Restoration of coherent reach-grasp-pull movement via sequential intraneural peripheral nerve stimulation in rats.

Objective.Peripheral nerve stimulation (PNS) has been demonstrated as an effective way to selectively activate muscles and to produce fine hand movements. However, sequential multi-joint upper limb movements, which are critical for paralysis rehabilitation, has not been tested with PNS. Here, we aimed to restore multiple upper limb joint movements through an intraneural interface with a single electrode, achieving coherent reach-grasp-pull movement tasks through sequential stimulation.Approach.A transverse intrafascicular multichannel electrode was implanted under the axilla of the rat's upper limb, traversing the musculocutaneous, radial, median, and ulnar nerves. Intramuscular electrodes were implanted into the biceps brachii (BB), triceps brachii (TB), flexor carpi radialis (FCR), and extensor carpi radialis (ECR) muscles to record electromyographic (EMG) activity and video recordings were used to capture the kinematics of elbow, wrist, and digit joints. Charge-balanced biphasic pulses were applied to different channels to recruit distinct upper limb muscles, with concurrent recording of EMG signals and joint kinematics to assess the efficacy of the stimulation. Finally, a sequential stimulation protocol was employed by generating coordinated pulses in different channels.Main results.BB, TB, FCR and ECR muscles were selectively activated and various upper limb movements, including elbow flexion, elbow extension, wrist flexion, wrist extension, digit flexion, and digit extension, were reliably generated. The modulation effects of stimulation parameters, including pulse width, amplitude, and frequency, on induced joint movements were investigated and reach-grasp-pull movement was elicited by sequential stimulation.Significance.Our results demonstrated the feasibility of sequential intraneural stimulation for functional multi-joint movement restoration, providing a new approach for clinical rehabilitation in paralyzed patients.

Neuronal representation of bimanual arm motor imagery in the motor cortex of a tetraplegia human, a pilot study.

Introduction: How the human brain coordinates bimanual movements is not well-established. Methods: Here, we recorded neural signals from a paralyzed individual's left motor cortex during both unimanual and bimanual motor imagery tasks and quantified the representational interaction between arms by analyzing the tuning parameters of each neuron. Results: We found a similar proportion of neurons preferring each arm during unimanual movements, however, when switching to bimanual movements, the proportion of contralateral preference increased to 71.8%, indicating contralateral lateralization. We also observed a decorrelation process for each arm's representation across the unimanual and bimanual tasks. We further confined that these changes in bilateral relationships are mainly caused by the alteration of tuning parameters, such as the increased bilateral preferred direction (PD) shifts and the significant suppression in bilateral modulation depths (MDs), especially the ipsilateral side. Discussion: These results contribute to the knowledge of bimanual coordination and thus the design of cutting-edge bimanual brain-computer interfaces.

Biological Cardiac Patch Based on Extracellular Vesicles and Extracellular Matrix for Regulating Injury-Related Microenvironment and Promoting Cardiac Tissue Recovery.

Cardiac patches are widely investigated to repair or regenerate diseased and aging cardiac tissues. While numerous studies looked into engineering the biochemical/biomechanical/cellular microenvironment and components in the heart tissue, the changes induced by cardiac patches and how they should be controlled to promote cardiac tissue repair/regeneration remains an important yet untapped direction, especially immunological responses. In this study, we designed and fabricated a bilaminated cardiac patch based on extracellular matrix (ECM) materials loaded with the extracellular vesicles (EVs) derived from mesenchymal stromal cells. The function of the biological material to modulate the injury-related microenvironment in a cardiac infarction model in mice was investigated. The study showed that the treatment of EV-ECM patches to the infarcted area increased the level of immunomodulatory major histocompatibility complex class IIlo macrophages in the early stage of myocardial injury to mitigate excessive inflammatory responses due to injury. The intensity of the acquired proinflammatory immune response in systemic immune organs was reduced. Further analyses indicated that the EV-ECM patches exhibited proangiogenic functions and decreased the infarct size with improved cardiac recovery in mice. The study provided insights into shaping the injury-related microenvironment through the incorporation of extracellular vesicles into cardiac patches, and the EV-ECM material is a promising design paradigm to improve the function of cardiac patches to treat myocardial injuries and diseases.

Endothelial cell membrane-based biosurface for targeted delivery to acute injury: analysis of leukocyte-mediated nanoparticle transportation.

Mimicking and leveraging biological structures and materials provide important approaches to develop functional vehicles for drug delivery. Taking advantage of the affinity and adhesion between the activated endothelial cells and innate immune cells during inflammatory responses, hybrid polyester nanoparticles coated with endothelial cell membranes (EM-P) containing adhesion molecules were fabricated and their capability as vehicles to travel to the acute injury sites through leukocyte-mediated processes was investigated. The in vivo studies and quantitative analyses performed through the lung-inflammation mouse models demonstrated that the EM-Ps preferentially interacted with the neutrophils and monocytes in the circulation and the cellular membrane-based biosurface improved the nanoparticle transportation to the inflamed lung possibly via the motility of neutrophils. Utilizing the transgenic zebrafish model, the leukocyte-mediated transportation and biodistribution of EM-Ps were further visualized in real time at the whole-organism level. Endothelial membranes provided a new biosurface for developing biomimetic vehicles to allow the immune cell-mediated transportation and may enable advanced systems for active and highly efficient drug delivery.

Fully autonomous mouse behavioral and optogenetic experiments in home-cage.

Goal-directed behaviors involve distributed brain networks. The small size of the mouse brain makes it amenable to manipulations of neural activity dispersed across brain areas, but existing optogenetic methods serially test a few brain regions at a time, which slows comprehensive mapping of distributed networks. Laborious operant conditioning training required for most experimental paradigms exacerbates this bottleneck. We present an autonomous workflow to survey the involvement of brain regions at scale during operant behaviors in mice. Naive mice living in a home-cage system learned voluntary head-fixation (>1 hr/day) and performed difficult decision-making tasks, including contingency reversals, for 2 months without human supervision. We incorporated an optogenetic approach to manipulate activity in deep brain regions through intact skull during home-cage behavior. To demonstrate the utility of this approach, we tested dozens of mice in parallel unsupervised optogenetic experiments, revealing multiple regions in cortex, striatum, and superior colliculus involved in tactile decision-making.

Mesenchymal stromal exosome-functionalized scaffolds induce innate and adaptive immunomodulatory responses toward tissue repair.

Designing scaffolds capable of inducing and guiding appropriate immune responses holds promise for tissue repair/regeneration. Biofunctional scaffolds were here prepared by immobilizing mesenchymal stromal exosomes onto fibrous polyester materials and allowed cell-mediated delivery of membrane-bound vesicles. Quantitative cell-level analyses revealed that immune cells dominated the uptake of exosomes from scaffolds in vivo, with materials and exosomes acting as the recruiter and trainer for immune cells, respectively, to synergistically promote beneficial macrophage and regulatory T cell responses in skin wounds in mice. Adaptive T helper cell responses were found active in remote immune organs, and exosome-laden scaffolds facilitated tissue repair in large skin injury models. This study demonstrated important mechanisms involved in local and systemic immune responses to biological implants, and understanding tissue-reparative immunomodulation may guide the design of new biofunctional scaffolds.

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace.

Reaching and grasping are highly-coupled movements, and their underlying neural dynamics have been widely studied in the last decade. To distinguish reaching and grasping encodings, it is essential to present different object identities independent of their positions. Presented here is the design of an automatic apparatus that is assembled with a turning table and three-dimensional (3D) translational device to achieve this goal. The turning table switches different objects corresponding to different grip types while the 3D translational device transports the turning table in 3D space. Both are driven independently by motors so that the target position and object are combined arbitrarily. Meanwhile, wrist trajectory and grip types are recorded via the motion capture system and touch sensors, respectively. Furthermore, representative results that demonstrate successfully trained monkey using this system are described. It is expected that this apparatus will facilitate researchers to study kinematics, neural principles, and brain-machine interfaces related to upper limb function.

An automated behavioral apparatus to combine parameterized reaching and grasping movements in 3D space.

BACKGROUND: The neural principles underlying reaching and grasping movements have been studied extensively in primates for decades. However, few experimental apparatuses have been developed to enable a flexible combination of reaching and grasping in one task in three-dimensional (3D) space. NEW METHOD: By combining a custom turning table with a 3D translational device, we have developed a highly flexible apparatus that enables the subject to reach multiple positions in 3D space, and grasp differently shaped objects with multiple grip types in each position. Meanwhile, hand trajectory and grip types can be recorded via optical motion tracking cameras and touch sensors, respectively. RESULTS: We have used the apparatus to successfully train a macaque monkey to accomplish a visually-guided reach-to-grasp task, in which, six objects, fixed on the turning table, were grasped appropriately when they were transported to multiple positions in 3D space. A preliminary analysis of neural signals recorded in primary motor cortex, shows that plenty of neurons exhibit significant tuning to both target position and grip type. COMPARISON WITH EXISTING METHOD(S): Our apparatus realizes an arbitrary combination of parameterized reaching and grasping movements in a single task, which were usually separated or fixed in other systems. Meanwhile, the apparatus has high expansibility in terms of dynamic range, object shapes and applicable subjects. CONCLUSIONS: The apparatus provides a valuable platform to study upper limb functions in behavioral and neurophysiological studies, and may facilitate simultaneous reconstruction of reaching and grasping movements in brain-machine interfaces (BMIs).

Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task.

We publish two electrophysiological datasets recorded in motor cortex of two macaque monkeys during an instructed delayed reach-to-grasp task, using chronically implanted 10-by-10 Utah electrode arrays. We provide a) raw neural signals (sampled at 30 kHz), b) time stamps and spike waveforms of offline sorted single and multi units (93/49 and 156/19 SUA/MUA for the two monkeys, respectively), c) trial events and the monkey's behavior, and d) extensive metadata hierarchically structured via the odML metadata framework (including quality assessment post-processing steps, such as trial rejections). The dataset of one monkey contains a simultaneously saved record of the local field potential (LFP) sampled at 1 kHz. To load the datasets in Python, we provide code based on the Neo data framework that produces a data structure which is annotated with relevant metadata. We complement this loading routine with an example code demonstrating how to access the data objects (e.g., raw signals) contained in such structures. For Matlab users, we provide the annotated data structures as mat files.

Neural synergies for controlling reach and grasp movement in macaques.

It has been suggested that the brain adopts a simplified strategy to coordinate a large number of degrees of freedom in motor control. Synergies have been proposed as a strategy to produce movements by recruitment of a small number of fixed modular patterns. However, there is no direct support for a synergistic organization of the brain itself. In this study, we recorded neural activities from the dorsal premotor cortex (PMd) of monkeys trained to reach and grasp differently shaped objects (grasping task) or the same object in different positions (reaching task). Non-negative matrix factorization (NNMF) was applied to the neural data to extract neural synergies, whose functional roles were verified in several ways. We found that motor cortex used similar neural synergies for grasping different objects; combining only a few of the synergies accounted for most of the variance in the original data. When used for single-trial task decoding, the synergy coefficients performed as well and robustly as the original data in both tasks. The synergy amplitudes for each unit were significantly correlated with the corresponding neuron's firing rate. In addition, we also observed synergies shared between tasks and task-specific synergies, as shown before for muscle synergies. Altogether, we demonstrated that neural synergies are effective in describing neural population activity during reach to grasp movements and provide a new tool for interpreting neural data for movement control.

Prior Knowledge of Target Direction and Intended Movement Selection Improves Indirect Reaching Movement Decoding.

Objective. Previous studies have demonstrated that target direction information presented by the dorsal premotor cortex (PMd) during movement planning could be incorporated into neural decoder for achieving better decoding performance. It is still unknown whether the neural decoder combined with only target direction could work in more complex tasks where obstacles impeded direct reaching paths. Methods. In this study, spike activities were collected from the PMd of two monkeys when performing a delayed obstacle-avoidance task. We examined how target direction and intended movement selection were encoded in neuron population activities of the PMd during movement planning. The decoding performances of movement trajectory were compared for three neural decoders with no prior knowledge, or only target direction, or both target direction and intended movement selection integrated into a mixture of trajectory model (MTM). Results. We found that not only target direction but also intended movement selection was presented in neural activities of the PMd during movement planning. It was further confirmed by quantitative analysis. Combined with prior knowledge, the trajectory decoder achieved the best performance among three decoders. Conclusion. Recruiting prior knowledge about target direction and intended movement selection extracted from the PMd could enhance the decoding performance of hand trajectory in indirect reaching movement.

Mapping Horizontal Spread of Activity in Monkey Motor Cortex Using Single Pulse Microstimulation.

Anatomical studies have demonstrated that distant cortical points are interconnected through long range axon collaterals of pyramidal cells. However, the functional properties of these intrinsic synaptic connections, especially their relationship with the cortical representations of body movements, have not been systematically investigated. To address this issue, we used multielectrode arrays chronically implanted in the motor cortex of two rhesus monkeys to analyze the effects of single-pulse intracortical microstimulation (sICMS) applied at one electrode on the neuronal activities recorded at all other electrodes. The temporal and spatial distribution of the evoked responses of single and multiunit activities was quantified to determine the properties of horizontal propagation. The typical responses were characterized by a brief excitatory peak followed by inhibition of longer duration. Significant excitatory responses to sICMS could be evoked up to 4 mm away from the stimulation site, but the strength of the response decreased exponentially and its latency increased linearly with the distance. We then quantified the direction and strength of the propagation in relation to the somatotopic organization of the motor cortex. We observed that following sICMS the propagation of neural activity is mainly directed rostro-caudally near the central sulcus but follows medio-lateral direction at the most anterior electrodes. The fact that these interactions are not entirely symmetrical may characterize a critical functional property of the motor cortex for the control of upper limb movements. Overall, these results support the assumption that the motor cortex is not functionally homogeneous but forms a complex network of interacting subregions.

Synthesis and characterization of cell-microenvironment-sensitive leakage-free gold-shell nanoparticles with the template of interlayer-crosslinked micelles.

Novel gold-shell nanoparticles (pH-GSNPs) are designed for the first time, which exhibit drug leakage-free behavior in a physiological environment, while achieving rapid drug release and remarkable aggregation for the nanogold interlayer of pH-GSNPs to shift their absorption to far-red and NIR as a photothermal agent in the intracellular microenvironment.

No association of single nucleotide polymorphisms involved in GHRL and GHSR with cancer risk: a meta-analysis.

BACKGROUND: Ghrelin was associated with several of cancers. The conflict results of SNPs with GHRL and GHSR gene were demonstrated in different studies. Thus, this meta-analysis is to evaluate the associations. METHODS: Systematic literature search was done on PubMed database up to October 2013. We used odds ratios (ORs) with 95% confidence intervals (CIs) to assess the strength of the association by a fixed-effect model and a random-effect model. RESULTS: A total of 7 studies, which included 3 studies for breast cancer, 2 for colorectal cancer, 1 for hepatocellular carcinoma, 1 for esophageal cancer and 1 for Non-Hodgkin lymphoma. When analyzed all the GHRL SNPs with all kinds of cancers, there was significantly difference with cancer patients compared with controls (Recessive model: OR 0.938, 95% CI 0.890-0.989, p=0.017), while no significant difference was existed in the additive model (OR 0.9903, 95% CI 0.957-1.024, p=0.558) and dominant model (OR 1.014, 95% CI 0.970-1.061, p=0.536). When analyzed all the GHSR SNPs with all kinds of cancers, no significant difference was observed. CONCLUSION: Our results suggest that the SNP with GHRL and GHSR might be weaker association with cancer risk, especially with breast cancer risk.

Distinct neural patterns enable grasp types decoding in monkey dorsal premotor cortex.

OBJECTIVE: Recent studies have shown that dorsal premotor cortex (PMd), a cortical area in the dorsomedial grasp pathway, is involved in grasp movements. However, the neural ensemble firing property of PMd during grasp movements and the extent to which it can be used for grasp decoding are still unclear. APPROACH: To address these issues, we used multielectrode arrays to record both spike and local field potential (LFP) signals in PMd in macaque monkeys performing reaching and grasping of one of four differently shaped objects. MAIN RESULTS: Single and population neuronal activity showed distinct patterns during execution of different grip types. Cluster analysis of neural ensemble signals indicated that the grasp related patterns emerged soon (200-300 ms) after the go cue signal, and faded away during the hold period. The timing and duration of the patterns varied depending on the behaviors of individual monkey. Application of support vector machine model to stable activity patterns revealed classification accuracies of 94% and 89% for each of the two monkeys, indicating a robust, decodable grasp pattern encoded in the PMd. Grasp decoding using LFPs, especially the high-frequency bands, also produced high decoding accuracies. SIGNIFICANCE: This study is the first to specify the neuronal population encoding of grasp during the time course of grasp. We demonstrate high grasp decoding performance in PMd. These findings, combined with previous evidence for reach related modulation studies, suggest that PMd may play an important role in generation and maintenance of grasp action and may be a suitable locus for brain-machine interface applications.

Information analysis on neural tuning in dorsal premotor cortex for reaching and grasping.

Previous studies have shown that the dorsal premotor cortex (PMd) neurons are relevant to reaching as well as grasping. In order to investigate their specific contribution to reaching and grasping, respectively, we design two experimental paradigms to separate these two factors. Two monkeys are instructed to reach in four directions but grasp the same object and grasp four different objects but reach in the same direction. Activities of the neuron ensemble in PMd of the two monkeys are collected while performing the tasks. Mutual information (MI) is carried out to quantitatively evaluate the neurons' tuning property in both tasks. We find that there exist neurons in PMd that are tuned only to reaching, tuned only to grasping, and tuned to both tasks. When applied with a support vector machine (SVM), the movement decoding accuracy by the tuned neuron subset in either task is quite close to the performance by full ensemble. Furthermore, the decoding performance improves significantly by adding the neurons tuned to both tasks into the neurons tuned to one property only. These results quantitatively distinguish the diversity of the neurons tuned to reaching and grasping in the PMd area and verify their corresponding contributions to BMI decoding.

Local-learning-based neuron selection for grasping gesture prediction in motor brain machine interfaces.

OBJECTIVE: The high-dimensional neural recordings bring computational challenges to movement decoding in motor brain machine interfaces (mBMI), especially for portable applications. However, not all recorded neural activities relate to the execution of a certain movement task. This paper proposes to use a local-learning-based method to perform neuron selection for the gesture prediction in a reaching and grasping task. APPROACH: Nonlinear neural activities are decomposed into a set of linear ones in a weighted feature space. A margin is defined to measure the distance between inter-class and intra-class neural patterns. The weights, reflecting the importance of neurons, are obtained by minimizing a margin-based exponential error function. To find the most dominant neurons in the task, 1-norm regularization is introduced to the objective function for sparse weights, where near-zero weights indicate irrelevant neurons. MAIN RESULTS: The signals of only 10 neurons out of 70 selected by the proposed method could achieve over 95% of the full recording's decoding accuracy of gesture predictions, no matter which different decoding methods are used (support vector machine and K-nearest neighbor). The temporal activities of the selected neurons show visually distinguishable patterns associated with various hand states. Compared with other algorithms, the proposed method can better eliminate the irrelevant neurons with near-zero weights and provides the important neuron subset with the best decoding performance in statistics. The weights of important neurons converge usually within 10-20 iterations. In addition, we study the temporal and spatial variation of neuron importance along a period of one and a half months in the same task. A high decoding performance can be maintained by updating the neuron subset. SIGNIFICANCE: The proposed algorithm effectively ascertains the neuronal importance without assuming any coding model and provides a high performance with different decoding models. It shows better robustness of identifying the important neurons with noisy signals presented. The low demand of computational resources which, reflected by the fast convergence, indicates the feasibility of the method applied in portable BMI systems. The ascertainment of the important neurons helps to inspect neural patterns visually associated with the movement task. The elimination of irrelevant neurons greatly reduces the computational burden of mBMI systems and maintains the performance with better robustness.

Decoding grasp types with high frequency of local field potentials from primate primary dorsal premotor cortex.

Recently, local field potentials (LFPs) have been successfully used to extract information of arm and hand movement in some brain-machine interfaces (BMIs) studies, which suggested that LFPs would improve the performance of BMI applications because of its long-term stability. However, the performance of LFPs in different frequency bands has not been investigated in decoding hand grasp types. Here, the LFPs from the monkey's dorsal premotor cortices were collected by microelectrode array when monkey was performing grip-specific grasp task. A K-nearest neighbor classifier performed on the power spectrum of LFPs was used to decode grasping movements. The decoding powers of LFPs in different frequency bands, channels and trials used for training were also studied. The results show that the broad high frequency band (200-400Hz) LFPs achieved the best performance with classification accuracy reaching over 0.9. It infers that high frequency band LFPs in PMd cortex could be a promising source of control signals in developing functional BMIs for hand grasping.

Decoding wrist kinematics from local field potentials of the ipsilateral primary motor and dorsal premotor cortices.

Local field potentials (LFP) are valuable signals for decoding motor kinematics in brain machine interfaces (BMIs). To take full advantage of LFPs, however, more systematic investigation of the relationship between LFPs and ipsilateral limb movement is required. In this paper we investigated the decoding power of LFPs for the ipsilateral wrist movement from two monkeys performing a 2D center-out task. The results show that LFPs could predict the ipsilateral wrist position and velocity with high accuracy, which is comparable to the accuracy of decoding the contralateral kinematics. Furthermore, similar to contralateral decoding, the low (0.3-5 Hz, 5-15 Hz) and high (100-200 Hz, 200-400 Hz) frequency bands resulted in significantly better decoding performance than the medium frequency bands. These results suggest that ipsilateral LFPs could be used to build better BMIs in similar ways of using contralateral LFPs.

FPGA implementation of hardware processing modules as coprocessors in brain-machine interfaces.

Real-time computation, portability and flexibility are crucial for practical brain-machine interface (BMI) applications. In this work, we proposed Hardware Processing Modules (HPMs) as a method for accelerating BMI computation. Two HPMs have been developed. One is the field-programmable gate array (FPGA) implementation of spike sorting based on probabilistic neural network (PNN), and the other is the FPGA implementation of neural ensemble decoding based on Kalman filter (KF). These two modules were configured under the same framework and tested with real data from motor cortex recording in rats performing a lever-pressing task for water rewards. Due to the parallelism feature of FPGA, the computation time was reduced by several dozen times, while the results are almost the same as those from Matlab implementations. Such HPMs provide a high performance coprocessor for neural signal computation.