Category: Frontiers in Neuroscience

The foot sole endures high magnitudes of pressure for sustained periods which results in transient but habitual cutaneous ischemia. Upon unloading, microvascular reactivity in cutaneous capillaries generates an influx of blood flow (PORH: post-occlusive reactive hyperemia). Whether pressure induced cutaneous ischemia from loading the foot sole impacts mechanoreceptor sensitivity remains unknown.

Pressure induced ischemia was attained using a custom-built-loading device that applied load to the whole right foot sole at 2 magnitudes (15 or 50% body weight), for 2 durations (2 or 10 minutes) in thirteen seated participants. Mechanoreceptor sensitivity was assessed using Semmes-Weinstein monofilaments over the third metatarsal (3MT), medial arch (MA), and heel. Perceptual thresholds (PT) were determined for each site prior to loading and then applied repeatedly to a metronome to establish the time course to return to PT upon unload, defined as PT recovery time. Microvascular flux was recorded from an in-line laser speckle contrast imager (FLPI-2, Moor Instruments Inc.) to establish PORH peak and recovery rates at each site.

PT recovery and PORH recovery rate were most influenced at the heel and by load duration rather than load magnitude. PT recovery time at the heel was significantly longer with 10 minutes of loading, regardless of magnitude. Heel PORH recovery rate was significantly slower with 10minutes of loading. The 3MT PT recovery time was only longer after 10 minutes of loading at 50% body weight. Microvascular reactivity or sensitivity was not influenced with loading at the MA. A simple linear regression found that PORH recovery rate could predict PT recovery time at the heel (R²=0.184, p<0.001).

In populations with degraded sensory feedback, such as diabetic neuropathy, the risk for ulcer development is heightened. Our work demonstrated that prolonged loading in healthy individuals can impair skin sensitivity, which highlights the risks of prolonged loading and is likely exacerbated in diabetes. Understanding the direct association between sensory function and microvascular reactivity in age and diabetes related nerve damage, could help detect early progressions of neuropathy and mitigate ulcer development.

Subjective tinnitus, the perception of sound without an external acoustic source, is often subsequent to noise-induced hearing loss or ototoxic medications. The condition is believed to result from neuroplastic alterations in the auditory centers, characterized by heightened spontaneous neural activities and increased synchrony due to an imbalance between excitation and inhibition. However, the role of the thalamic reticular nucleus (TRN), a structure composed exclusively of GABAergic neurons involved in thalamocortical oscillations, in the pathogenesis of tinnitus remains largely unexplored.

We induced tinnitus in mice using sodium salicylate and assessed tinnitus-like behaviors using the Gap Pre-Pulse Inhibition of the Acoustic Startle (GPIAS) paradigm. We utilized combined viral tracing techniques to identify the neural circuitry involved and employed immunofluorescence and confocal imaging to determine cell types and activated neurons.

Salicylate-treated mice exhibited tinnitus-like behaviors. Our tracing clearly delineated the inputs and outputs of the auditory-specific TRN. We discovered that chemogenetic activation of the auditory TRN significantly reduced the salicylate-evoked rise in c-Fos expression in the auditory cortex.

This finding posits the TRN as a potential modulatory target for tinnitus treatment. Furthermore, the mapped sensory inputs to the auditory TRN suggest possibilities for employing optogenetic or sensory stimulations to manipulate thalamocortical activities. The precise mapping of the auditory TRN-mediated neural pathways offers a promising avenue for designing targeted interventions to alleviate tinnitus symptoms.

The purpose of this study was to observe, using Footscan analysis, the effect of electromyographic feedback functional electrical stimulation (FES) on the changes in the plantar pressure of drop foot patients.

This case–control study enrolled 34 stroke patients with foot drop. There were 17 cases received FES for 20 min per day, 5 days per week for 4 weeks (the FES group) and the other 17 cases only received basic rehabilitations (the control group). Before and after 4 weeks, the walking speed, spatiotemporal parameters and plantar pressure were measured.

After 4 weeks treatments, Both the FES and control groups had increased walking speed and single stance phase percentage, decreased step length symmetry index (SI), double stance phase percentage and start time of the heel after 4 weeks (p < 0.05). The increase in walking speed and decrease in step length SI in the FES group were more significant than the control group after 4 weeks (p < 0.05). The FES group had an increased initial contact phase, decreased SI of the maximal force (Max F) and impulse in the medial heel after 4 weeks (p < 0.05).

The advantages of FES were: the improvement of gait speed, step length SI, and the enhancement of propulsion force were more significant. The initial contact phase was closer to the normal range, which implies that the control of ankle dorsiflexion was improved. The plantar dynamic parameters between the two sides of the foot were more balanced than the control group. FES is more effective than basic rehabilitations for stroke patients with foot drop based on current spatiotemporal parameters and plantar pressure results.

Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity.

Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand.

The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age.

The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.

Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.

Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.

Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.

Exercise is pivotal for maintaining physical health in contemporary society. However, improper postures and movements during exercise can result in sports injuries, underscoring the significance of skeletal motion analysis. This research aims to leverage advanced technologies such as Transformer, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs) to optimize sports training and mitigate the risk of injuries.

The study begins by employing a Transformer network to model skeletal motion sequences, facilitating the capture of global correlation information. Subsequently, a Graph Neural Network is utilized to delve into local motion features, enabling a deeper understanding of joint relationships. To enhance the model's robustness and adaptability, a Generative Adversarial Network is introduced, utilizing adversarial training to generate more realistic and diverse motion sequences.

In the experimental phase, skeletal motion datasets from various cohorts, including professional athletes and fitness enthusiasts, are utilized for validation. Comparative analysis against traditional methods demonstrates significant enhancements in specificity, accuracy, recall, and F1-score. Notably, specificity increases by ~5%, accuracy reaches around 90%, recall improves to around 91%, and the F1-score exceeds 89%.

The proposed skeletal motion analysis method, leveraging Transformer and Graph Neural Networks, proves successful in optimizing exercise training and preventing injuries. By effectively amalgamating global and local information and integrating Generative Adversarial Networks, the method excels in capturing motion features and enhancing precision and adaptability. Future research endeavors will focus on further advancing this methodology to provide more robust technological support for healthy exercise practices.