Fine control of operantly conditioned firing patterns of cortical neurons

Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task

Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.

Operant conditioning reveals task-specific responses of single neurons in a brain-machine interface

OBJECTIVE

Volitional modulation of single cortical neurons holds great potential for the implementation of brain-machine interfaces (BMIs) because it can induce a rapid acquisition of arbitrary associations between machines and neural activity. It can also be used as a framework to study the limits of single-neuron control in BMIs.

APPROACH

We tested the control of a one-dimensional actuator in two BMI tasks which differed only in the neural contingency that determined when a reward was dispensed. A thresholded activity task, commonly implemented in single-neuron BMI control, consisted of reaching or exceeding a neuron activity level, while the second task consisted of reaching and maintaining a narrow neuron activity level (i.e. windowed activity task).

MAIN FINDINGS

Single neurons in layer V of the motor cortex of rats improved performance during both the thresholded activity and windowed activity BMI tasks. However, correct performance during the windowed activity task was accompanied by activation of neighboring neurons, not in direct control of the BMI. In contrast, only neurons in direct control of the BMI were active at the time of reward during the thresholded activity task.

SIGNIFICANCE

These results suggest that thresholded activity single-neuron BMI implementations are more appropriate compared to windowed activity BMI tasks to capitalize on the adaptability of cortical circuits to acquire novel arbitrary skills.

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed.

Implantable neurotechnologies: a review of micro- and nanoelectrodes for neural recording

Electrodes serve as the first critical interface to the biological organ system. In neuroprosthetic applications, for example, electrodes interface to the tissue for either signal recording or tissue stimulation. In this review, we consider electrodes for recording neural activity. Recording electrodes serve as wiretaps into the neural tissues, providing readouts of electrical activity. These signals give us valuable insights into the organization and functioning of the nervous system. The recording interfaces have also shown promise in aiding treatment of motor and sensory disabilities caused by neurological disorders. Recent advances in fabrication technology have generated wide interest in creating tiny, high-density electrode interfaces for neural tissues. An ideal electrode should be small enough and be able to achieve reliable and conformal integration with the structures of the nervous system. As a result, the existing electrode designs are being shrunk and packed to form small form factor interfaces to tissue. Here, an overview of the historic and state-of-the-art electrode technologies for recording neural activity is presented first with a focus on their development road map. The fact that the dimensions of recording electrode sites are being scaled down from micron to submicron scale to enable dense interfaces is appreciated. The current trends in recording electrode technologies are then reviewed. Current and future considerations in electrode design, including the use of inorganic nanostructures and biologically inspired or biocomapatible materials are discussed, along with an overview of the applications of flexible materials and transistor transduction schemes. Finally, we detail the major technical challenges facing chronic use of reliable recording electrode technology.

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.

Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons

Pioneering studies demonstrated that novel degrees of freedom could be controlled individually by directly encoding the firing rate of single motor cortex neurons, without regard to each neuron's role in controlling movement of the native limb. In contrast, recent brain-computer interface work has emphasized decoding outputs from large ensembles that include substantially more neurons than the number of degrees of freedom being controlled. To bridge the gap between direct encoding by single neurons and decoding output from large ensembles, we studied monkeys controlling one degree of freedom by comodulating up to four arbitrarily selected motor cortex neurons. Performance typically exceeded random quite early in single sessions and then continued to improve to different degrees in different sessions. We therefore examined factors that might affect performance. Performance improved with larger ensembles. In contrast, other factors that might have reflected preexisting synaptic architecture-such as the similarity of preferred directions-had little if any effect on performance. Patterns of comodulation among ensemble neurons became more consistent across trials as performance improved over single sessions. Compared with the ensemble neurons, other simultaneously recorded neurons showed less modulation. Patterns of voluntarily comodulated firing among small numbers of arbitrarily selected primary motor cortex (M1) neurons thus can be found and improved rapidly, with little constraint based on the normal relationships of the individual neurons to native limb movement. This rapid flexibility in relationships among M1 neurons may in part underlie our ability to learn new movements and improve motor skill.

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.

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.

Dissociating motor cortex from the motor

During closed-loop control of a brain-computer interface, neurons in the primary motor cortex can be intensely active even though the subject may be making no detectable movement or muscle contraction. How can neural activity in the primary motor cortex become dissociated from the movements and muscles of the native limb that it normally controls? Here we examine circumstances in which motor cortex activity is known to dissociate from movement--including mental imagery, visuo-motor dissociation and instructed delay. Many such motor cortex neurons may be related to muscle activity only indirectly. Furthermore, the integration of thousands of synaptic inputs by individual α-motoneurons means that under certain circumstances even cortico-motoneuronal cells, which make monosynaptic connections to α-motoneurons, can become dissociated from muscle activity. The natural ability of motor cortex neurons under voluntarily control to become dissociated from bodily movement may underlie the utility of this cortical area for controlling brain-computer interfaces.

Neural decoding based on probabilistic neural network

Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices, such as robot arms, computer cursors, and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper, two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced, the PNN decoder and the modified PNN (MPNN) decoder. In the experiment, rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity, and pressure was recorded by a pressure sensor synchronously. After training, the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their performances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder, with a CC of 0.8657 and an MSE of 0.2563, outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance, indicating that the MPNN decoder can handle different tasks in BMI system, including the detection of movement states and estimation of continuous kinematic parameters.

Rapid changes in throughput from single motor cortex neurons to muscle activity

Motor cortex output is capable of considerable reorganization, which involves modulation of excitability within the cortex. Does such reorganization also involve changes beyond the cortex, at the level of throughput from single motor cortex neurons to muscle activity? We examined such throughput during a paradigm that provided incentive for enhancing functional connectivity from motor cortex neurons to muscles. Short-latency throughput from a recorded neuron to muscle activity not present during some behavioral epochs often appeared during others. Such changes in throughput could not always be attributed to a higher neuron firing rate, to more ongoing muscle activity, or to neuronal synchronization, indicating that reorganization of motor cortex output may involve rapid changes in functional connectivity from single motor cortex neurons to alpha-motoneuron pools.

Selection and parameterization of cortical neurons for neuroprosthetic control

When designing neuroprosthetic interfaces for motor function, it is crucial to have a system that can extract reliable information from available neural signals and produce an output suitable for real life applications. Systems designed to date have relied on establishing a relationship between neural discharge patterns in motor cortical areas and limb movement, an approach not suitable for patients who require such implants but who are unable to provide proper motor behavior to initially tune the system. We describe here a method that allows rapid tuning of a population vector-based system for neural control without arm movements. We trained highly motivated primates to observe a 3D center-out task as the computer played it very slowly. Based on only 10-12 s of neuronal activity observed in M1 and PMd, we generated an initial mapping between neural activity and device motion that the animal could successfully use for neuroprosthetic control. Subsequent tunings of the parameters led to improvements in control, but the initial selection of neurons and estimated preferred direction for those cells remained stable throughout the remainder of the day. Using this system, we have observed that the contribution of individual neurons to the overall control of the system is very heterogeneous. We thus derived a novel measure of unit quality and an indexing scheme that allowed us to rate each neuron's contribution to the overall control. In offline tests, we found that fewer than half of the units made positive contributions to the performance. We tested this experimentally by having the animals control the neuroprosthetic system using only the 20 best neurons. We found that performance in this case was better than when the entire set of available neurons was used. Based on these results, we believe that, with careful task design, it is feasible to parameterize control systems without any overt behaviors and that subsequent control system design will be enhanced with cautious unit selection. These improvements can lead to systems demanding lower bandwidth and computational power, and will pave the way for more feasible clinical systems.

The cone electrode: a long-term electrode that records from neurites grown onto its recording surface

A novel long-term recording electrode combines neural regeneration with a standard wire recording technique. The electrode consists of an insulated gold wire fixed inside a hollow glass cone. A piece of sciatic nerve is placed in the glass cone before implantation in cortex of rat. Cortical neurites grow into the sciatic nerve in the cone from surrounding neurons and their electrical activity is recorded via the wire (or wires) in the cone. This activity increases in amplitude over the first few weeks after implantation and remains stable until termination of the experiment many months later. Activity of both single and multi units has been recorded. The cone electrode opens unique opportunities for studies of neurite growth in vivo, for plasticity studies on a captive set of neurites, for studying the neural correlates of behavior and motor learning, and for accessing the central nervous systems of patients with severe paralysing and communicative disorders.

Evolution of myoelectrical and precentral cell activities during learning of a new amplitude of movement

The object of these experiments was to determine if changes in precentral neuron activity may be related to learning of a new amplitude of movement. Data were obtained from two monkeys trained to stop, in a given position, an elbow flexion movement in order to get a reward. A screen prevented the animal from seeing its forearm. The terminal position was not indicated by a cue, and movements were self-initiated. Motor performance and cell activity were analyzed during the period of learning of a new amplitude of movement. The cells studied presented the reciprocal patterns of activity: they were active flexion but discharged during passive extension. Results clearly indicated that: (1) amplitude was the actual parameter which was learned; (2) the peak frequency of discharge of motor cortex neurons increased during learning, as did the frequency of reward; and (3) the peak frequency of discharge related to passive movements was not changed by conditioning. The results support the hypothesis that the increase of the activity tied to active movements which is observed during conditioning may not be related to an increase of peripheral feedback but expresses a greater 'corticalization' of the movement.

Operant control of precentral neurons: control of modal interspike intervals

The objects of these experiments were: (a) to determine modal interspike intervals (ISIs) of precentral cells involved in repetitious, gross motor movements; (b) to compare those modal ISIs to the modal ISIs of similar neurons under operant control; and (c) to determine if monkeys could change the modal ISIs of operantly controlled precentral neurons. Data were obtained from 4 monkeys conditioned to produce tonic firing of precentral neurons and one monkey trained to produce repetitious movements of the neck and contralateral limbs. Results are: (a) the modal ISIs from operantly controlled precentral units do not differ significantly from precentral neurons involved in repetitive gross motor movements; and (b) while under operant control, the monkeys cannot modify significantly the modal ISI of the majority of precentral neurons.