Journal of neural engineering最新文献

Objective.Investigation into complex neural circuits necessitates interfaces capable of high channel count recording and stimulation. However, existing commercial neural headstages often have limited scalability, restrictive proprietary designs, and constrained bidirectional capabilities, which worsens accessibility challenges and compels researchers to reinvent tools rather than build on a shared foundation.Approach.Here, we present two open-source, 128 channel headstages-Iris 128B and Iris 128S-designed for integration with microelectrode arrays. The Iris 128B enables fully bidirectional interfacing, with stimulation or recording across all 128 electrode channels, while the Iris 128S provides recording on 128 channels and stimulation on 16 simultaneous channels, which can be assigned to any 16 of the 32 available stimulation channels. Both designs use Intan Technologies' RHS and RHD series integrated circuits for amplification, filtering, digitization and stimulation, and are available on GitHub.Main results.The headstages were validated through benchtop impedance, noise, and frequency response measurements, as well as acutein vivorecordings in an anesthetized rat. Results demonstrate low noise levels and reliable signal acquisition across all channels.Significance.By releasing fully documented printed circuit board designs for headstages, this work aims to take a step towards broader adoption of bidirectional recording and stimulation systems while increasing channel counts. Future iterations will focus on miniaturization and wireless integration to improve usability in chronic and freely moving small animal experiments.

Objective. Sources of epileptic traveling waves offer critical insights into seizure onset zone (SOZ) localization, making them invaluable for preoperative assessment in patients with epilepsy. However, the absence of tailored source-tracing methods and the inherent instability of epileptiform activity make it difficult to achieve reliable source identification for SOZ localization. This study aimed to analyze the propagation pattern during seizure events and develop a framework to trace the sources of epileptic expanding traveling waves (ETWs).Approach. 101 seizure events were recorded from five 4-Aminopyridine-induced acute cortical rat epilepsy models. In each seizure event, epileptiform activities were classified into two categories according to their time-frequency diagrams (multiband and non-multiband epileptiform activities). The center of the SOZ was regarded as the recording site with the largest amplitude of epileptiform activities. Using the spatial-phase-based analysis, we analyzed the propagation pattern during the seizure event and extracted the ETWs. The sources of ETWs were traced by the intersection of spatial-phase-gradient.Main results. The ETW proportion of multiband epileptiform activities was 62.7%±8.3%, significantly higher than those in non-multiband epileptiform activities (53.8%±9.0%). ETWs with stable propagation patterns gave rise to a concentrated source tracing outcome. The single-band signal (component of the multiband activities) had a more stable ETW propagation pattern than both the multiband and non-multiband activities. The source tracing results of the single-band signals clustered around the SOZ center and remained stable even when the SOZ center was out of coverage (removing half of the recording sites, among which the SOZ center was included).Significance. The proposed framework enables ETW extraction from epileptiform activities and can trace ETW sources even when the sources are out of coverage. Therefore, the proposed framework may prove clinically valuable in cases with sparse intracranial recordings, addressing the limitation of traditional SOZ localization methods.

Objective.Electroencephalography (EEG) records the spontaneous electrical activity in the brain. Despite the growing application of deep learning in EEG decoding, traditional methods still rely heavily on supervised learning, which is often limited by task specificity and dataset dependency, restricting model performance and generalization. Inspired by the success of large language models, EEG foundation models (EEG FMs) are attracting increasing attention as a unified paradigm for EEG decoding. In this study, we review a selection of representative studies on EEG FMs, aiming to extract trends and provide recommendations for future research.Approach.We provide a comprehensive analysis of recent advances in EEG FMs, with a focus on downstream tasks, benchmark datasets, model architectures, and pre-training techniques. We analyze and synthesize core FMs components, and systematically compare their performances and generalizabilities.Main results.Our review reveals that EEG FMs are pre-trained on large-scale datasets, typically involving several hundred subjects. The number of subjects can reach up to 14 987, with a maximum total duration of 27 062 h. Current EEG FMs most adopt mask-based reconstruction pre-training strategy and employ efficient transformer-based architectures. Our comparative analysis shows that EEG FMs demonstrate significant potential in advancing EEG decoding tasks, particularly in seizure detection. However, their performance in complex scenarios such as motor imagery decoding remains limited.Significance.This review summarizes the existing approaches and performance outcomes of EEG FM, offers valuable insights into their current limitations and delineates prospective avenues for future research.

The treatment of glioblastoma (GBM) presents significant challenges, with median survival rates remaining low despite standard-of-care therapies. A novel approach, cytostatic hypothermia (CH), is under development against GBM; it is a window of temperature (typically 20 °C-25 °C) which halts tumor growthin vivo.Objective.This feasibility study expands upon the findings through the computational evaluation of a fully implantable system. Our simulations evaluate a thermoelectric cooler with a microwire array (NeuraTEC) and a novel ambient recirculating core (ARC) to achieve uniform cooling of a region in the brain without overheating local skin temperature.Approach.Finite-element modeling was employed to simulate coupled bioheat transfer and laminar non-isothermal fluid flow dynamics.Main results.Our results indicate that NeuraTEC can attain local tissue temperatures within a cytostatic range while minimizing thermal gradients. The use of multiple narrow, thermally conductive wires enhances cooling uniformity with minimal tissue displacement. The ARC provides a unique form of heat management that enables full implantability and hence portability. This work suggests it can facilitate the transfer of heat from a brain region to the skin. Future work will focus on device prototyping and validation throughin vitroandin vivostudies in large animal models.Significance.These simulations suggest that the proposed intracranial cooling system could make CH a practicable approach against GBM. Furthermore, this approach to internal heat management may also open new avenues for treating neurological conditions through local and chronic hypothermia, extending beyond the short-duration (acute) cooling methods currently tested.

Objective.In brain-computer interface (BCI) applications, signals are conventionally acquired from the cerebrum, and only a subset of the complex interactions that occur in several areas of the brain are collected. One area that has not been investigated for BCI application is the cerebellum, despite its involvement in movement and executive function. The present study aimed to determine the features of movement-related cerebellar electrocerebellography (ECeG) that are most useful for decoding, and how performance compares with conventional electroencephalography (EEG) recordings from the cerebrum.Approach.ECeG and EEG data were collected from six healthy adults to identify useful movement-related features from both cerebrum and cerebellum. Electromyography was used to capture the movements from the muscles. Decoding was conducted in binary movement vs. rest and movement vs. movement systems using support vector machines. Decoding performance was compared between cerebral, cerebellar, a combination of both, and temporal groups. Re-referencing techniques were applied to compensate for possible common reference artefacts or volume conduction effects.Main results. Movement-related features were decoded from over the cerebellum and the cerebrum. Classification accuracies were similar in both the cerebrum and cerebellum, when classifying movement vs. rest (cerebrum: 0.78 ± 0.02, cerebellum: 0.70 ± 0.01) and movement vs. movement states (cerebrum: 0.76 ± 0.02, cerebellum: 0.71 ± 0.02). The delta band (1-3 Hz) was the most useful feature for decoding.Significance.This study demonstrated, for the first time, that ECeG is a feasible source of movement related signals for implementing a BCI. The present study also demonstrated that the ECeG closely resembled the EEG signals and represents an alternate approach for BCI where the signal from the cerebrum is unreliable either due to disease or injury.

Objective. The goal of this study was to examine the effects of spinal cord stimulation (SCS) on muscle activity during walking after lower-limb amputation. Amputation results in a loss of sensory feedback and alterations in gait biomechanics, including co-contractions of antagonist muscles about the knee and ankle, and reduced pelvic obliquity range-of-motion and pelvic drop. SCS can restore sensation in the missing limb, but its effects on muscle activation and gait biomechanics have not been studied in people with lower-limb amputation.Approach. This case study included a participant with transtibial amputation who was implanted percutaneously with SCS electrodes over the lumbosacral enlargement for 84 d. SCS was used during in-lab experiments to provide somatosensory feedback from the missing limb, relaying a sense of plantar pressure when the prosthesis was in the stance phase of the gait cycle. We used electromyography (EMG) to record muscle activity from the residual and intact limbs, and 3D motion capture to measure pelvic obliquity and knee and ankle joint angles. EMG signals were recorded during walking with and without SCS at early (Day 30) and late (Day 63) time points across the implant duration.Main results. During walking, co-contraction of knee antagonist muscles was reduced following multiple sessions of SCS-mediated sensory restoration. Additionally, the activation of the hip abductor (tensor fasciae latae) muscle increased activity during gait with SCS-mediated sensory restoration, which corresponded to an increase in pelvic obliquity range-of-motion and pelvic drop, towards normal.Significance. Restoring sensation in the missing limb using SCS altered muscle activity during walking led to improved coordination and pelvic motion in an individual with lower-limb amputation.

Objective.Magnetomyography (MMG) using optically pumped magnetometers (OPM) offers a contactless, non-invasive approach to assess muscle activity. However, fluctuations in the sensor-to-source distance during MMG recordings pose a significant challenge to accurate signal interpretation since amplitude decays with distance. No established method exists for MMG to continuously monitor sensor-to-source distance changes in real-time.Approach.This study presents a new non-magnetic, cost-effective solution using a digital fiber optic sensor to continuously measure the distance between an OPM and the subject's skin. Following sensor calibration, distance measurements were recorded during an isometric muscle fatigue task in five healthy participants to assess whether MMG amplitude changes were due to physiological effects or variations in sensor-to-source distance. Alongside OPM-MMG and distance tracking, electromyography (EMG), the neurophysiological gold standard, was simultaneously recorded.Main results.We found significant changes in MMG-RMS and MMG-MDF during muscle fatigue that were not merely explained by changes in sensor-to-source distance. Furthermore, we found substantial correlations between OPM-MMG and EMG that were strongest for small sensor-to-source distance (r= 0.91).Significance.Fiber optic sensors offer non-magnetic, precise, real-time monitoring of the distance between the OPM and the skin, making it ideal for MMG applications to account for distance-related variability during measurements. Our results suggest that changes in MMG-RMS and MMG-MDF during muscle fatigue reflect genuine physiological effects rather than distance confounds.

Objective. Several data-driven approaches based on information theory have been proposed for analyzing high-order interactions (HOIs) involving three or more components of a network system. The existing methods do not account for temporal correlations in the data, or are defined only in the time domain and rely on the assumption of stationarity in the underlying dynamics, making them inherently unable to detect frequency-specific behaviors and track transient functional links in physiological networks.Approach. This study introduces a new framework which enables the time-varying and time-frequency analysis of HOIs in networks of random processes through the spectral representation of vector autoregressive models. The time- and frequency-resolved analysis of synergistic and redundant interactions among groups of processes is ensured by a robust identification procedure based on a recursive least squares estimator with a forgetting factor.Main results. Validation on simulated networks illustrates how the time-frequency analysis is able to highlight transient synergistic behaviors emerging in specific frequency bands which cannot be detected by time-domain stationary analyzes. The application on brain evoked potentials in rats elicits the presence of redundant information timed with whisker stimulation and mostly occurring in the contralateral hemisphere. The application to cardiovascular oscillations reveals a reduction in redundant information following head-up tilt, reflecting a functional disconnection within the physiological network of heart period, respiratory, and arterial pressure signals.Significance. The proposed framework enables a comprehensive time-varying and time-frequency analysis of the hierarchical organization of dynamic networks. As our approach goes beyond pairwise interactions, it is well suited for the study of transient high-order behaviors arising during state transitions in many network systems commonly studied in physiology, neuroscience and other fields.

Tourette syndrome (TS) is a chronic tic disorder characterized by motor and vocal tics. Neuropsychiatric symptoms are nearly universal in TS, particularly attention deficit hyperactivity disorder and obsessive-compulsive disorder. TS can have substantial effects on quality of life, social and intellectual development, opportunities, relationships, and more. Treatment options are limited; the most common being behavioral therapy and pharmacological interventions, such as antipsychotics and anti-adrenergic agents, often yielding unsatisfactory benefits. Neuromodulation, the alteration of neural pathways and networks under external stimulation, has been established as a viable treatment strategy for specific aspects of TS. Several neuromodulation techniques have been utilized, with deep brain stimulation (DBS) exhibiting the strongest efficacy at around 50% reduction of tics on average across cohorts. However, the invasive nature of DBS remains a disincentive for its uptake, as well as the natural reduction in tic severity for many TS individuals as they enter adulthood. Less-invasive neuromodulation has also been explored, but efficacy remains limited. Given its effectiveness in TS, DBS provides the unique opportunity to record neural activity from deep brain structures, which has been used to investigate underlying pathophysiology and search for biomarkers of treatment response. These insights may guide strategies for less invasive neuromodulation. In this narrative review, we aim to discuss currently utilized neuromodulation therapies for the treatment of TS, as well as propose potential future strategies. Additionally, we discuss how to maximize progress in the field, including crucial multicenter data sharing, utilization of recording capabilities on DBS devices, correlation with the precise location of implanted electrodes, and harnessing pre-clinical studies for a more parameterized understanding of TS neuromodulation. These techniques will enable a clearer understanding of TS and the mechanisms behind successful treatment. This could lead to advanced therapies that improve the quality of life for individuals with TS.

Objective.This paper presents an in-depth analysis of the recent literature on dynamic functional connectivity (dFC) analysis. This represents a paradigm shift in the analysis of neural data to overcome the inherent limitations of static assumptions about functional brain connectivity. By exploiting the information provided by high temporal resolution neuroimaging techniques, such as magnetoencephalography (MEG) and electroencephalography (EEG), the possibility of tracking functional network organization and reconfiguration that support brain functions at different temporal scales has been extensively explored.Approach.This review examines the current state-of-the-art of the methodological approaches for dFC analysis in biomedical science, focusing on literature from 2018 to 2024 and on the analysis of EEG and MEG data. The review primarily concentrates on methods for estimating the time-resolved functional connectivity matrix, also providing an overview of approaches for summarising and inferring dynamic information.Main results.An insight into the available methodological approaches for tracking dFC at different temporal scales is offered. Besides the classical sliding window method, advances in instantaneous dFC algorithms are described and two novel approaches are introduced: microstate-based dFC (micro-dFC) and data-driven dFC methods. For each approach, specific features are detailed, and the dataset characteristics to ensure applicability are discussed. In addition, possible post-processing procedures for extracting the dynamic properties and information of interest are presented.Significance.The undoubted potential of dFC analysis for the study of brain dynamics is highlighted, providing a guide for its application, also taking into consideration the study protocol, the nature of the data and the temporal resolution of interest. Current limitations and open challenges are also critically addressed.