Journal of neural engineering最新文献

Objective. Transcranial direct current stimulation (tDCS) generates sustained electric fields in the brain, that may be amplified when crossing capillary walls (across blood-brain barrier, BBB). Electric fields across the BBB may generate fluid flow by electroosmosis. We consider that tDCS may thus enhance interstitial fluid flow.Approach. We developed a modeling pipeline novel in both (1) spanning the mm (head),μm (capillary network), and then nm (down to BBB tight junction (TJ)) scales; and (2) coupling electric current flow to fluid current flow across these scales. Electroosmotic coupling was parametrized based on prior measures of fluid flow across isolated BBB layers. Electric field amplification across the BBB in a realistic capillary network was converted to volumetric fluid exchange.Main results. The ultrastructure of the BBB results in peak electric fields (per mA of applied current) of 32-63Vm-1across capillary wall and >1150Vm-1in TJs (contrasted with 0.3Vm-1in parenchyma). Based on an electroosmotic coupling of 1.0 × 10^-9- 5.6 × 10^-10m3s-1m2perVm-1, peak water fluxes across the BBB are 2.44 × 10^-10- 6.94 × 10^-10m3s-1m2, with a peak 1.5 × 10^-4- 5.6 × 10^-4m3min-1m3interstitial water exchange (per mA).Significance. Using this pipeline, the fluid exchange rate per each brain voxel can be predicted for any tDCS dose (electrode montage, current) or anatomy. Under experimentally constrained tissue properties, we predicted tDCS produces a fluid exchange rate comparable to endogenous flow, so doubling fluid exchange with further local flow rate hot spots ('jets'). The validation and implication of such tDCS brain 'flushing' is important to establish.

Peripheral neuroregeneration research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures that can serve as biomarkers of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, such biomarkers can elucidate regeneration mechanisms and open new avenues for research. Without these measures, clinical decision-making falls short, and research becomes more costly, time-consuming, and sometimes infeasible. As a companion to Part 2, which is focused on non-invasive imaging, Part 1 of this two-part scoping review systematically identifies and critically examines many current and emerging neurophysiological techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Objective. Slow-wave modulation occurs during states of unconsciousness and is a large-scale indicator of underlying brain states. Conventional methods typically characterize these large-scale dynamics by assuming that slow-wave activity is sinusoidal with a stationary frequency. However, slow-wave activity typically has an irregular waveform shape with a non-stationary frequency, causing these methods to be highly unpredictable and inaccurate. To address these limitations, we developed a novel method using tau-modulation, which is more robust than conventional methods in estimating the modulation of slow-wave activity and does not require assumptions on the shape or stationarity of the underlying waveform.Approach. We propose a novel method to estimate modulatory effects on slow-wave activity. Tau-modulation curves are constructed from cross-correlation between slow-wave and high-frequency activity. The resultant curves capture several aspects of modulation, including attenuation or enhancement of slow-wave activity, the temporal synchrony between slow-wave and high-frequency activity, and the rate at which the overall brain activity oscillates between states.Main results. The method's performance was tested on an open electrocorticographic dataset from two monkeys that were recorded during propofol-induced anesthesia, with electrodes implanted over the left hemispheres. We found a robust propagation of slow-wave modulation along the anterior-posterior axis of the lateral aspect of the cortex. This propagation preferentially originated from the anterior superior temporal cortex and anterior cingulate gyrus. We also found the modulation frequency and polarity to track the stages of anesthesia. The algorithm performed well, even with non-sinusoidal activity and in the presence of real-world noise.Significance. The novel method provides new insight into several aspects of slow-wave modulation that have been previously difficult to evaluate across several brain states. This ability to better characterize slow-wave modulation, without spurious correlations induced by non-sinusoidal signals, may lead to robust and physiologically-plausible diagnostic tools for monitoring brain functions during states of unconsciousness.

Objective.Surface electromyography measurements of the Hoffmann (H-) reflex are essential in a wide range of neuroscientific and clinical applications. One promising emerging therapeutic application is H-reflex operant conditioning, whereby a person is trained to modulate the H-reflex, with generalized beneficial effects on sensorimotor function in chronic neuromuscular disorders. Both traditional diagnostic and novel realtime therapeutic applications rely on accurate definitions of the H-reflex and M-wave temporal bounds, which currently depend on expert case-by-case judgment. The current study automates such judgments.Approach.Our novel wavelet-based algorithm automatically determines temporal extent and amplitude of the human soleus H-reflex and M-wave. In each of 20 participants, the algorithm was trained on data from a preliminary 3 or 4 min recruitment-curve measurement. Output was evaluated on parametric fits to subsequent sessions' recruitment curves (92 curves across all participants) and on the conditioning protocol's subsequent baseline trials (∼1200 per participant) performed nearH_max. Results were compared against the original temporal bounds estimated at the time, and against retrospective estimates made by an expert 6 years later.Main results.Automatic bounds agreed well with manual estimates: 95% lay within ±2.5 ms. The resulting H-reflex magnitude estimates showed excellent agreement (97.5% average across participants) between automatic and retrospective bounds regarding which trials would be considered successful for operant conditioning. Recruitment-curve parameters also agreed well between automatic and manual methods: 95% of the automatic estimates of the current required to elicitH_maxfell within±1.4%of the retrospective estimate; for the 'threshold' current that produced an M-wave 10% of maximum, this value was±3.5%.Significance.Such dependable automation of M-wave and H-reflex definition should make both established and emerging H-reflex protocols considerably less vulnerable to inter-personnel variability and human error, increasing translational potential.

Objective.Three-dimensional (3D) neural tissue engineering is expected to provide new stride in developing neural disease models and functional substitutes to aid in the treatment of central nervous system injury. We have previously detailed an electrical stimulation (ES) system to generate 3D mouse engineered neural tissue (mENT)in vitro. However, ES-induced human ENT (hENT) has not previously been either investigated or identified in structural and functional manner. Here, we applied ES as a stimulator to regulate human neural stem cells in 3D Matrigel, explored the components and functional properties of hENTs.Approach.By immunofluorescence chemical staining and electron microscope imaging, we evaluated the effects of ES on (1) neuronal differentiation and maturation, (2) neurites outgrowth and alignment in hENT, (3) formation of synapses and myelin sheaths in hENT. We further investigated the formation of synaptic connections betweenex-vivo-fused mouse and human tissue. We used calcium imaging to detect activities of neurons in hENT culture.Results.ES could induce neuronal differentiation, the orderly growth of neurites and the maturation of neuron subtypes to construct a well-developed neuronal network with synapses and myelin sheaths. Most importantly, we discovered that raising extracellular K⁺concentration resulted the increasing neuronal excitability in the hENT, indicating electrical activities in neuronal cells.Significance.We applied ES to generate the organised 3D hENTs and identified them in both structural and functional manner.

Objective.Transfer entropy (TE) has been widely used to infer causal relationships among dynamical systems, especially in neuroscience. Kendall transformation provides a novel quantization method for estimating information-theoretic measures and shows potential advantages for small-sample neural signals. But it has yet to be introduced into the framework of TE estimation, which commonly suffers from the limitation of small sample sizes. This paper aims to introduce the idea of Kendall correlation into TE estimation and verify its effect.Approach.We proposed the Kendall TE (KTE) which combines the improved Kendall transformation and the TE estimation. To confirm its effectiveness, we compared KTE with two common TE estimation techniques: the adaptive partitioning algorithm (D-V partitioning) and the symbolic TE. Their performances were estimated by simulation experiments which included linear, nonlinear, linear + nonlinear models and neural mass models. Moreover, the KTE was also applied to real electroencephalography (EEG) recordings to quantify the directional connectivity between frontal and parietal regions with propofol-induced general anesthesia.Main results.The simulation results showed that the KTE outperformed the other two methods by many measures: (1) identifying the coupling direction under a small sample size; (2) the sensitivity to coupling strength; (3) noise resistance; and (4) the sensitivity to time-dependent coupling changes. For real EEG recordings, the KTE clearly detected the disrupted frontal-to-parietal connectivity in propofol-induced unconsciousness, which is in agreement with previous findings.Significance.We reveal that the proposed KTE method is a robust and powerful tool for estimating TE, and is particularly suitable for small sample sizes. The KTE also provides an innovative form of quantizing continuous time series for information-theoretic measures.

Objective.Current ear electrodes often require complex placing or long stimulation durations to achieve good detection of steady-state visual evoked potential (SSVEP). To improve the practicability of ear electrode-based SSVEP-BCI (brain-computer interface) system, we developed a high-performance ear electrode that can be easily placed.Approach.Hydrogel based disposable and replaceable semi-dry electrodes are developed to improve the contact impedance and wear feeling. The best combination of electrodes for SSVEP-BCI application around the ear is optimized by assessing the electrode on volunteers, and the performance of the electrode was compared with that of the occipital electrode.Main results.The developed ear hydrogel electrode can achieve an impedance close to that of the wet electrode. Three combinations of ear electrode groups demonstrate high information transfer rate (ITR) and accuracy in SSVEP-BCI applications. According to the rating of the comprehensive assessment and BCI performance in the online session, the behind-aural electrode is the best electrode combination for recording SSVEP in the ear region. The average preparation time is the shortest, and the average impedance is the lowest. The ITR of the behind-aural electrode based SSVEP-BCI system can reach 37.5 ± 18 bits min^-1. The stimulus duration was as low as 3 s compared to 5 s or 10 s in other studies.Significance.The accuracy, ITR, and wear feeling can be improved by introducing a semi-dry ear electrode and optimizing the position and the combination of ear electrode. By providing a better trade-off between performance and convenience, the ear electrode-based SSVEP-BCI promises to be used in daily life.

Objective.The motor hyperdirect pathway (HDP) is a key target in the treatment of Parkinson's disease with deep brain stimulation (DBS). Biophysical models of HDP DBS have been used to explore the mechanisms of stimulation. Built upon finite element method volume conductor solutions, such models are limited by a resolution mismatch, where the volume conductor is modeled at the macro scale, while the neural elements are at the micro scale. New techniques are needed to better integrate volume conductor models with neuron models.Approach.We simulated subthalamic DBS of the human HDP using finely meshed axon models to calculate surface charge deposition on insulting membranes of nonmyelinated axons. We converted the corresponding double layer extracellular problem to a single layer problem and applied the well-conditioned charge-based boundary element fast multipole method (BEM-FMM) with unconstrained numerical spatial resolution. Commonly used simplified estimations of membrane depolarization were compared with more realistic solutions.Main result.Neither centerline potential nor estimates of axon recruitment were impacted by the estimation method used except at axon bifurcations and hemispherical terminations. Local estimates of axon polarization were often much higher at bifurcations and terminations than at any other place along the axon and terminal arbor. Local average estimates of terminal electric field are higher by 10%-20%.Significance. Biophysical models of action potential initiation in the HDP suggest that axon terminations are often the lowest threshold elements for activation. The results of this study reinforce that hypothesis and suggest that this phenomenon is even more pronounced than previously realized.

Objective. Vagus nerve stimulation (VNS), which involves a surgical procedure to place electrodes directly on the vagus nerve (VN), is approved clinically for the treatment of epilepsy, depression, and to facilitate rehabilitation in stroke. VNS at surgically implanted electrodes is often limited by activation of motor nerve fibers near and within the VN that cause neck muscle contraction. In this study we investigated endovascular VNS that may allow activation of the VN at locations where the motor nerve fibers are not localized.Approach. We used endovascular electrodes within the nearby internal jugular vein (IJV) to electrically stimulate the VN while recording VN compound action potentials (CAPs) and neck muscle motor evoked potentials (MEPs) in an acute intraoperative swine experiment.Main Results. We show that the stimulation electrode position within the IJV is critical for efficient activation of the VN. We also demonstrate use of fluoroscopy (cone beam CT mode) and ultrasound to determine the position of the endovascular stimulation electrode with respect to the VN and IJV. At the most effective endovascular stimulation locations tested, thresholds for VN activation were several times higher than direct stimulation of the nerve using a cuff electrode; however, this work demonstrates the feasibility of VNS with endovascular electrodes and provides tools to optimize endovascular electrode positions for VNS.Significance. This work lays the foundation to develop endovascular VNS strategies to stimulate at VN locations that would be otherwise too invasive and at VN locations where structures such as motor nerve fibers do not exist.

Objectives. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties.Approach.We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called 'detection confidence', quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat.Main Results.Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms.Significance.Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.