Tight junctions between brain endothelial cells are one of the specialized structural components of the blood-brain barrier (BBB) to proteins. Research in the last decade has demonstrated that the integral membrane proteins of cerebral endothelial tight junctions are claudin, occludin, and junctional adhesion molecule (JAM). Altered expression of these tight junction proteins could cause BBB breakdown following brain injury leading to edema. In this study, expression of JAM-A, was analyzed by immunostaining and immunoblotting in the rat cortical cold injury model, a well-characterized in vivo model of BBB breakdown. Temporal and spatial expression of JAM-A was examined at 12 hours, days 2, 4, and 6 post-lesion in cold-injured and control rats. Control rats showed punctate JAM-A immunoreactivity at intervals along the circumference of the endothelial layer at tight junctions where JAM-A colocalized with occludin. A significant decrease in JAM-A expression was noted at the lesion site by immunoblotting at 12 h only. At this time period, lesion vessels showed loss of endothelial JAM-A immunostaining while day 2 onwards, there was recovery of endothelial JAM-A immunoreactivity. Dual labelling for JAM-A and fibronectin showed that only lesion vessels with BBB breakdown to fibronectin at 12 h also showed lack of endothelial JAM-A immunoreactivity supporting the evidence that JAM-A contributes to tight junction integrity.
Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.
In this work, we present a myoelectric interface that extracts natural motor synergies from multi-muscle signals and adapts in real-time with new user inputs. With this unsupervised adaptive myocontrol (UAM) system, optimal synergies for control are continuously co-adapted with changes in user motor control, or as a function of perturbed conditions via online non-negative matrix factorization guided by physiologically informed sparseness constraints in lieu of explicit data labelling. Methods: UAM was tested in a set of virtual target reaching tasks completed by able-bodied and amputee subjects. Tests were conducted under normative and electrode perturbed conditions to gauge control robustness with comparisons to non-adaptive and supervised adaptive myocontrol schemes. Furthermore, UAM was used to interface an amputee with a multi-functional powered hand prosthesis during standardized Clothespin Relocation Tests, also conducted in normative and perturbed conditions. Results: In virtual tests, UAM effectively mitigated performance degradation caused by electrode displacement, affording greater resilience over an existing supervised adaptive system for amputee subjects. Induced electrode shifts also had negligible effect on the real world control performance of UAM with consistent completion times (23.91±1.33 s) achieved across Clothespin Relocation Tests in the normative and electrode perturbed conditions. Conclusion: UAM affords comparable robustness improvements to existing supervised adaptive myocontrol interfaces whilst providing additional practical advantages for clinical deployment. Significance: The proposed system uniquely incorporates neuromuscular control principles with unsupervised online learning methods and presents a working example of a freely co-adaptive bionic interface.
The decomposition of HD-EMG into motor unit (MU) discharge timings permit a detailed window into the motoneuronal manifestation of motor intent. Recently, the feasibility of MU-driven wrist joint angle estimation was preliminarily demonstrated although the influences of certain parameter selections have yet to be fully investigated. Here, a decomposition algorithm was used to predict wrist joint kinematics over three DoFs in a pseudo-online manner. Three separate estimator types were tested and the effects of two key parameters on their prediction accuracies were studied: the decomposition extension factor and process window length. Pre-recorded EMG from four able-bodied subjects was decomposed in a simulated real-time manner as to permit parameter scanning, with the tested estimators being linear regression (LR), linear discriminant analysis (LDA), and LDA with LR for proportionality control (LDA-LR). Results showed the best performing combination of parameters were an extension factor of 8 with window length of 50ms which allowed the LDA-LR estimator to yield an average R 2 value of 0.86±0.05. Under the most computationally demanding set of parameters, the median processing time of the algorithm on a desktop computer was 47ms which was within the update rate of the proposed system. Such results also indicate that parameters optimal for online control applications deviate from those ideal for offline physiological studies.
Non-negative Matrix Factorization (NMF) has been effective in extracting commands from surface electromyography (EMG) for the control of upper-limb prostheses. This approach enables Simultaneous and Proportional Control (SPC) over multiple degrees-of-freedom (DoFs) in a minimally supervised way. Here, like with other myoelectric approaches, robustness remains essential for clinical adoption, with device donning/doffing being a known cause for performance degradation. Previous research has demonstrated that NMF-based myocontrollers, trained on just single-DoF activations, permit a certain degree of user adaptation to a range of disturbances. In this study, we compare this traditional NMF controller with its sparsity constrained variation that allows initialization using both single and combined-DoF activations (NMF-C). The evaluation was done on 12 able bodied participants through a set of online target-reaching tests. Subjects were fitted with an 8-channel bipolar EMG setup, which was shifted by 1cm in both transversal directions throughout the experiments without system retraining. In the baseline condition NMF performed somewhat better than NMF-C, but it did suffer more following the electrode repositioning, making the two perform on par. With no significant difference present across the conditions, results suggest that there is no immediate advantage from the naïve inclusion of more comprehensive training sets to the classic synergy-inspired implementation of SPC.
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