Journal of neural engineering最新文献

Objective.Balance impairment is one of the most debilitating consequences of traumatic brain injury (TBI). To study the neurophysiological underpinnings of balance impairment, the brain functional connectivity during perturbation tasks can provide new insights. To better characterize the association between the task-relevant functional connectivity and the degree of balance deficits in TBI, the analysis needs to be performed on the data stratified based on the balance impairment. However, such stratification is not straightforward, and it warrants a data-driven approach.Approach.We conducted a study to assess the balance control using a computerized posturography platform in 17 individuals with TBI and 15 age-matched healthy controls. We stratified the TBI participants into balance-impaired and non-impaired TBI usingk-means clustering of either center of pressure (COP) displacement during a balance perturbation task or Berg Balance Scale score as a functional outcome measure. We analyzed brain functional connectivity using the imaginary part of coherence across different cortical regions in various frequency bands. These connectivity features are then studied using the mean-centered partial least squares correlation analysis, which is a multivariate statistical framework with the advantage of handling more features than the number of samples, thus making it suitable for a small-sample study.Main results.Based on the nonparametric significance testing using permutation and bootstrap procedure, we noticed that the weakened theta-band connectivity strength in the following regions of interest significantly contributed to distinguishing balance impaired from non-impaired population, regardless of the type of stratification:left middle frontal gyrus, right paracentral lobule, precuneus, andbilateral middle occipital gyri. Significance.Identifying neural regions linked to balance impairment enhances our understanding of TBI-related balance dysfunction and could inform new treatment strategies. Future work will explore the impact of balance platform training on sensorimotor and visuomotor connectivity.

Objective.Cryogel microcarriers made of poly(ethylene glycol) diacrylate and 3-sulfopropyl acrylate have the potential to act as delivery vehicles for long-term retention of neurotrophic factors (NTFs) in the brain. In addition, they can potentially enhance stem cell-derived dopaminergic (DAergic) cell replacement strategies for Parkinson's disease (PD), by addressing the limitations of variable survival and poor differentiation of the transplanted precursors due to neurotrophic deprivation post-transplantation in the brain. In this context, to develop a proof-of-concept, the aim of this study was to determine the efficacy of glial cell line-derived NTF (GDNF)-loaded cryogel microcarriers by assessing their impact on the survival of, and reinnervation by, primary DAergic grafts after intra-striatal delivery in Parkinsonian rat brains.Approach.Rat embryonic day 14 ventral midbrain cells were transplanted into the 6-hydroxydopamine-lesioned striatum either alone, or with GDNF, or with unloaded cryogel microcarriers, or with GDNF-loaded cryogel microcarriers.Post-mortem, GDNF and tyrosine hydroxylase immunostaining were used to identify retention of the delivered GDNF within the implanted cryogel microcarriers, and to identify the transplanted DAergic neuronal cell bodies and fibres in the brains, respectively.Main results.We found an intact presence of GDNF-stained cryogel microcarriers in graft sites, indicating their ability for long-term retention of the delivered GDNF up to 4 weeks in the brain. This resulted in an enhanced survival (1.9-fold) of, and striatal reinnervation (density & volume) by, the grafted DAergic neurons, in addition to an enhanced sprouting of fibres within graft sites.Significance.This data provides an important proof-of-principle for the beneficial effects of neurotrophin-loaded cryogel microcarriers on engraftment of cells in the context of cell replacement therapy in PD. For clinical translation, further studies will be needed to assess the impact of cryogel microcarriers on the survival and differentiation of stem cell-derived DAergic precursors in Parkinsonian rat brains.

Objective. Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events intocorrectorerroneousto the present day. Due to this predominant formulation as a binary classification problem, classical ErrP-based BCIs fail to monitor tasks demanding quantitative information on error severity rather than mere qualitative decisions on error occurrence. As a result, fine-tuned and natural feedback control based on continuously perceived deviations from an intended target remains beyond the capabilities of previously used BCI setups.Approach.To address this issue for future BCI designs, we investigated the feasibility of regressing rather than classifying error-related activity non-invasively from the brain.Main results.Using pre-recorded data from ten able-bodied participants in three sessions each and a multi-output convolutional neural network, we demonstrated the above-chance regression of ongoing target-feedback discrepancies from brain signals in a pseudo-online fashion. In a second step, we used this inferred information about the target deviation to correct the initially displayed feedback accordingly, reporting significant improvements in correlations between corrected feedback and target trajectories across feedback conditions.Significance.Our results indicate that continuous information on target-feedback discrepancies can be successfully regressed from cortical activity, paving the way to increasingly naturalistic, fine-tuned correction mechanisms for future BCI applications.

Objective.We investigated tinnitus-related cortical networks in cochlear implant users who experience tinnitus and whose perception of tinnitus changes with use of their implant. Tinnitus, the perception of unwanted sounds which are not present externally, can be a debilitating condition. In individuals with cochlear implants, use of the implant is known to modulate tinnitus, often improving symptoms but worsening them in some cases. Little is known about underlying cortical changes with use of the implant, which lead to changes in tinnitus perception. In this study we investigated whether changes in brain networks with the cochlear implant turned on and off, were associated with changes in tinnitus perception, as rated subjectively.Approach.Using functional near-infrared spectroscopy, we recorded cortical activity at rest, from 14 cochlear implant users who experienced tinnitus. Recordings were performed with the cochlear implant turned off and on. For each condition, participants rated the loudness and annoyance of their tinnitus using a visual rating scale. Changes in neural synchrony have been reported in humans and animal models of tinnitus. To assess neural synchrony, functional connectivity networks with the implant turned on and off, were compared using two network features: node strength and diversity coefficient.Main results.Changes in subjective ratings of loudness were significantly correlated with changes in node strength, averaged across occipital channels (r=-0.65, p=0.01). Changes in both loudness and annoyance were significantly correlated with changes in diversity coefficient averaged across all channels (r=-0.79,p<0.001 and r=-0.86,p<0.001). More distributed connectivity with the implant on, compared to implant off, was associated with a reduction in tinnitus loudness and annoyance.Significance.A better understanding of neural mechanisms underlying tinnitus suppression with cochlear implant use, could lead to their application as a tinnitus treatment and pave the way for effective use of other less invasive stimulation-based treatments.

Objective. Transcranial ultrasound (US) stimulation serves as an external input to a neuron, and thus the evoked response relies on neurons' intrinsic properties. Neural activity is limited to a couple hundred hertz and often exhibits preference to input frequencies. Accordingly, US pulsed at specific physiologic pulse repetition frequencies (PRFs) may selectively engage neurons with the corresponding input frequency preference. However, most US parametric studies examine the effects of supraphysiologic PRFs. It remains unclear whether pulsing US at different physiologic PRFs could activate distinct neurons in the awake mammalian brain.Approach. We recorded cellular calcium responses of individual motor cortex neurons to US pulsed at PRFs of 10, 40, and 140 Hz in awake mice. We compared the evoked responses across these PRFs in the same neurons. To further understand the cell-type dependent effects, we categorized the recorded neurons as parvalbumin positive fast spiking interneurons or putative excitatory neurons and analyzed single-cell mechanosensitive channel expression in mice and humans using the Allen Brain Institute's RNA-sequencing databases.Main results. We discovered that many neurons were preferentially activated by only one PRF and different PRFs selectively engaged distinct neuronal populations. US-evoked cellular calcium responses exhibited the same characteristics as those naturally occurring during spiking, suggesting that US increases intrinsic neuronal activity. Furthermore, evoked responses were similar between fast-spiking inhibitory neurons and putative excitatory neurons. Thus, variation in individual neuron's cellular properties dominates US-evoked response heterogeneity, consistent with our observed cell-type independent expression patterns of mechanosensitive channels across individual neurons in mice and humans. Finally, US transiently increased network synchrony without producing prolonged over-synchronization that could be detrimental to neural circuit functions.Significance. These results highlight the feasibility of activating distinct neuronal subgroups by varying PRF and the potential to improve neuromodulation effects by combining physiologic PRFs.

Objective.To promote the development of objective and comprehensive motion function assessment for patients, based on high-density surface electromyography (HD-sEMG), this study investigates the temporal and spatial variations of neuromuscular activities related to upper limb motor dysfunction.Approach.Patients with unilateral upper limb motor dysfunction and healthy controls were enrolled in the study. HD-sEMG was collected from both arms while they were performing eight hand and wrist movements. Muscle synergies were extracted from the HD-sEMG. Symmetry of bilateral upper limb synergies and synergy differences between motions were proposed as spatial indicators to measure alterations in synergy spatial distribution. Additionally, as a temporal characteristic, the correlation of bilateral upper limb activation coefficient was proposed to describe the coordination control of the central nervous system. All temporal and spatial indicators were compared between patients and healthy subjects.Main results.The patients showed a significant decrease (p< 0.05) in the symmetry of bilateral upper limb synergy spatial distribution and correlation of bilateral upper limb activation coefficient. Patients with motor dysfunction also showed an increase in synergy similarity between motions, indicating altered spatial distribution of muscle synergies.Significance.These findings provide valuable insights into specific patterns associated with motor dysfunction, informing motor function assessment, and guiding targeted interventions and rehabilitation strategies for neurologically disordered patients.

Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.

Objective.This study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance.Approach.The experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks.Main results.The findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operationSignificance. This study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.

Objective.With prolonged life expectancy, the incidence of memory deficits, especially in Alzheimer's disease (AD), has increased. Although multiple treatments have been evaluated, no promising treatment has been found to date. Deep brain stimulation (DBS) of the fornix area was explored as a possible treatment because the fornix is intimately connected to memory-related areas that are vulnerable in AD; however, a proper imaging biomarker for assessing the therapeutic efficiency of forniceal DBS in AD has not been established.Approach.This study assessed the efficacy and safety of DBS by estimating the optimal intersection volume between the volume of tissue activated and the fornix. Utilizing a gold-electroplating process, the microelectrode's surface area on the neural probe was increased, enhancing charge transfer performance within potential water window limits. Bilateral fornix implantation was conducted in triple-transgenic AD mice (3 × Tg-AD) and wild-type mice (strain: B6129SF1/J), with forniceal DBS administered exclusively to 3 × Tg-AD mice in the DBS-on group. Behavioral tasks, diffusion tensor imaging (DTI), and immunohistochemistry (IHC) were performed in all mice to assess the therapeutic efficacy of forniceal DBS.Main results.The results illustrated that memory deficits and increased anxiety-like behavior in 3 × Tg-AD mice were rescued by forniceal DBS. Furthermore, forniceal DBS positively altered DTI indices, such as increasing fractional anisotropy (FA) and decreasing mean diffusivity (MD), together with reducing microglial cell and astrocyte counts, suggesting a potential causal relationship between revised FA/MD and reduced cell counts in the anterior cingulate cortex, hippocampus, fornix, amygdala, and entorhinal cortex of 3 × Tg-AD mice following forniceal DBS.Significance.The efficacy of forniceal DBS in AD can be indicated by alterations in DTI-based biomarkers reflecting the decreased activation of glial cells, suggesting reduced neural inflammation as evidenced by improvements in memory and anxiety-like behavior.

Objective: Training plays a significant role in motor imagery (MI), particularly in applications such as Motor Imagery-based Brain-Computer Interface (MIBCI) systems and rehabilitation systems. Previous studies have investigated the intricate relationship between cues and MI signals. However, the medium of presentation still remains an emerging area to be explored, as possible factors to enhance Motor Imagery signals.. Approach: We hypothesise that the medium used for cue presentation can significantly influence both performance and training outcomes in MI tasks. To test this hypothesis, we designed and executed an experiment implementing no- feedback MI. Our investigation focused on three distinct cue presentation mediums -audio, screen, and virtual reality(VR) headsets-all of which have potential implications for BCI use in the Activities of Daily Lives. Main Results: The results of our study uncovered notable variations in MI signals depending on the medium of cue presentation, where the analysis is based on 3 EEG channels. To substantiate our findings, we employed a comprehensive approach, utilizing various evaluation metrics including Event- Related Synchronisation(ERS)/Desynchronisation(ERD), Feature Extraction (using Recursive Feature Elimination (RFE)), Machine Learning methodologies (using Ensemble Learning), and participant Questionnaires. All the approaches signify that Motor Imagery signals are enhanced when presented in VR, followed by audio, and lastly screen. Applying a Machine Learning approach across all subjects, the mean cross-validation accuracy (Mean ± Std. Error) was 69.24 ± 3.12, 68.69 ± 3.3 and 66.1±2.59 when for the VR, audio-based, and screen-based instructions respectively. Significance: This multi-faceted exploration provides evidence to inform MI- based BCI design and advocates the incorporation of different mediums into the design of MIBCI systems, experimental setups, and user studies. The influence of the medium used for cue presentation may be applied to develop more effective and inclusive MI applications in the realm of human-computer interaction and rehabilitation.