Journal of neural engineering最新文献

Objective.Group interactions capture cooperative dynamics among neural populations quantitatively, while also enabling precise detection of ensemble-level synchrony patterns and transcending the limitations of node-level relationships. To evaluate higher-order group interactions, we propose the PLASSO-homophily framework using multichannel stereo-electroencephalography (SEEG) recorded from patients with epilepsy.Approach.Specifically, we use phase locking value to improve least absolute shrinkage and selection operator method for constructing hypergraphs. Afterwards, we calculate affinity ratios between brain zones. Finally, we investigate higher-order interactions among different groups from a homophily perspective. The extremal result of strict homophily serves as a crucial theoretical framework for understanding homophily concepts, reflecting the constraints that different groups follow in higher-order interactions.Main results.It is observed that group interactions between seizure onset zones (SOZ), propagation zones (PZ) and non-involved zones (NIZ) present significant distinction across different seizure phases. In particular, the homophily of SOZ reaches a peak point during the seizure and sharply decreases in the post-seizure, with the most statistically significant differences onθandγbands. Furthermore, during the seizure, SOZ-PZ exhibits enhanced coupling while SOZ-NIZ exhibits impaired functional integration. Finally, among three groups, only SOZ exhibits strict monotonic and majority homophily.Significance.By analyzing changes in in-class and out-class connectivity, we quantitatively assess the activity levels and combinatorial constraints of the SOZ, PZ, and NIZ, thereby providing a novel perspective for exploring seizure mechanisms and developing epilepsy treatments.

Objective.Transcutaneous spinal cord stimulation, a non-invasive spinal cord neuromodulation method holds tremendous promise and hope to restore functions in individuals with paralysis resulting from spinal cord injury (SCI), cerebral palsy, stroke and other neurological conditions. Yet, there are relatively few options for such stimulation devices compared to conventional stimulators commonly used for neuromuscular electrical stimulation, transcutaneous electrical nerve stimulation, and functional electrical stimulation, particularly for people with neurological conditions in the developing countries.Approach.In this report, we present OpenXstim, an open-source, two-channel programmable electrical stimulator developed to advance research in non-invasive muscle, nerve, or spinal cord stimulation treatments.Main results. OpenXstim can deliver current pulses up to 110 mA with a compliance voltage of 96 V per channel. In benchtop testing, we found that the stimulator successfully generates high frequency (9 kHz) burst stimulation, a mode commonly used for spinal cord neuromodulation. The stimulator was further tested in two individuals with SCI and showed preliminary indications of functional improvement. However, large controlled trials are needed to establish efficacy. Although special care was taken in the design of the stimulator to ensure user safety, users are strongly warned to handle the device with utmost caution, as it can generate high voltage and current that may cause adverse health effects if not used properly.Significance.This programmable, open-source stimulator offers tangible hope for improving the accessibility of non-invasive neuromodulation treatments for people with paralysis worldwide. The design and complete source-code of the stimulator are freely available online in a public repository:https://github.com/OpenMedTech-Lab/OpenXstim.

Objective.As the prevalence of dementia continues to rise, the need for accurate and early diagnostic tools becomes increasingly critical. Despite diverse underlying causes, dementia types share common cognitive symptoms, making accurate diagnosis essential for effective treatment.Approach: This study investigates electroencephalographic (EEG)-based spectral brain connectivity in individuals with Alzheimer's disease (AD,N=36), frontotemporal dementia (FTD,N=23), and healthy controls (HCs,N=29), with the dual aim of identifying condition-specific connectivity patterns and evaluating three coherence-based connectivity measures: coherence, imaginary coherence, and partial coherence. Resting-state, eyes-closed EEG data (19 channels) were analyzed, and connectivity was estimated across frequencies to assess both global and local network alterations.Main results.The results indicate that dementias (both AD and FTD) are characterized by decreased connectivity in higher frequency bands and increased connectivity in lower frequencies, reflecting respectively impaired neural communication and neurodegeneration. Moreover, the severity of cognitive impairment correlates with the spatial extent and magnitude of connectivity disruptions. Notably, partial coherence-unlike coherence and imaginary coherence-effectively distinguishes between the AD and FTD groups, suggesting that direct connectivity measures may provide more discriminative information for differential diagnosis.Significance.These findings highlight the potential of EEG-based spectral connectivity analysis, particularly partial coherence, as a non-invasive tool to aid in the diagnosis and differential diagnosis of dementia subtypes, supporting early clinical decision-making.

Objective.Domain adaptation (DA) has achieved remarkable performance in cross-subject electroencephalogram (EEG) decoding by mitigating the inter-subject data distribution discrepancies. However, when exploring the feature alignment subspace and performing self-supervised pseudo-labeling in an iterative way, two difficulties are often encountered: one is that unreliable target labeling results inevitably mislead the domain-free feature learning process in the early stage and the other is that the contribution of source and target samples should be balanced in the later stage.Approach.To address both issues, this paper proposes prototype-based progressive confident target sample labeling (P²CSL) method to use subspace class prototypes to assist in labeling target samples under the unified framework of domain-invariant EEG feature learning and the self-supervised target sample labeling, and progressively incorporate confident target samples into DA model fitting. The underlying rationality is that early-stage pseudo-labels from unconverged models are prone to error propagation, requiring auxiliary mechanisms to ensure their reliability and stabilize training. With the gradual alignment of cross-subject features, the estimated pseudo-label information of target domain will be more reliable, meaning that more target samples should be involved in model training.Main results.Experiments on emotion recognition and inner speech decoding demonstrate the competitive performance of P²CSL in cross-subject EEG classification in comparison with SOTA methods.Significance.Our study indicates the effectiveness of jointly considering the reliability of target samples and their contribution to model training in the context of DA. In addition, some fine-grained results including the sample confidence allocation strategy, the DA effects, and the dynamic model optimization process are provided to further illustrate the model execution details.

Obective.Bipolar re-referencing (BPRR), in which one electrode's signal is subtracted from a neighboring electrode to produce a differential signal, can improve signal readability and refine localization for intracranial electroencephalography. There is wide variation in manufactured electrode array spacing, yet how BPRR affects specific frequencies at precise inter-electrode distances has not been systematically evaluated.Approach.Intracranial recordings with uniquely large numbers of electrodes were obtained for sixteen patients with drug-resistant epilepsy. We evaluated combinations of high-density subdural grid, depth, and strip electrodes (n= 3,664, 742, and 336) with manufactured linear inter-electrode distances of 4, 5, and 10 mm, respectively. BPRR was performed using all possible electrode pairs (n= 445 305 grid, 16 004 depth, 3278 strip) spanning distances from 2-60 mm. Multi-taper power spectra were generated separately for grid, depth, and strip contacts. Distances were consolidated across patients and anatomical areas for generalizability, and distance-related influences on task-related brain activity and quantitative interictal epileptiform discharge localization were evaluated.Main results.We identified 8 mm as a consistent reversal point for BPRR, below which low-frequency signals (<30 Hz) had consistently decreased power, and higher frequencies had increased power. Larger distances increased all broadband (2-200 Hz) signals. Task-related increases in superior temporal gyrus 50-200 Hz activity were consistently enhanced across 4-40 mm bipolar distances. There were non-significant difference trends between 4 and 8 mm re-referencing on epileptiform discharge detection.Significance.BPRR distance imposed specific transition points for distance and frequency (roughly 8 mm and ∼30 Hz, respectively) that produced differential effects on measurements of signal power. The consistency across brain regions and electrode types (depth, subdural) suggests these influences are physical brain bio-signal properties, potentially related to spatial wavelength of periodic oscillations in lower frequencies in contrast to more aperiodic activity in higher frequencies. A distance-frequency relation map is provided to help optimize neural signal biomarker quality for intracranial applications by guiding strategic re-referencing distance selection.

Objective.Infrared neural stimulation (INS) has emerged as a potent neuromodulation technology, offering safe and focal stimulation with superior spatial recruitment profiles compared to conventional electrical methods. However, the neural dynamics induced by INS stimulation remain poorly understood. Elucidating these dynamics will help develop new INS stimulation paradigms and advance its clinical application.Approach.In this study, we assessed the local network dynamics of INS entrainment in the auditory thalamocortical circuit using the chronically implanted rat model. Our approach focused on measuring INS energy-based local field potential (LFP) recruitment induced by focal thalamocortical stimulation. We further characterized linear and nonlinear oscillatory LFP activity in response to single-pulse and periodic INS and performed spectral decomposition to uncover specific LFP band entrainment to INS. Finally, we examined spike-field transformations across the thalamocortical synapse using spike-LFP coherence coupling measures.Main results.We found that INS significantly increases LFP amplitude as a log-linear function of INS energy per pulse, primarily entraining to LFPβandγbands with synchrony extending to 200 Hz in some cases. A subset of neurons demonstrated nonlinear, chaotic oscillations linked to information transfer across cortical circuits. Finally, we utilized spike-field coherences to correlate spike coupling to LFP frequency band activity and suggest an energy-dependent model of network activation resulting from INS stimulation.Significance.We show that INS reliably drives robust network activity and can potently modulate cortical field potentials across a wide range of frequencies in a stimulus parameter-dependent manner. Based on these results, we propose design principles for developing full coverage, all-optical thalamocortical auditory neuroprostheses.

Objective.Cranial nerve stimulation uses electric current to modulate higher-order brain activity and organ function via nerves, including the vagus and trigeminal, with applications in migraine, epilepsy, and pediatric attention deficit hyperactivity disorder. The trigeminal nerve is an emerging target for non-invasive neuromodulation due to the superficial trajectory of its branches, the supraorbital (SON), infraorbital (ION), and mental nerves (MN), and the predominantly sensory composition of the SON and ION. However, the parameters and outcomes of trigeminal nerve stimulation (TNS) remain varied.Approach.This study characterizes the anatomical course and tissue composition of the SON, ION, and MN using five human donors. Computed tomography imaging was used to localize each nerve's exit foramen and distance to midline. Microdissections quantified nerve circumference and depth relative to the skin surface. Histological analysis described the number of fascicles and fascicular tissue area. Nerve depths were incorporated into an illustrative finite element model to assess the effect of interface properties on activation of on- and off-target neural pathways.Main results.Cadaveric measurements, histological analyses, and imaging outline the depths, branching patterns, and fascicular organization within the trigeminal nerve branches. The SON was found to be significantly more superficial than the ION and MN with a higher nerve-to-connective tissue ratio compared to the MN. Our illustrative modeling demonstrated that depth was a driving factor for neural activation and sensitivity to skin impedance properties.Significance.The SON presents the most accessible and anatomically favorable target for transcutaneous TNS among the branches examined due to its superficial location. Consistent with our previous work, however, preferential activation of low-threshold nociceptors compared to nerve trunks may lead to treatment-limiting off-target side effects. These findings offer an anatomically informed framework to guide further modeling, electrode design, andin situimaging of nerve branching patterns to better estimate activation of on- and off- target pathways.

Objective.With the advancement of deep learning technologies, more and more researchers have begun developing end-to-end automatic sleep stage classification frameworks. However, these frameworks typically require access to large electroencephalogram (EEG) datasets for training, which imposes a significant computational burden. Furthermore, EEG data contains patient privacy information, and using such data for training raises concerns about privacy infringement. To address these issues, we propose a hybrid data distillation method. We aim to enable single-channel EEG sleep stage classification with less training cost and privacy risk by distilling large real datasets into a tiny, privacy-preserving synthetic set for training from scratch.Approach.We first apply the gradient matching method to optimize the randomly initialized synthetic dataset. The gradient changes in the early stages of model training can quickly reduce the performance gap between the synthetic dataset and the source dataset. Subsequently, to avoid oscillations near the optimal solution during gradient matching, we switch to distribution matching to further optimize the synthetic dataset. This method aligns the data distribution at a global level, enhancing overall consistency. In addition, we adopt a novel mini-batch iteration method to assist the synthetic dataset in learning temporal dependencies.Main results.We validated our framework on three public datasets and achieved robust results.Significance.This study proposes an efficient and robust hybrid data distillation algorithm, providing a feasible approach for implementing sleep stage staging based on privacy protection.

Objective.As an emerging paradigm of brain-computer interfaces (BCIs), speech BCI has the potential to directly reflect auditory perception and thoughts, offering a promising communication alternative for patients with aphasia. Chinese is one of the most widely spoken languages in the world, whereas there is very limited research on speech BCIs for Chinese language.Approach.This paper reports a text-magnetoencephalography (MEG) dataset for non-invasive Chinese speech BCIs. It also proposes a multi-modality assisted speech decoding (MASD) algorithm to capture both text and acoustic information embedded in brain signals during speech activities.Main results.Experiment results demonstrated the effectiveness of both our text-MEG dataset and our proposed MASD algorithm.Significance.To our knowledge, this is the first study on multi-modality assisted decoding for non-invasive Chinese speech BCIs.

Objective.Neural electrode arrays, as essential tools for recording and stimulating neural tissues, significantly impact therapeutic strategies for neurological disorders through deep brain stimulation, responsive neurostimulation, and brain-computer interfaces. Despite considerable advancements, the efficiency and longevity of neural electrode arrays are compromised by brain micromotion, induced by physiological activities such as cardiac pulsation and respiration. The mechanical mismatch between rigid electrode arrays and soft neural tissue generates persistent stresses at the electrode-tissue interface, triggering tissue damage, inflammatory responses, encapsulation, and ultimately electrode failure. Deployable neural electrode arrays, characterized by structural reconfiguration after implantation, have emerged to address these challenges. Deployment mechanisms, including unfolding, expanding, unrolling, or ejecting electrode arms from an initially compact configuration, reduce insertion trauma, maximize spatial coverage, and mitigate brain micromotion effects, thereby enhancing long-term stability and recording fidelity.Approach.This review provides the first comprehensive analysis of deployable intracortical and electrocorticography electrode arrays, emphasizing their design principles, deployment mechanisms, mechanical performance, advantages, and limitations.Main results.This review fills a critical gap in the existing neural electrode literature by transitioning the focus from traditional geometric and material considerations to advanced structural reconfiguration strategies.Significance.An understanding of the advantages and disadvantages of these deployment strategies provides essential insights and future directions for optimizing neural electrode technologies.