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 80 ± 10% (min: 62% to max: 97%) and 55 ± 8% (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 0.5 ± 0.13 (n = 124), 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.

Electrical fields induced inside the rat brain with skin, skull, and dural placements of the current injection electrode.

Transcranial electrical stimulation (tES) is rapidly becoming an indispensable clinical tool with its different forms. Animal data are crucially needed for better understanding of the underlying mechanisms of tES. For reproducibility of results in animal experiments, the electric fields (E-Fields) inside the brain parenchyma induced by the injected currents need to be predicted accurately. In this study, we measured the electrical fields in the rat brain perpendicular to the brain surface, i.e. vertical electric field (VE-field), when the stimulation electrode was placed over the skin, skull, or dura mater through a craniotomy hole. The E-field attenuation through the skin was a few times larger than that of the skull and the presence of skin substantially reduced the VE-field peak at the cortical surface near the electrode. The VE-field declined much quicker in the gray matter underneath the pial surface than it did in the white matter, and thus the large VE-fields were contained mostly in the gray matter. The transition at the gray/white matter border caused a significant peak in the VE-field, as well as at other local inhomogeneties. A conductivity value of 0.57 S/m is predicted as a global value for the whole brain by matching our VE-field measurements to the field profile given by analytical equations for volume conductors. Finally, insertion of the current return electrode into the shoulder, submandibular, and hind leg muscles had virtually no effects on the measured E-field amplitudes in the cortex underneath the epidural electrodes.

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 (R 2) 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.

Electric Fields Induced By Transcutaneous And Intracranial Current Injections In The Rat Brain.

As a non-invasive brain stimulation technique, transcranial electrical stimulation (TES) and specifically the transcranial direct current stimulation (tDCS) has gained popularity in recent years for treatment of a wide variety of cognitive and neurological disorders. Recent studies have shown that TES can alter the motor cortex excitability. Animal studies to demonstrate the underlying mechanisms of TES are clearly lacking in literature. Clinical studies have agreed on the critical role of the current intensity and the montage of the electrodes for the treatment to be effective. In this study, we used a rat model for in vivo investigation of the vertical electrical (E) field distribution due to electrodes placed over the skin and through a craniotomy hole. A mono-phasic current pulse was used as a substitute for DC currents by taking advantage of primarily resistive properties of the brain tissue at low frequencies. The electrical potentials induced by the current pulses were recorded with penetrations at 0mm, 2mm, and 4mm away from the stimulation electrode. The results showed that the E-field was maximum immediately under the anodic electrode and decreased both in the vertical and horizontal directions rapidly by distance. The magnitude of the electric field varied from tens of mV/mm to a fraction of mV/mm by distance for a 100 µ A stimulus amplitude. The results also show that the E-field amplitudes and distribution strongly depend on whether the stimulus electrode is placed over the skin or into a craniotomy hole.

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 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.