Encoding of forelimb forces by corticospinal tract activity in the rat

Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using Three-Dimensional Convolutional Neural Network

To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats. The cats were trained to walk on a moving belt in a hindlimb-only condition, while their forelimbs were kept on the front body of the treadmill. The bilateral hindlimb joint angles were decoded using local field potential signals recorded using a microelectrode array implanted in the dorsal and lateral columns of both the left and right sides of the cat spinal cord. The results show that contralateral hindlimb kinematics can be decoded as accurately as ipsilateral kinematics. Interestingly, hindlimb kinematics of both legs can be accurately decoded from the lateral columns within one side of the spinal cord during hindlimb-only locomotion. The results indicated that there was no significant difference between the decoding performances obtained using neural signals recorded from the dorsal and lateral columns. The results of the time-frequency analysis show that event-related synchronization (ERS) and event-related desynchronization (ERD) patterns in all frequency bands could reveal the dynamics of the neural signals during movement. The onset and offset of the movement can be clearly identified by the ERD/ERS patterns. The results of the mutual information (MI) analysis showed that the theta frequency band contained significantly more limb kinematics information than the other frequency bands. Moreover, the theta power increased with a higher locomotion speed.

Spinal cord bioelectronic interfaces: opportunities in neural recording and clinical challenges

Bioelectronic stimulation of the spinal cord has demonstrated significant progress in restoration of motor function in spinal cord injury (SCI). The proximal, uninjured spinal cord presents a viable target for the recording and generation of control signals to drive targeted stimulation. Signals have been directly recorded from the spinal cord in behaving animals and correlated with limb kinematics. Advances in flexible materials, electrode impedance and signal analysis will allow SCR to be used in next-generation neuroprosthetics. In this review, we summarize the technological advances enabling progress in SCR and describe systematically the clinical challenges facing spinal cord bioelectronic interfaces and potential solutions, from device manufacture, surgical implantation to chronic effects of foreign body reaction and stress-strain mismatches between electrodes and neural tissue. Finally, we establish our vision of bi-directional closed-loop spinal cord bioelectronic bypass interfaces that enable the communication of disrupted sensory signals and restoration of motor function in SCI.

The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance

Numerous brain–machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI.

Decoding hindlimb kinematics from descending and ascending neural signals during cat locomotion

OBJECTIVE

The main objective of this research is to record both sensory and motor information from the ascending and descending tracts within the spinal cord for decoding the hindlimb kinematics during walking on the treadmill.

APPROACH

Two different experimental paradigms (i.e., active and passive) were used in the current study. During active experiments, five cats were trained to walk bipedally while their hands kept on the front frame of the treadmill for balance or to walk quadrupedally. During passive experiments, the limb was passively moved by the experimenter. Local field potential (LFP) activity was recorded using a microwire array implanted in the dorsal column (DC) and lateral column (LC) of the L3-L4 spinal segments. The amplitude and frequency components of the LFP formed the feature set and the elastic net regularization was used to decode the hindlimb joint angles.

MAIN RESULTS

The results show that there is no significant difference between the information content of the signals recorded from the DC and LC regions during walking on the treadmill, but the information content of the DC is significantly higher than that of the LC during passively applied movement of the hindlimb in the anesthetized cats. Moreover, the decoding performance obtained using the recorded signals from the DC is comparable with that from the LC during locomotion. But, the decoding performance obtained using the recording channels in the DC is significantly better than that obtained using the signals recorded from the LC. The long-term analysis shows that robust decoding performance can be achieved over 2-3 months without a significant decrease in performance.

SIGNIFICANCE

This work presents a promising approach to developing a natural and robust motor neuroprosthesis device using descending neural signals to execute the movement and ascending neural signals as the feedback information for control of the movement.

Behavioral testing in animal models of spinal cord injury

This review is based on a lecture presented at the Craig H. Neilsen Foundation sponsored Spinal Cord Injury Training Program at Ohio State University. We discuss the advantages and challenges of injury models in rodents and theory relation to various behavioral outcome measures. We offer strategies and advice on experimental design, behavioral testing, and on the challenges, one will encounter with animal testing. This review is designed to guide those entering the field of spinal cord injury and/or involved with in vivo animal testing.

Carbon Fiber Electrodes for in Vivo Spinal Cord Recordings

Development of micro electrode arrays for neural recording is an active field that thrives on novel materials and fabrication techniques offered by micro fabrication technology. The material and mechanical properties of microelectrode arrays have a critical role on the quality and longevity of neural signals. In this study, carbon fiber microelectrode (CFME) bundles were developed and implanted in the spinal cord of experimental animals for textbf{\textit{in vivo{recording. Neural data analysis revealed that single spikes could successfully be recorded and sorted. Removal of approximately $75 \mu \mathrm{m}$ of the parylene-C coating at the tips of the fibers increased the signalto-noise ratio. Connecting multiple (three) carbon fiber filaments to the same recording channel did not deteriorate the signal quality compared to that of undesheathed fibers. Immunohistochemistry showed that electrode tips were splayed in tissue after implantation and CF bundles had a small footprint with mild encapsulation around them. These results are very promising for the use of CFME bundles for recordings of spinal cord signals in behaving animals.

Prediction of Forelimb EMGs and Movement Phases from Corticospinal Signals in the Rat During the Reach-to-Pull Task

Brain-computer interfaces access the volitional command signals from various brain areas in order to substitute for the motor functions lost due to spinal cord injury or disease. As the final common pathway of the central nervous system (CNS) outputs, the descending tracts of the spinal cord offer an alternative site to extract movement-related command signals. Using flexible 2D microelectrode arrays, we have recorded the corticospinal tract (CST) signals in rats during a reach-to-pull task. The CST activity was then classified by the forelimb movement phases into two or three classes in a training dataset and cross validated in a test set. The average classification accuracies were [Formula: see text] (min: [Formula: see text] to max: [Formula: see text]) and [Formula: see text] (min: 43% to max: 71%) for two-class and three-class cases, respectively. The forelimb flexor and extensor EMG envelopes were also predicted from the CST signals using linear regression. The average correlation coefficient between the actual and predicted EMG signals was [Formula: see text] [Formula: see text], whereas the highest correlation was 0.81 for the biceps EMG. Although the forelimb motor function cannot be explained completely by the CST activity alone, the success rates obtained in reconstructing the EMG signals support the feasibility of a spinal-cord-computer interface as a concept.

Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals

Advanced algorithms are required to reveal the complex relations between neural and behavioral data. In this study, forelimb electromyography (EMG) signals were reconstructed from multi-unit neural signals recorded with multiple electrode arrays (MEAs) from the corticospinal tract (CST) in rats. A six-layer convolutional neural network (CNN) was compared with linear decoders for predicting the EMG signal. The network contained three session-dependent Rectified Linear Unit (ReLU) feature layers and three Gamma function layers were shared between sessions. Coefficient of determination (R²) values over 0.2 and correlations over 0.5 were achieved for reconstruction within individual sessions in multiple animals, even though the forelimb position was unconstrained for most of the behavior duration. The CNN performed visibily better than the linear decoders and model responses outlasted the activation duration of the rat neuromuscular system. These findings suggest that the CNN model implicitly predicted short-term dynamics of skilled forelimb movements from neural signals. These results are encouraging that similar problems in neural signal processing may be solved using variants of CNNs defined with simple analytical functions. Low powered firmware can be developed to house these CNN solutions in real-time applications.

Prediction of forelimb muscle EMGs from the corticospinal signals in rats

To generate voluntary forearm movements, the information that is encoded in the activity of the cortical neurons has to travel through the spinal cord and activate the skeletal muscles. The axons carrying these signals are tightly bundled together in the descending tracts that control the spinal circuitry innervating the forearm muscles. In this paper, we show that corticospinal tract (CST) signals can be used to predict forearm electromyographic (EMG) activities that are recorded during an isometric-pull task. Rats were trained to pull on a metal bar through a window. A flexible-substrate multi-electrode array was chronically implanted into the dorsal column of the cervical spinal cord. Field potentials and multi-unit activities were recorded from the descending axons of the CST while the rat performed the task. Forelimb forces and EMG signals from a wrist extensor and a flexor, and the biceps and triceps were reconstructed using the neural signals in multiple sessions over three weeks. The regression coefficients found from the trial set were cross-validated on the other trials recorded on the same day. The maximum correlation coefficient between the actual and predicted signal was for the biceps (R=0.88). These results suggest the feasibility of an EMG-based spinal-cord-computer-interface (SCCI) for subjects with spinal cord injury.

A Long-Term BCI Study With ECoG Recordings in Freely Moving Rats

BACKGROUND

Brain Computer Interface (BCI) studies are performed in an increasing number of applications. Questions are raised about electrodes, data processing and effectors. Experiments are needed to solve these issues.

OBJECTIVE

To develop a simple BCI set-up to easier studies for improving the mathematical tools to process the ECoG to control an effector.

METHOD

We designed a simple BCI using transcranial electrodes (17 screws, three mechanically linked to create a common reference, 14 used as recording electrodes) to record Electro-Cortico-Graphic (ECoG) neuronal activities in rodents. The data processing is based on an online self-paced non-supervised (asynchronous) BCI paradigm. N-way partial least squares algorithm together with Continuous Wavelet Transformation of ECoG recordings detect signatures related to motor activities. Signature detection in freely moving rats may activate external effectors during a behavioral task, which involved pushing a lever to obtain a reward.

RESULTS

After routine training, we showed that peak brain activity preceding a lever push (LP) to obtain food reward was located mostly in the cerebellar cortex with a higher correlation coefficient, suggesting a strong postural component and also in the occipital cerebral cortex. Analysis of brain activities provided a stable signature in the high gamma band (∼180Hz) occurring within 1500 msec before the lever push approximately around -400 msec to -500 msec. Detection of the signature from a single cerebellar cortical electrode triggers the effector with high efficiency (68% Offline and 30% Online) and rare false positives per minute in sessions about 30 minutes and up to one hour (∼2 online and offline).

CONCLUSIONS

In summary, our results are original as compared to the rest of the literature, which involves rarely rodents, a simple BCI set-up has been developed in rats, the data show for the first time long-term, up to one year, unsupervised online control of an effector.

Polydimethylsiloxane-based optical waveguides for tetherless powering of floating microstimulators

Neural electrodes and associated electronics are powered either through percutaneous wires or transcutaneous powering schemes with energy harvesting devices implanted underneath the skin. For electrodes implanted in the spinal cord and the brain stem that experience large displacements, wireless powering may be an option to eliminate device failure by the breakage of wires and the tethering of forces on the electrodes. We tested the feasibility of using optically clear polydimethylsiloxane (PDMS) as a waveguide to collect the light in a subcutaneous location and deliver to deeper regions inside the body, thereby replacing brittle metal wires tethered to the electrodes with PDMS-based optical waveguides that can transmit energy without being attached to the targeted electrode. We determined the attenuation of light along the PDMS waveguides as 0.36 ± 0.03 ?? dB / cm and the transcutaneous light collection efficiency of cylindrical waveguides as 44 % ± 11 % by transmitting a laser beam through the thenar skin of human hands. We then implanted the waveguides in rats for a month to demonstrate the feasibility of optical transmission. The collection efficiency and longitudinal attenuation values reported here can help others design their own waveguides and make estimations of the waveguide cross-sectional area required to deliver sufficient power to a certain depth in tissue.

Continuous Force Decoding from Local Field Potentials of the Primary Motor Cortex in Freely Moving Rats

Local field potential (LFP) signals recorded by intracortical microelectrodes implanted in primary motor cortex can be used as a high informative input for decoding of motor functions. Recent studies show that different kinematic parameters such as position and velocity can be inferred from multiple LFP signals as precisely as spiking activities, however, continuous decoding of the force magnitude from the LFP signals in freely moving animals has remained an open problem. Here, we trained three rats to press a force sensor for getting a drop of water as a reward. A 16-channel micro-wire array was implanted in the primary motor cortex of each trained rat, and obtained LFP signals were used for decoding of the continuous values recorded by the force sensor. Average coefficient of correlation and the coefficient of determination between decoded and actual force signals were r = 0.66 and R² = 0.42, respectively. We found that LFP signal on gamma frequency bands (30–120 Hz) had the most contribution in the trained decoding model. This study suggests the feasibility of using low number of LFP channels for the continuous force decoding in freely moving animals resembling BMI systems in real life applications.