturized neural stimulator may facilitate closed-loop neurostimulation for therapeutic interventions.

Neural reinforcement: re-entering and refining neural dynamics leading to desirable outcomes.

How do organisms learn to do again, on-demand, a behavior that led to a desirable outcome? Dopamine-dependent cortico-striatal plasticity provides a framework for learning behavior's value, but it is less clear how it enables the brain to re-enter desired behaviors and refine them over time. Reinforcing behavior is achieved by re-entering and refining the neural patterns that produce it. We review studies using brain-machine interfaces which reveal that reinforcing cortical population activity requires cortico-basal ganglia circuits. Then, we propose a formal framework for how reinforcement in cortico-basal ganglia circuits acts on the neural dynamics of cortical populations. We propose two parallel mechanisms: i) fast reinforcement which selects the inputs that permit the re-entrance of the particular cortical population dynamics which naturally produced the desired behavior, and ii) slower reinforcement which leads to refinement of cortical population dynamics and more reliable production of neural trajectories driving skillful behavior on-demand.

A wireless and artefact-free 128-channel neuromodulation device for closed-loop stimulation and recording in non-human primates.

Closed-loop neuromodulation systems aim to treat a variety of neurological conditions by delivering and adjusting therapeutic electrical stimulation in response to a patient's neural state, recorded in real time. Existing systems are limited by low channel counts, lack of algorithmic flexibility, and the distortion of recorded signals by large and persistent stimulation artefacts. Here, we describe an artefact-free wireless neuromodulation device that enables research applications requiring high-throughput data streaming, low-latency biosignal processing, and simultaneous sensing and stimulation. The device is a miniaturized neural interface capable of closed-loop recording and stimulation on 128 channels, with on-board processing to fully cancel stimulation artefacts. In addition, it can detect neural biomarkers and automatically adjust stimulation parameters in closed-loop mode. In a behaving non-human primate, the device enabled long-term recordings of local field potentials and the real-time cancellation of stimulation artefacts, as well as closed-loop stimulation to disrupt movement preparatory activity during a delayed-reach task. The neuromodulation device may help advance neuroscientific discovery and preclinical investigations of stimulation-based therapeutic interventions.

A high-density carbon fiber neural recording array technology.

OBJECTIVE: Microwire and Utah-style neural recording arrays are the predominant devices used for cortical neural recording, but the implanted electrodes cause a significant adverse biological response and suffer from well-studied performance degradation. Recent work has demonstrated that carbon fiber electrodes do not elicit this same adverse response, but these existing designs are not practically scalable to hundreds or thousands of recording sites. We present technology that overcomes these issues while additionally providing fine electrode pitch for spatial oversampling. APPROACH: We present a 32-channel carbon fiber monofilament-based intracortical neural recording array fabricated through a combination of bulk silicon microfabrication processing and microassembly. This device represents the first truly two-dimensional carbon fiber neural recording array. The density, channel count, and size scale of this array are enabled by an out-of-plane microassembly technique in which individual fibers are inserted through metallized and isotropically conductive adhesive-filled holes in an oxide-passivated microfabricated silicon substrate. MAIN RESULTS: Five-micron diameter fibers are spaced at a pitch of 38 microns, four times denser than state of the art one-dimensional arrays. The fine diameter of the carbon fibers affords both minimal cross-section and nearly three orders of magnitude greater lateral compliance than standard tungsten microwires. Typical [Formula: see text] impedances are on the order of hundreds of kiloohms, and successful in vivo recording is demonstrated in the motor cortex of a rat. 22 total units are recorded on 20 channels, with unit SNR ranging from 1.4 to 8.0. SIGNIFICANCE: This is the highest density microwire-style electrode array to date, and this fabrication technique is scalable to a larger number of electrodes and allows for the potential future integration of microelectronics. Large-scale carbon fiber neural recording arrays are a promising technology for reducing the inflammatory response and increasing the information density, particularly in neural recording applications where microwire arrays are already used.

Evidence for a neural law of effect.

Thorndike's law of effect states that actions that lead to reinforcements tend to be repeated more often. Accordingly, neural activity patterns leading to reinforcement are also reentered more frequently. Reinforcement relies on dopaminergic activity in the ventral tegmental area (VTA), and animals shape their behavior to receive dopaminergic stimulation. Seeking evidence for a neural law of effect, we found that mice learn to reenter more frequently motor cortical activity patterns that trigger optogenetic VTA self-stimulation. Learning was accompanied by gradual shaping of these patterns, with participating neurons progressively increasing and aligning their covariance to that of the target pattern. Motor cortex patterns that lead to phasic dopaminergic VTA activity are progressively reinforced and shaped, suggesting a mechanism by which animals select and shape actions to reliably achieve reinforcement.

Volitional Modulation of Primary Visual Cortex Activity Requires the Basal Ganglia.

Animals acquire behaviors through instrumental conditioning. Brain-machine interfaces have used instrumental conditioning to reinforce patterns of neural activity directly, especially in frontal and motor cortices, which are a rich source of signals for voluntary action. However, evidence suggests that activity in primary sensory cortices may also reflect internally driven processes, instead of purely encoding antecedent stimuli. Here, we show that rats and mice can learn to produce arbitrary patterns of neural activity in their primary visual cortex to control an auditory cursor and obtain reward. Furthermore, learning was prevented when neurons in the dorsomedial striatum (DMS), which receives input from visual cortex, were optogenetically inhibited, but not during inhibition of nearby neurons in the dorsolateral striatum. After learning, DMS inhibition did not affect production of the rewarded patterns. These data demonstrate that cortico-basal ganglia circuits play a general role in learning to produce cortical activity that leads to desirable outcomes.

Recent advances in neural dust: towards a neural interface platform.

The neural dust platform uses ultrasonic power and communication to enable a scalable, wireless, and batteryless system for interfacing with the nervous system. Ultrasound offers several advantages over alternative wireless approaches, including a safe method for powering and communicating with sub mm-sized devices implanted deep in tissue. Early studies demonstrated that neural dust motes could wirelessly transmit high-fidelity electrophysiological data in vivo, and that theoretically, this system could be miniaturized well below the mm-scale. Future developments are focused on further minimization of the platform, better encapsulation methods as a path towards truly chronic neural interfaces, improved delivery mechanisms, stimulation capabilities, and finally refinements to enable deployment of neural dust in the central nervous system.

Caudate Microstimulation Increases Value of Specific Choices.

Value-based decision-making involves an assessment of the value of items available and the actions required to obtain them. The basal ganglia are highly implicated in action selection and goal-directed behavior [1-4], and the striatum in particular plays a critical role in arbitrating between competing choices [5-9]. Previous work has demonstrated that neural activity in the caudate nucleus is modulated by task-relevant action values [6, 8]. Nonetheless, how value is represented and maintained in the striatum remains unclear since decision-making in these tasks relied on spatially lateralized responses, confounding the ability to generalize to a more abstract choice task [6, 8, 9]. Here, we investigate striatal value representations by applying caudate electrical stimulation in macaque monkeys (n = 3) to bias decision-making in a task that divorces the value of a stimulus from motor action. Electrical microstimulation is known to induce neural plasticity [10, 11], and caudate microstimulation in primates has been shown to accelerate associative learning [12, 13]. Our results indicate that stimulation paired with a particular stimulus increases selection of that stimulus, and this effect was stimulus dependent and action independent. The modulation of choice behavior using microstimulation was best modeled as resulting from changes in stimulus value. Caudate neural recordings (n = 1) show that changes in value-coding neuron activity are stimulus value dependent. We argue that caudate microstimulation can differentially increase stimulus values independent of action, and unilateral manipulations of value are sufficient to mediate choice behavior. These results support potential future applications of microstimulation to correct maladaptive plasticity underlying dysfunctional decision-making related to neuropsychiatric conditions.