Biomimetic Peripheral Nerve Stimulation Promotes the Rat Hindlimb Motion Modulation in Stepping: An Experimental Analysis.

Peripheral nerve stimulation is an effective neuromodulation method in patients with lower extremity movement disorders caused by stroke, spinal cord injury, or other diseases. However, most current studies on rehabilitation using sciatic nerve stimulation focus solely on ankle motor regulation through stimulation of common peroneal and tibial nerves. Using the electrical nerve stimulation method, we here achieved muscle control via different sciatic nerve branches to facilitate the regulation of lower limb movements during stepping and standing. A map of relationships between muscles and nerve segments was established to artificially activate specific nerve fibers with the biomimetic stimulation waveform. Then, characteristic curves depicting the relationship between neural electrical stimulation intensity and joint control were established. Finally, by testing the selected stimulation parameters in anesthetized rats, we confirmed that single-cathode extraneural electrical stimulation could activate combined movements to promote lower limb movements. Thus, this method is effective and reliable for use in treatment for improving and rehabilitating lower limb motor dysfunction.

Neural Decoding for Intracortical Brain-Computer Interfaces.

Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain-computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.

Time-Varying Functional Connectivity of Rat Brain during Bipedal Walking on Unexpected Terrain.

The cerebral cortex plays an important role in human and other animal adaptation to unpredictable terrain changes, but little was known about the functional network among the cortical areas during this process. To address the question, we trained 6 rats with blocked vision to walk bipedally on a treadmill with a random uneven area. Whole-brain electroencephalography signals were recorded by 32-channel implanted electrodes. Afterward, we scan the signals from all rats using time windows and quantify the functional connectivity within each window using the phase-lag index. Finally, machine learning algorithms were used to verify the possibility of dynamic network analysis in detecting the locomotion state of rats. We found that the functional connectivity level was higher in the preparation phase compared to the walking phase. In addition, the cortex pays more attention to the control of hind limbs with higher requirements for muscle activity. The level of functional connectivity was lower where the terrain ahead can be predicted. Functional connectivity bursts after the rat accidentally made contact with uneven terrain, while in subsequent movement, it was significantly lower than normal walking. In addition, the classification results show that using the phase-lag index of multiple gait phases as a feature can effectively detect the locomotion states of rat during walking. These results highlight the role of the cortex in the adaptation of animals to unexpected terrain and may help advance motor control studies and the design of neuroprostheses.

The effects of aerobic exercise combined with resistance training on inflammatory factors and heart rate variability in middle-aged and elderly women with type 2 diabetes mellitus.

OBJECTIVE: This study investigated the effects of aerobic exercise combined with resistance training on serum inflammatory factors and heart rate variability (HRV) in women with type 2 diabetes mellitus (T2DM). METHODS: A total of 30 patients with diabetic cardiovascular autonomic neuropathy (DCAN) were randomly divided into a control group (n = 15) and an exercise group (n = 15). The control group was treated with routine hypoglycemic drugs, while the exercise group was treated with routine hypoglycemic drugs + resistance training (AE + RT). The levels of fasting plasma glucose (FBG), two-hour plasma glucose (2hPG), serum inflammatory factors C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) were measured before and after the intervention. The HRV was evaluated by 24-h ambulatory electrocardiogram. RESULTS: After the intervention, the levels of FBG, 2hPG, serum inflammatory factors, IL-6 and TNF-α in the exercise group were significantly lower than those in the control group (p < .05) with no significant differences in serum CRP (p > .05). After the intervention, the HRV time domain and frequency domain indexes in the two groups were significantly improved compared with those before the exercise experiment (p < .01) and with no significant difference in (lnlf) (p > .05). The time-domain indexes, i.e., SDNN and RMSSD, as well as the frequency domain index, i.e., (lnhf), were significantly higher in the exercise group than in the control group, whereas lnlf/lnhf were significantly lower than those in the control group (p < .05). CONCLUSIONS: Compared with routine hypoglycemic drug therapy, combining aerobic exercise and resistance training helped to reduce the level of blood glucose and serum inflammatory factors in T2DM patients with DCAN, and improved autonomic nerve function.

Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing.

Robust classification of natural hand grasp type based on electromyography (EMG) still has some shortcomings in the practical prosthetic hand control, owing to the influence of dynamic arm position changing during hand actions. This study provided a framework for robust hand grasp type classification during dynamic arm position changes, improving both the "hardware" and "algorithm" components. In the hardware aspect, co-located synchronous EMG and force myography (FMG) signals are adopted as the multi-modal strategy. In the algorithm aspect, a sequential decision algorithm is proposed by combining the RNN-based deep learning model with a knowledge-based post-processing model. Experimental results showed that the classification accuracy of multi-modal EMG-FMG signals was increased by more than 10% compared with the EMG-only signal. Moreover, the classification accuracy of the proposed sequential decision algorithm improved the accuracy by more than 4% compared with other baseline models when using both EMG and FMG signals.

Effect of neuromorphic transcutaneous electrical nerve stimulation (nTENS) of cortical functional networks on tactile perceptions: an event-related electroencephalogram study.

Background.Transcutaneous electrical nerve stimulation (TENS) is generally applied for tactile feedback in the field of prosthetics. The distinct mechanisms of evoked tactile perception between stimulus patterns in conventional TENS (cTENS) and neuromorphic TENS (nTENS) are relatively unknown. This is the first study to investigate the neurobiological effect of nTENS for cortical functional mechanism in evoked tactile perception.Methods.Twenty-one healthy participants were recruited in this study. Electroencephalogram (EEG) was recorded while the participants underwent a tactile discrimination task. One cTENS pattern (square pattern) and two nTENS patterns (electromyography and single motor unit patterns) were applied to evoke tactile perception in four fingers, including the right and left index and little fingers. EEG was preprocessed and somatosensory-evoked potentials (SEPs) were determined. Then, source-level functional networks based on graph theory were evaluated, including clustering coefficient, path length, global efficiency, and local efficiency in six frequency bands.Main results.Behavioral results suggested that the single motor units (SMUs) pattern of nTENS was the most natural tactile perception. SEPs results revealed that SMU pattern exhibited significant shorter latency in P1 and N1 components than the other patterns, while nTENS patterns have significantly longer latency in P3 component than cTENS pattern. Cortical functional networks showed that the SMU pattern had the lowest short path and highest efficiency in beta and gamma bands.Conclusion.This study highlighted that distinct TENS patterns could affect brain activities. The new characteristics in tactile manifestation of nTENS would provide insights for the application of tactile perception restoration.

Subjective Well-being of Special Education Teachers in China: The Relation of Social Support and Self-Efficacy.

In order to explore the relationship of social support, self-efficacy, and subjective well-being of special education teachers in China, 496 teachers from 67 special education schools were surveyed by questionnaire. We found that (1) the subjective well-being of special education teachers in China was in the medial level. (2) There were significant differences in subjective well-being level among teachers of different genders, teacher position, education background, and teaching age. Male teachers were of higher subjective well-being; subjective well-being of head teachers was lower than those were not head teachers; teachers with the educational background of postgraduate were of higher relaxation and tension than those with junior college educational background; the control scores of emotion and behavior of teachers with teaching age of 3 years and below were significantly lower than those of teachers with teaching age of more than 10 years. (3) Self-efficacy played a partially mediating role in the relationship between social support and subjective well-being of special education teachers. Suggestions to improve the subjective well-being of special education teachers were discussed in the article.

[Variation Characteristics of Ambient Volatile Organic Compounds (VOCs) Volume Fraction During Hangzhou COVID-19 Period].

A continuous observation campaign was carried out with the Syntech Spectras GC955 volatile organics online monitoring system from December 1, 2019 to March 31, 2020 during the COVID-19 period in Hangzhou. Composition characteristics, diurnal variation, and atmospheric chemical reactivity of VOCs were analyzed. The results showed that φ(total VOCs) were the highest before the COVID-19 pandemic in different sites and the lowest during the first response period. The φ(total VOCs) at night was higher than that during the day. The daily variation in Wolongqiao φ(total VOCs) was less than that in Xiasha. The daily variation in φ(total VOCs) during the first level response period was less than that during the other three periods. The diurnal variation in the φ (total VOCs) in Xiasha showed a "V" shape, and that in Wolongqiao showed a typical bimodal structure. The OFP in Xiasha was higher than that in Wolongqiao. The OFP were the highest at the two sites before the COVID-19 pandemic. The OFP was the lowest during the first response period in Xiasha and the lowest during the second response period in Wolongqiao. The OFP of aromatics and olefins was higher, and the OFP of alkynes was the lowest in Xiasha. The OFP of olefin in Wolongqiao was much higher than that of the other three components, followed by alkane and alkyne.

Multisite Simultaneous Neural Recording of Motor Pathway in Free-Moving Rats.

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.

Neurons in Primary Motor Cortex Encode External Perturbations during an Orientation Reaching Task.

When confronting an abrupt external perturbation force during movement, subjects continuously adjust their behaviors to adapt to changes. Such adaptation is of great importance for realizing flexible motor control in varied environments, but the potential cortical neuronal mechanisms behind it have not yet been elucidated. Aiming to reveal potential neural control system compensation for external disturbances, we applied an external orientation perturbation while monkeys performed an orientation reaching task and simultaneously recorded the neural activity in the primary motor cortex (M1). We found that a subpopulation of neurons in the primary motor cortex specially created a time-locked activity in response to a "go" signal in the adaptation phase of the impending orientation perturbation and did not react to a "go" signal under the normal task condition without perturbation. Such neuronal activity was amplified as the alteration was processed and retained in the extinction phase; then, the activity gradually faded out. The increases in activity during the adaptation to the orientation perturbation may prepare the system for the impending response. Our work provides important evidence for understanding how the motor cortex responds to external perturbations and should advance research about the neurophysiological mechanisms underlying motor learning and adaptation.

Spinal Cord Injury-Induced Changes in Encoding and Decoding of Bipedal Walking by Motor Cortical Ensembles.

Recent studies have shown that motor recovery following spinal cord injury (SCI) is task-specific. However, most consequential conclusions about locomotor functional recovery from SCI have been derived from quadrupedal locomotion paradigms. In this study, two monkeys were trained to perform a bipedal walking task, mimicking human walking, before and after T8 spinal cord hemisection. Importantly, there is no pharmacological therapy with nerve growth factor for monkeys after SCI; thus, in this study, the changes that occurred in the brain were spontaneous. The impairment of locomotion on the ipsilateral side was more severe than that on the contralateral side. We used information theory to analyze single-cell activity from the left primary motor cortex (M1), and results show that neuronal populations in the unilateral primary motor cortex gradually conveyed more information about the bilateral hindlimb muscle activities during the training of bipedal walking after SCI. We further demonstrated that, after SCI, progressively expanded information from the neuronal population reconstructed more accurate control of muscle activity. These results suggest that, after SCI, the unilateral primary motor cortex could gradually regain control of bilateral coordination and motor recovery and in turn enhance the performance of brain-machine interfaces.

Different Modes of Low-Frequency Focused Ultrasound-Mediated Attenuation of Epilepsy Based on the Topological Theory.

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.

Low-Intensity Focused Ultrasound-Mediated Attenuation of Acute Seizure Activity Based on EEG Brain Functional Connectivity.

(1) Background: Ultrasound has been used for noninvasive stimulation and is a promising technique for treating neurological diseases. Epilepsy is a common neurological disorder, that is attributed to uncontrollable abnormal neuronal hyperexcitability. Abnormal synchronized activities can be observed across multiple brain regions during a seizure. (2) Methods: we used low-intensity focused ultrasound (LIFU) to sonicate the brains of epileptic rats, analyzed the EEG functional brain network to explore the effect of LIFU on the epileptic brain network, and continued to explore the mechanism of ultrasound neuromodulation. LIFU was used in the hippocampus of epileptic rats in which a seizure was induced by kainic acid. (3) Results: By comparing the brain network characteristics before and after sonication, we found that LIFU significantly impacted the functional brain network, especially in the low-frequency band. The brain network connection strength across multiple brain regions significantly decreased after sonication compared to the connection strength in the control group. The brain network indicators (the path length, clustering coefficient, small-worldness, local efficiency and global efficiency) all changed significantly in the low-frequency. (4) Conclusions: These results revealed that LIFU could reduce the network connections of epilepsy circuits and change the structure of the brain network at the whole-brain level.

Reduction in post-spinal cord injury spasticity by combination of peripheral nerve grafting and acidic fibroblast growth factor infusion in monkeys.

OBJECTIVE: Spasticity is a frequent complication after spinal cord injury (SCI), but the existing therapies provide only limited relief and are associated with adverse reactions. Therefore, we aimed to develop a novel strategy to ameliorate the spasticity induced by SCI. METHODS: This nonrandomized controlled study used a repeated measurement design. The study involved four monkeys, two of which served as controls and only underwent spinal cord hemisection surgery at the T8 spine level. The other two monkeys underwent transplantation of sural nerve segments into the injured sites and long-term infusion of acidic fibroblast growth factor (aFGF). All monkeys received postoperative exercise training and therapy. RESULTS: The combined therapy substantially reduced the spasticity in leg muscle tone, patella tendon reflex, and fanning of toes. Although all monkeys showed spontaneous recovery of function over time, the recovery in the controls reached a plateau and started to decline after 11 weeks. CONCLUSIONS: The combination of peripheral nerve grafting and aFGF infusion may serve as a complementary approach to reduce the signs of spasticity in patients with SCI.

Rat Locomotion Detection Based on Brain Functional Directed Connectivity from Implanted Electroencephalography Signals.

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.

Association between cortical thickness and distinct vascular cognitive impairment and dementia in patients with white matter lesions.

NEW FINDINGS: What is the central question of this study? White matter lesions (WMLs) are a brain disease characterized by altered brain structural and functional connectivity, but findings have shown an inconsistent pattern: are there distinct cortical thickness changes in patients with WMLs subtypes? What is the main finding and its importance? Patients with WMLs with non-dementia vascular cognitive impairment and WMLs with vascular dementia showed distinct pathophysiology in cortical thickness. These neural correlates of WMLs should be considered in future treatment. ABSTRACT: The effect of cortical thickness on white matter lesions (WMLs) in patients with distinct vascular cognitive impairments is relatively unknown. This study investigated the correlation between cortical thickness and vascular cognitive manifestations. WML patients and healthy controls from Beijing Tiantan Hospital between 2014 and 2018 were included. The patients were further divided into two subgroups, namely WMLs with non-dementia vascular cognitive impairment (WML-VCIND) and WMLs with vascular dementia (WML-VaD) according to the Clinical Dementia Rating (CDR) scale and the Beijing version of the Montreal Cognitive Assessment (MoCA). Changes in cortical thickness were calculated using FreeSurfer. Pearson's correlation analysis was performed to explore the relationship between cognitive manifestations and cortical thickness in WML patients. Forty-five WML patients and 23 healthy controls were recruited. The WML group exhibited significant difference in cortical thickness compared to the control group. Significantly decreased cortical thickness in the middle and superior frontal gyri, middle temporal gyrus, angular gyrus and insula was found in the WML-VaD versus WML-VCIND subgroup. Cortical thickness deficits of the left caudal middle frontal gyrus (r = 0.451, P = 0.002), left rostral middle frontal gyrus (r = 0.514, P < 0.001), left superior frontal gyrus (r = 0.410, P = 0.006), right middle temporal gyrus (r = 0.440, P = 0.003), right pars triangularis (r = 0.462, P = 0.002), right superior frontal gyrus (r = 0.434, P = 0.004) and right insula (r = 0.499, P = 0.001) were positively correlated with the MoCA score in WML patients. The specific pattern of cortical thickness deficits in the WML-VaD subgroup revealed the pathophysiology of WMLs, which should be considered in future treatment of WMLs.

A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model.

The loss of mobility function and sensory information from the arm, hand, and fingertips hampers the activities of daily living (ADL) of patients. A modern bionic prosthetic hand can compensate for the lost functions and realize multiple degree of freedom (DoF) movements. However, the commercially available prosthetic hands usually have limited DoFs due to limited sensors and lack of stable classification algorithms. This study aimed to propose a controller for finger joint angle estimation by surface electromyography (sEMG). The sEMG data used for training were gathered with the Myo armband, which is a commercial EMG sensor. Two features in the time domain were extracted and fed into a nonlinear autoregressive model with exogenous inputs (NARX). The NARX model was trained with pre-selected parameters using the Levenberg-Marquardt algorithm. Comparing with the targets, the regression correlation coefficient (R) of the model outputs was more than 0.982 over all test subjects, and the mean square error was less than 10.02 for a signal range in arbitrary units equal to [0, 255]. The study also demonstrated that the proposed model could be used in daily life movements with good accuracy and generalization abilities.

Unexpected Terrain Induced Changes in Cortical Activity in Bipedal-Walking Rats.

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 ß 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 ß (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.

Electrocortical activity in freely walking rats varies with environmental conditions.

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 ß 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.

Feature-Selection-Based Transfer Learning for Intracortical Brain-Machine Interface Decoding.

The time spent in collecting current samples for decoder calibration and the computational burden brought by high-dimensional neural recordings remain two challenging problems in intracortical brain-machine interfaces (iBMIs). Decoder calibration optimization approaches have been proposed, and neuron selection methods have been used to reduce computational burden. However, few methods can solve both problems simultaneously. In this article, we present a symmetrical-uncertainty-based transfer learning (SUTL) method that combines transfer learning with feature selection. The proposed method uses symmetrical uncertainty to quantitatively measure three indices for feature selection: stationarity, importance and redundancy of the feature. By selecting the stationary features, the disparities between the historical data and current data can be diminished, and the historical data can be effectively used for decoder calibration, thereby reducing the demand for current data. After selecting the important and non-redundant features, only the channels corresponding to them need to work; thus, the computational burden is reduced. The proposed method was tested on neural data recorded from two rhesus macaques to decode the reaching position or grasping gesture. The results showed that the SUTL method diminished the disparities between the historical data and current data, while achieving superior decoding performance with the needs of only ten current samples each category, less than 10% the number of features and 30% the number of neural recording channels. Additionally, unlike most studies on iBMIs, feature selection was implemented instead of neuron selection, and the average decoding accuracy achieved by the former was 6.6% higher.