Temporal relation between neural activity and neurite pruning on a numerical model and a microchannel device with micro electrode array.

Synapse elimination and neurite pruning are essential processes for the formation of neuronal circuits. These regressive events depend on neural activity and occur in the early postnatal days known as the critical period, but what makes this temporal specificity is not well understood. One possibility is that the neural activities during the developmentally regulated shift of action of GABA inhibitory transmission lead to the critical period. Moreover, it has been reported that the shifting action of the inhibitory transmission on immature neurons overlaps with synapse elimination and neurite pruning and that increased inhibitory transmission by drug treatment could induce temporal shift of the critical period. However, the relationship among these phenomena remains unclear because it is difficult to experimentally show how the developmental shift of inhibitory transmission influences neural activities and whether the activities promote synapse elimination and neurite pruning. In this study, we modeled synapse elimination in neuronal circuits using the modified Izhikevich's model with functional shifting of GABAergic transmission. The simulation results show that synaptic pruning within a specified period like the critical period is spontaneously generated as a function of the developmentally shifting inhibitory transmission and that the specific firing rate and increasing synchronization of neural circuits are seen at the initial stage of the critical period. This temporal relationship was experimentally supported by an in vitro primary culture of rat cortical neurons in a microchannel on a multi-electrode array (MEA). The firing rate decreased remarkably between the 18-25 days in vitro (DIV), and following these changes in the firing rate, the neurite density was slightly reduced. Our simulation and experimental results suggest that decreasing neural activity due to developing inhibitory synaptic transmission could induce synapse elimination and neurite pruning at particular time such as the critical period. Additionally, these findings indicate that we can estimate the maturity level of inhibitory transmission and the critical period by measuring the firing rate and the degree of synchronization in engineered neural networks.

Rattractor-Instant guidance of a rat into a virtual cage using the deep brain stimulation.

We developed "Rattractor" (rat attractor), a system to apply electrical stimuli to the deep brain of a rat as it stays in a specified region or a virtual cage to demonstrate an instant electrophysiological feedback guidance for animals. Two wire electrodes were implanted in the brains of nine rats. The electrodes targeted the medial forebrain bundle (MFB), which is a part of the reward system in the deep brain. Following the recovery period, the rats were placed in a plain field where they could move freely, but wired to a stimulation circuit. An image sensor installed over the field detected the subject's position, which triggered the stimulator such that the rat remained within the virtual cage. We conducted a behavioral experiment to evaluate the sojourn ratio of rats residing in the region. Thereafter, a histological analysis of the rat brain was performed to confirm the position of the stimulation sites in the brain. Seven rats survived the surgery and the recovery period without technical failures such as connector breaks. We observed that three of them tended to stay in the virtual cage during stimulation, and this effect was maintained for two weeks. Histological analysis revealed that the electrode tips were correctly placed in the MFB region of the rats. The other four subjects showed no apparent preference for the virtual cage. In these rats, we did not find electrode tips in the MFB, or could not determine their positions. Almost half of the rats tended to remain inside the virtual cage when position-related reward stimuli were triggered in the MFB region. Notably, our system did not require previous training or sequential interventions to affect the behavioral preferences of subjects. This process is similar to the situation in which sheep are chased by a shepherd dog in the desired direction.

A novel micro-ECoG recording method for recording multisensory neural activity from the parietal to temporal cortices in mice.

Characterization of inter-regional interactions in brain is essential for understanding the mechanism relevant to normal brain function and neurological disease. The recently developed flexible micro (µ)-electrocorticography (µECoG) device is one prominent method used to examine large-scale cortical activity across multiple regions. The sheet-shaped µECoG electrodes arrays can be placed on a relatively wide area of cortical surface beneath the skull by inserting the device into the space between skull and brain. Although rats and mice are useful tools for neuroscience, current µECoG recording methods in these animals are limited to the parietal region of cerebral cortex. Recording cortical activity from the temporal region of cortex in mice has proven difficult because of surgical barriers created by the skull and surrounding temporalis muscle anatomy. Here, we developed a sheet-shaped 64-channel µECoG device that allows access to the mouse temporal cortex, and we determined the factor determining the appropriate bending stiffness for the µECoG electrode array. We also established a surgical technique to implant the electrode arrays into the epidural space over a wide area of cerebral cortex covering from the barrel field to olfactory (piriform) cortex, which is the deepest region of the cerebral cortex. Using histology and computed tomography (CT) images, we confirmed that the tip of the µECoG device reached to the most ventral part of cerebral cortex without causing noticeable damage to the brain surface. Moreover, the device simultaneously recorded somatosensory and odor stimulus-evoked neural activity from dorsal and ventral parts of cerebral cortex in awake and anesthetized mice. These data indicate that our µECoG device and surgical techniques enable the recording of large-scale cortical activity from the parietal to temporal cortex in mice, including somatosensory and olfactory cortices. This system will provide more opportunities for the investigation of physiological functions from wider areas of the mouse cerebral cortex than those currently available with existing ECoG techniques.

Brain-controlled cycling system for rehabilitation following paraplegia with delay-time prediction.

Objective.Robotic rehabilitation systems have been investigated to assist with motor dysfunction recovery in patients with lower-extremity paralysis caused by central nervous system lesions. These systems are intended to provide appropriate sensory feedback associated with locomotion. Appropriate feedback is thought to cause synchronous neuron firing, resulting in the recovery of function.Approach.In this study, we designed and evaluated an ergometric cycling wheelchair, with a brain-machine interface (BMI), that can force the legs to move by including normal stepping speeds and quick responses. Experiments were conducted in five healthy subjects and one patient with spinal cord injury (SCI), who experienced the complete paralysis of the lower limbs. Event-related desynchronization in theßband (18-28 Hz) was used to detect lower-limb motor images.Main results.An ergometer-based BMI system was able to safely and easily force patients to perform leg movements, at a rate of approximately 1.6 s/step (19 rpm), with an online accuracy rate of 73.1% for the SCI participant. Mean detection time from the cue to pedaling onset was 0.83±0.31 s.Significance.This system can easily and safely maintain a normal walking speed during the experiment and be designed to accommodate the expected delay between the intentional onset and physical movement, to achieve rehabilitation effects for each participant. Similar BMI systems, implemented with rehabilitation systems, may be applicable to a wide range of patients.

Efficient sequential Bayesian inference method for real-time detection and sorting of overlapped neural spikes.

Overlapping of extracellularly recorded neural spike waveforms causes the original spike waveforms to become hidden and merged, confounding the real-time detection and sorting of these spikes. Methods proposed for solving this problem include using a multi-trode or placing a restriction on the complexity of overlaps. In this paper, we propose a rapid sequential method for the robust detection and sorting of arbitrarily overlapped spikes recorded with arbitrary types of electrodes. In our method, the probabilities of possible spike trains, including those that are overlapping, are evaluated by sequential Bayesian inference based on probabilistic models of spike-train generation and extracellular voltage recording. To reduce the high computational cost inherent in an exhaustive evaluation, candidates with low probabilities are considered as impossible candidates and are abolished at each sampling time to limit the number of candidates in the next evaluation. In addition, the data from a few subsequent sampling times are considered and used to calculate the "look-ahead probability", resulting in improved calculation efficiency due to a more rapid elimination of candidates. These sufficiently reduce computational time to enable real-time calculation without impairing performance. We assessed the performance of our method using simulated neural signals and actual neural signals recorded in primary cortical neurons cultured on a multi-electrode array. Our results demonstrated that our computational method could be applied in real-time with a delay of less than 10 ms. The estimation accuracy was higher than that of a conventional spike sorting method, particularly for signals with multiple overlapping spikes.

Development of exoskeletal robotic limbs for a rat controlled by neural signals based on a vehicular neuro-robotic platform RatCar.

A pair of exoskeletal limbs for a rat has been developed based on a vehicular Brain-Machine Interface "Rat-Car". The "RatCar" is a whole-body motor prosthesis system for a rat developed by the authors, estimating locomotion velocity according to neural signals pattern to move the rat body by the vehicle instead of its original limbs. In this paper, exoskeletal limbs have displaced the wheels for more natural modality of body control. The system was tested by applying peripheral nerve signals from a behaving rat.

RatCar: A vehicular neuro-robotic platform for a rat with a sustaining structure of the rat body under the vehicle.

An online neuro-robotic platform in the form of a small vehicle, the "RatCar" has been developed. First, a rat had neural electrodes implanted in the motor cortices to record extracellular potentials. Then, our system combined the rat and its vehicle body by hanging the rat under its floor. In this paper, an experimental platform is proposed to observe and analyze motor commands by correlating neural signals with locomotion states. It was designed to operate in 2 modes; (a) adaptively correlating neural signals and locomotion states to determine motor commands, and (b) applying the estimated motor commands to control the vehicle according to the intention of the rat. As a result, time-varying correlation between neural and locomotion activities has been adaptively visualized in real time to analyze motor commands in various body conditions. In addition, a control of the vehicle has been improved.

Estimation of finger postures to control a maniform device for playing a trumpet using electromyographic signals with external triggers.

Electromyographic (EMG) signals have been used to control active prosthetic arms for amputees. One of the obstacles in making such prosthetic arms is the timed estimation of posture, because EMG signals and muscle movements are not necessarily synchronized. We estimated the finger motions for trumpet players by using both surface EMG (sEMG) and the timing information using body motion. The algorithms consisted of Principal Component Analysis (PCA), and Support Vector Machine (SVM). The results showed that applying the timing information using body motion increases how precisely the motion of the fingers is estimated.

Effect of simultaneous vibrations to two tendons on velocity of the induced illusory movement.

A typical prosthetic limb has sensory shortcomings, one of which is lack of kinesthesia. Conventional methods to evoke kinesthesia, which would be required for a precise control of prosthetic limbs, include tendon vibration and skin stretch, but these produce only the sensation of small movements. In this study, tendon vibration is extended to evoke sensations of a more rapid movement. A stimulation method in which vibration is applied to two tendons is proposed, in contrast to most studies in which a single tendon was vibrated. Experimental results indicated that vibration applied to both ends of a muscle produces sensations of more rapid movement than vibration to just one. However, no significant difference in sensation was found between vibrating tendons of synergistic muscles and a single tendon.

RatCar system for estimating locomotion states using neural signals with parameter monitoring: Vehicle-formed brain-machine interfaces for rat.

An online brain-machine interface (BMI) in the form of a small vehicle, the 'RatCar,' has been developed. A rat had neural electrodes implanted in its primary motor cortex and basal ganglia regions to continuously record neural signals. Then, a linear state space model represents a correlation between the recorded neural signals and locomotion states (i.e., moving velocity and azimuthal variances) of the rat. The model parameters were set so as to minimize estimation errors, and the locomotion states were estimated from neural firing rates using a Kalman filter algorithm. The results showed a small oscillation to achieve smooth control of the vehicle in spite of fluctuating firing rates with noises applied to the model. Major variation of the model variables converged in a first 30 seconds of the experiments and lasted for the entire one hour session.

Estimation of locomotion speed and directions changes to control a vehicle using neural signals from the motor cortex of rat.

We have developed a brain-machine interface (BMI) in the form of a small vehicle, which we call the RatCar. In this system, we implanted wire electrodes in the motor cortices of rat's brain to continuously record neural signals. We applied a linear model to estimate the locomotion state (e.g., speed and directions) of a rat using a weighted summation model for the neural firing rates. With this information, we then determined the approximate movement of a rat. Although the estimation is still imprecise, results suggest that our model is able to control the system to some degree. In this paper, we give an overview of our system and describe the methods used, which include continuous neural recording, spike detection and a discrimination algorithm, and a locomotion estimation model minimizes the square error of the locomotion speed and changes in direction.