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This paper proposes a marker-based motion capture system for rat motion detection and gait analysis. Motion capture in small animals such as rodents is more challenging than in humans because of their small bodies and rapid motion. Existing algorithms have poor applicability in rat motion capture in environments outside the studio. Moreover, gait analysis targeting on the rat is not performed by existing motion capture software. Our method consists of four procedures. First, Region of Interest (ROI) is extracted from the background using the inter-frame difference method and a depth filter. Second, a double-threshold marker detection method is used to detect markers in ROI and a marker shape filter is used to classify the markers. Third, a marker corrector is designed to modify missing and incorrect markers. Finally, a deep learning network is used to analyse the gait trajectory to classify rat as healthy, injured, or rehabilitated. The experimental result shows that marker recognition accuracy is 99.33%, higher than that of most existing software. The validation accuracy of the network is 100% and the loss is 0.0001. This method is conductive to the development of motion capture systems for small animals and research into the gait kinematics of rodents. [ABSTRACT FROM AUTHOR]

Clinical research indicates that the epidural spinal cord stimulation (ESCS) at lumbosacral segment has shown potential for promoting locomotor recovery in patients with incomplete spinal cord injury. However, the underlying neural mechanism needs to be determined by animal experiments. In order to refine experimental protocols, we used a finite element simulation to investigate the activation of nerve fibers in a rat spinal cord model. Our model is composed of a volume conductor model from L1 to S2 spinal segments and the McIntyre–Richard–Grill axon model, which is used to investigate the threshold of selected spinal fibers with different diameters at varied locations and predict the neural responses of any target fibers with bipolar electrode configuration. Mathematical modeling suggests that the electrode-fiber distance may play an important role in the recruitment of nerve fibers, whereas longer pulse width predicted greater activity of spinal root fibers and dorsal column fibers, as well as may exert an effective influence on the motor system by the ability to increase and even “steer” spatial selectivity with deeper penetration into the dorsal columns. The spikes were initiated at sites along the nerve fibers depending on which component was closest to the cathode among the longitudinal part of the fiber, its entrance into spinal cord, or strong bending at the entry. Our simulation results show good agreement with the previous findings from animal studies. It is concluded that the computational ESCS model is a valuable tool to obtain a better insight into the immediately evoked electrophysiological phenomena in animal models, and provides further guidelines for conducting animal experiments to enhance the exploration of basic neural mechanisms. [ABSTRACT FROM AUTHOR]

Simple Summary: Most studies on cortical dynamics during walking require subjects to walk stably on specific terrain. In fact, humans or other animals are often disturbed by an abrupt change in terrains during walking. To study the impact of unexpected terrain on cortical activity, we analyzed the kinematics and electroencephalography (EEG) dynamics of bipedal-walking rats after encountering unexpected terrain. We found that the gait of rats after encountering the unexpected terrain were significantly different from normal walking. Furthermore, the activities of the left and right primary motor areas (M1), the left and right primary somatosensory areas (S1), and the retrosplenial area (RSP) are coupled to gait cycle phase and varied with the terrain conditions. These findings suggest that unexpected terrains induced changes in gait and cortical activity, and provide novel insights into cortical dynamics during walking. Humans and other animals can quickly respond to unexpected terrains during walking, but little is known about the cortical dynamics in this process. To study the impact of unexpected terrains on brain activity, we allowed rats with blocked vision to walk on a treadmill in a bipedal posture and then walk on an uneven area at a random position on the treadmill belt. Whole brain EEG signals and hind limb kinematics of bipedal-walking rats were recorded. After encountering unexpected terrain, the θ band power of the bilateral M1, the γ band power of the left S1, and the θ to γ band power of the RSP significantly decreased compared with normal walking. Furthermore, when the rats left uneven terrain, the β band power of the bilateral M1 and the α band power of the right M1 decreased, while the γ band power of the left M1 significantly increased compared with normal walking. Compared with the flat terrain, the θ to low β (3–20 Hz) band power of the bilateral S1 increased after the rats contacted the uneven terrain and then decreased in the single- or double- support phase. These results support the hypothesis that unexpected terrains induced changes in cortical activity. [ABSTRACT FROM AUTHOR]

Neural interfaces typically focus on one or two sites in the motoneuron system simultaneously due to the limitation of the recording technique, which restricts the scope of observation and discovery of this system. Herein, we built a system with various electrodes capable of recording a large spectrum of electrophysiological signals from the cortex, spinal cord, peripheral nerves, and muscles of freely moving animals. The system integrates adjustable microarrays, floating microarrays, and microwires to a commercial connector and cuff electrode on a wireless transmitter. To illustrate the versatility of the system, we investigated its performance for the behavior of rodents during tethered treadmill walking, untethered wheel running, and open field exploration. The results indicate that the system is stable and applicable for multiple behavior conditions and can provide data to support previously inaccessible research of neural injury, rehabilitation, brain-inspired computing, and fundamental neuroscience. [ABSTRACT FROM AUTHOR]

Motor neuroprosthetics research has focused on reproducing natural limb motions by correlating firing rates of cortical neurons to continuous movement parameters. We propose an alternative system where specific spatial-temporal spike patterns, emerging in tasks, allow detection of classes of behavior with the aid of sophisticated nonlinear classification algorithms. Specifically, we attempt to examine ensemble activity from motor cortical neurons, not to reproduce the action this neural activity normally precedes, but rather to predict an output supervisory command to potentially control a vehicle. To demonstrate the principle, this design approach was implemented in a discrete directional task taking a small number of motor cortical signals (8–10 single units) fed into a support vector machine (SVM) to produce the commands Left and Right. In this study, rats were placed in a conditioning chamber performing a binary paddle pressing task mimicking the control of a wheelchair turning left or right. Four animal subjects (male Spragne-Dawley rats) were able to use such a brain-machine interface (BMI) with an average accuracy of 78% on their first day of exposure. Additionally, one animal continued to use the interface for three consecutive days with an average accuracy over 90%. [ABSTRACT FROM AUTHOR]

Epilepsy is common brain dysfunction, where abnormal synchronized activities can be observed across multiple brain regions. Low-frequency focused pulsed ultrasound has been proven to modulate the epileptic brain network. In this study, we used two modes of low-intensity focused ultrasound (pulsed-wave and continuous-wave) to sonicate the brains of KA-induced epileptic rats, analyzed the EEG functional brain connections to explore their respective effect on the epileptic brain network, and discuss the mechanism of ultrasound neuromodulation. By comparing the brain network characteristics before and after sonication, we found that two modes of ultrasound both significantly affected the functional brain network, especially in the low-frequency band below 12 Hz. After two modes of sonication, the power spectral density of the EEG signals and the connection strength of the brain network were significantly reduced, but there was no significant difference between the two modes. Our results indicated that the ultrasound neuromodulation could effectively regulate the epileptic brain connections. The ultrasound-mediated attenuation of epilepsy was independent of modes of ultrasound. [ABSTRACT FROM AUTHOR]

Previous findings have suggested that the cortex involved in walking control in freely locomotion rats. Moreover, the spectral characteristics of cortical activity showed significant differences in different walking conditions. However, whether brain connectivity presents a significant difference during rats walking under different behavior conditions has yet to be verified. Similarly, whether brain connectivity can be used in locomotion detection remains unknown. To address those concerns, we recorded locomotion and implanted electroencephalography signals in freely moving rats performing two kinds of task conditions (upslope and downslope walking). The Granger causality method was used to determine brain functional directed connectivity in rats during these processes. Machine learning algorithms were then used to categorize the two walking states, based on functional directed connectivity. We found significant differences in brain functional directed connectivity varied between upslope and downslope walking. Moreover, locomotion detection based on brain connectivity achieved the highest accuracy (91.45%), sensitivity (90.93%), specificity (91.3%), and F1-score (91.43%). Specifically, the classification results indicated that connectivity features in the high gamma band contained the most discriminative information with respect to locomotion detection in rats, with the support vector machine classifier exhibiting the most efficient performance. Our study not only suggests that brain functional directed connectivity in rats showed significant differences in various behavioral contexts but also proposed a method for classifying the locomotion states of rat walking, based on brain functional directed connectivity. These findings elucidate the characteristics of neural information interaction between various cortical areas in freely walking rats. [ABSTRACT FROM AUTHOR]

A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn't work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the current frame and match the ID of the objects. The nine-point position correction algorithm is presented to adjust the correctness of the Rat-YOLO result and the predicted results. As the training of deep learning needs more datasets than our experiment, and it is time-consuming to process manual marking, automatic software for generating labeled datasets is proposed under a fixed scene and the labeled datasets are manually verified in term of their correctness. Besides this, in an off-line experiment, a mask is presented to remove the highlight. In this experiment, we select the 500 frames of the data as the training datasets and label these images with the automatic label generating software. A video (of 2892 frames) is tested by the trained Rat model and the accuracy of detecting all the three rats is around 72.545%, however, the Rat-YOLO combining the Kalman Filter and nine-point position correction arithmetic improved the accuracy to 95.194%. [ABSTRACT FROM AUTHOR]

• Limb kinematics of rats differed among with different behavioral contexts. • Rats cortical modulation across walking locomotion varied with behaviors contexts. • The enhanced cortical activity was correlated with increased locomotion complexity. Longstanding theories in the field of neurophysiology have held that walking in rats is an unconscious, rhythmic locomotion that does not require cortical involvement. However, recent studies have suggested that the extent of cortical involvement during walking actually varies depending on the environmental conditions. To determine the impact of environmental conditions on cortical engagement in freely walking rats, we recorded limb kinematics and signals from implanted electroencephalography arrays in rats performing a series of natural behaviors. We found that rat gaits were significantly different across various locomotion terrains (e.g. walking on an upslope vs. downslope). Further, rat forelimbs and hindlimbs showed similar patterns of motion. The results also suggested that rat cortical engagement during walking varied across environmental conditions. Specifically, α band power significantly increased during 30° downslope walking in the posterior parietal, left secondary motor, and left somatosensory clusters. Additionally, during 30° upslope walking, the β band power was greater in the left primary motor and left and right secondary motor sources. Further, rats walking on up- or downslopes of varying steepness were found to have different cortical activities. Compared with 10° downslope walking, α band power was greater during 30° downslope locomotion in the left primary motor and somatosensory sources. These findings support the hypothesis that cortical contribution during walking in rats is influenced by environmental conditions, underlining the importance of goal-directed behaviors for motor function rehabilitation and neuro-prosthetic control in brain-machine interfaces. [ABSTRACT FROM AUTHOR]