Journal of Neural Engineering

Objective. Modern neural devices allow to interact with degenerated tissue in order to restore sensoric loss function and to suppress symptoms of neurodegenerative diseases using microelectronic arrays (MEA). They have a bidirectional interface for performing electrical stimulation to write-in new information and for recording the neural activity to read-out a neural task, e.g. movement ambitions. For both applications, the electrical impedance of the electrode-tissue interface (ETI) is crucial. However, the ETI can change during run-time due to encapsulation effects and changes of the neuronal structures. We investigated if an impedance spectrum can be reliably extracted from recordings during stimulation with microelectrode arrays. Approach. We present a measurement method for characterizing the electrical impedance spectrum during stimulation. We performed charge-controlled stimulation with a penetrating microelectrode array in an electrolyte solution. From the stimulation recordings, we extracted the impedance. Furthermore, a numerical model (digital twin) of the stimulation electrodes is established. Main results. We obtained consistent results for relevant electrochemical using electrochemical impedance spectroscopy, time-domain analysis and Fourier-transform-based impedance estimation. Moreover, the numerical simulations confirmed that the measured microelectrode had the expected properties. Significance. Our results pave the way to enable a live assessment of the impedance in future MEA-based neural devices. This will enable adaptive electrical stimulation or (re-)selection of recording electrodes by taking the actual state of the electrode into account.

Objective. Electrically evoked auditory steady-state responses (EASSRs) are potential neural responses for objectively determining stimulation parameters of cochlear implants (CIs). Unfortunately, they are difficult to detect in electroencephalography (EEG) recordings due to the electrical stimulation artifacts of the CI. This study investigates a novel stimulation paradigm hypothesized to improve artifact removal efficacy via system identification (SI), and therefore to improve response detection and clinical applicability. Approach. An amplitude-modulated (AM) CI stimulation pulse train with a step-wise increase in modulation frequency is created (referred to as SWEEP stimulation). Another stimulation is created by randomly shuffling modulation frequencies of the SWEEP stimulation (referred to as Shuffled-SWEEP stimulation). AM pulse trains with fixed modulation frequency (referred to as conventional AM stimulation), which elicit EASSRs, are also created for comparison. EEG data is collected from four CI users. A supra-threshold stimulation condition is used to investigate whether the SWEEP and Shuffled-SWEEP stimulation can elicit envelope-following responses (EFRs). A sub-threshold stimulation condition allows the collection of artifact-only EEG data, which is used to compare the SI accuracy on recordings from the SWEEP and the conventional AM stimulation. Main results. In all CI users, neural responses, following the SWEEP, Shuffled-SWEEP, and conventional AM stimulation are detected after artifact removal with SI. The validation with artifact-only EEG data shows higher F1 scores when comparing recordings with SWEEP stimulation (F1 = 0.9) to recordings with conventional AM stimulation (F1 = 0.82). Significance. Being able to accurately identify the response within one EEG recording enables the development of effective, online, objective fitting protocols. The increased neural response detection sensitivity with SWEEP stimulation reduces clinical recording time on average by a factor of 2.07. Detecting EFRs following complex stimulation paradigms offers a potential advancement in the systematic assessment of the temporal envelope processing in CI users.

Objective. Resting electroencephalographic activity is typically indistinguishable from a filtered linear random process across a diverse range of behavioural and pharmacological states, suggesting that the power spectral density of the resting electroencephalogram (EEG) can be modelled as the superposition of multiple, stochastically driven and independent, alpha band (8–13 Hz) relaxation oscillators. This simple model can account for variations in alpha band power and '1/f scaling' in eyes-open/eyes-closed conditions in terms of alterations in the distribution of the alpha band oscillatory relaxation rates. As changes in alpha band power and '1/f scaling' have been reported in anaesthesia we hypothesise that such changes may also be accounted for by alterations in alpha band relaxation oscillatory rate distributions. Approach. On this basis we choose to study the EEG activity of xenon and nitrous oxide, gaseous anaesthetic agents that have been reported to produce different EEG effects, notable given they are both regarded as principally acting via N-methyl-D-aspartate (NMDA) receptor antagonism. By recording high density EEG from participants receiving equilibrated step-level increases in inhaled concentrations of xenon (n = 24) and nitrous oxide (n = 20), alpha band relaxation rate (damping) distributions were estimated by solving an inhomogeneous integral equation describing the linear superposition of multiple alpha-band relaxation oscillators having a continuous distribution of dampings. Main results. For both agents, level-dependent reductions in alpha band power and spectral slope exponent (15–40 Hz) were observed, that were accountable by increases in mean alpha band damping. Significance. These shared increases suggest that, consistent with their identified molecular targets of action, xenon and nitrous oxide are mechanistically similar, a conclusion further supported by neuronal population modelling in which NMDA antagonism is associated with increases in damping and reductions in peak alpha frequency. Alpha band damping may provide an important link between experiment and theories of consciousness, such as the global neuronal network theory, where the likelihood of a globally excited state ('conscious percept'), is inversely related to mean damping.

Objective. Intracortical brain–computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as 'click-and-hold' or 'drag-and-drop'. Approach. Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 s in duration. We then designed a novel 'latch decoder' that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task. Main results. Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 s. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control. Significance. This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI.

Objective. There is a notable need of quantifiable and objective methods for the classification of schizophrenia. Patients with schizophrenia exhibit atypical eye movements compared with healthy individuals. To address this need, we have developed a classification model based on eye-tracking (ET) data to assist physicians in the intelligent auxiliary diagnosis of schizophrenia. Approach. This study employed three ET experiments—picture-free viewing, smooth pursuit tracking, and fixation stability—to collect ET data from patients with schizophrenia and healthy controls. The ET data of 292 participants (133 healthy controls and 159 patients with schizophrenia) were recorded. Utilizing ET data in picture-free viewing, we introduce a Resnet-based Attention Network for ET (RAnet-ET) integrated with the attention mechanism. RAnet-ET was trained by employing multiple loss functions to classify patients with schizophrenia and healthy controls. Furthermore, we proposed a classifier for handling multimodal features that combines specific features extracted from the well-trained RAnet-ET, 100 ET variables extracted from three ET experiments, and 19 MATRICS Consensus Cognitive Battery scores. Main results. The RAnet-ET achieved good performance in classifying schizophrenia, yielding an accuracy of 89.04%, a specificity of 90.56%, and an F1 score of 87.87%. The classification results based on multimodal features demonstrated improved performance, achieving 96.37% accuracy, 96.87% sensitivity, 95.87% specificity, and 96.37% F1 score. Significance. By integrating attention mechanisms, we designed RAnet-ET, which achieved good performance in classifying schizophrenia from free-viewing ET data. The synergistic combination of specific features extracted from the well-trained RAnet-ET, MCCB scores, and ET variables achieved exceptional classification performance, distinguishing individuals with schizophrenia from healthy controls. This study underscores the potential of our approach as a pivotal asset for the diagnosis of schizophrenia.

Objective. Machine learning's (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the validity and reproducibility of ML methods is critical. Unfortunately, this has not always been the case in practice, as there have been recent retractions across various scientific fields due to the misuse of ML methods and validation procedures. To address these concerns, we propose the first version of the neural engineering reproducibility and validity essentials for ML (NERVE-ML) checklist, a framework designed to promote the transparent, reproducible, and valid application of ML in neural engineering. Approach. We highlight some of the unique challenges of model validation in neural engineering, including the difficulties from limited subject numbers, repeated or non-independent samples, and high subject heterogeneity. Through detailed case studies, we demonstrate how different validation approaches can lead to divergent scientific conclusions, highlighting the importance of selecting appropriate procedures guided by the NERVE-ML checklist. Effectively addressing these challenges and properly scoping scientific conclusions will ensure that ML contributes to, rather than hinders, progress in neural engineering. Main results. Our case studies demonstrate that improper validation approaches can result in flawed studies or overclaimed scientific conclusions, complicating the scientific discourse. The NERVE-ML checklist effectively addresses these concerns by providing guidelines to ensure that ML approaches in neural engineering are reproducible and lead to valid scientific conclusions. Significance. By effectively addressing these challenges and properly scoping scientific conclusions guided by the NERVE-ML checklist, we aim to help pave the way for a future where ML reliably enhances the quality and impact of neural engineering research.

Objective. Sharing computational models offers many benefits, including increased scientific rigor during project execution, readership of the associated paper, resource usage efficiency, replicability, and reusability. In recognition of the growing practice and requirement of sharing models, code, and data, herein, we provide guidance to facilitate sharing of computational models by providing an accessible resource for regular reference throughout a project's stages. Approach. We synthesized literature on good practices in scientific computing and on code and data sharing with our experience in developing, sharing, and using models of neural stimulation, although the guidance will also apply well to most other types of computational models. Main results. We first describe the '6 R' characteristics of shared models, leaning on prior scientific computing literature, which enforce accountability and enable advancement: re-runnability, repeatability, replicability, reproducibility, reusability, and readability. We then summarize action items associated with good practices in scientific computing, including selection of computational tools during project planning, code and documentation design during development, and user instructions for deployment. We provide a detailed checklist of the contents of shared models and associated materials, including the model itself, code for reproducing published figures, documentation, and supporting datasets. We describe code, model, and data repositories, including a list of characteristics to consider when selecting a platform for sharing. We describe intellectual property (IP) considerations to balance permissive, open-source licenses versus software patents and bespoke licenses that govern and incentivize commercialization. Finally, we exemplify these practices with our ASCENT pipeline for modeling peripheral nerve stimulation. Significance. We hope that this paper will serve as an important and actionable reference for scientists who develop models—from project planning through publication—as well as for model users, institutions, IP experts, journals, funding sources, and repository platform developers.

The Tenth International brain–computer interface (BCI) meeting was held June 6–9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc & Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.

The brain is a highly complex physical system made of assemblies of neurons that work together to accomplish elaborate tasks such as motor control, memory and perception. How these parts work together has been studied for decades by neuroscientists using neuroimaging, psychological manipulations, and neurostimulation. Neurostimulation has gained particular interest, given the possibility to perturb the brain and elicit a specific response. This response depends on different parameters such as the intensity, the location and the timing of the stimulation. However, most of the studies performed so far used previously established protocols without considering the ongoing brain activity and, thus, without adaptively targeting the stimulation. In control theory, this approach is called open-loop control, and it is always paired with a different form of control called closed-loop, in which the current activity of the brain is used to establish the next stimulation. Recently, neuroscientists are beginning to shift from classical fixed neuromodulation studies to closed-loop experiments. This new approach allows the control of brain activity based on responses to stimulation and thus to personalize individual treatment in clinical conditions. Here, we review this new approach by introducing control theory and focusing on how these aspects are applied in brain studies. We also present the different stimulation techniques and the control approaches used to steer the brain. Finally, we explore how the closed-loop framework will revolutionize the way the human brain can be studied, including a discussion on open questions and an outlook on future advances.

Neurotechnologies are increasingly becoming integrated with our everyday lives, our bodies and our mental states. As the popularity and impact of neurotechnology grows, so does our responsibility to ensure we understand its particular implications on its end users, as well as broader ethical and societal implications. There are many different terms and frameworks to articulate the concept of involving end users in the technology development lifecycle, for example: 'Public and Patient Involvement and Engagement' (PPIE), 'lived experience', 'co-design' or 'co-production'. The objective of this tutorial is to utilise the PPIE framework to develop clear guidelines for implementing a robust involvement process of current and future end-users in neurotechnology, with emphasis on patient involvement. After an introduction that coveys the tangible and conceptual benefits of user involvement, we first guide the reader to develop a general strategy towards setting up their own PPIE process. We then help the reader map out their relevant stakeholders and provide advice on how to consider user diversity and representation. We also provide advice and tools on how to quantify the outcomes of the engagement. We consolidate advice from various online sources to orient individual teams (and their funders) to carve up their own approach to meaningful involvement. Key outputs include a stakeholder mapping tool, methods to measure the impact of engagement, and a structured checklist for transparent reporting. Enabling end-users and other stakeholders to participate in the development of neurotechnology, even at its earliest stages of conception, will help us better navigate our design around ethical, social, and usability considerations, and deliver more impactful technologies. The overall aim is the establishment of gold-standard methodologies for ensuring that patient and public insights are at the forefront of our scientific inquiry and product development.

Vibrotactile stimulation (VS) has been widely used as an appropriate motor imagery (MI) guidance strategy to improve MI performance. However, most vibrotactile stimulation induced by a single vibrator cannot provide spatiotemporal information of tactile sensation associated with the visual guidance of the imagined motion process, not vividly providing MI guidance for subjects. Methods: This paper proposed a paradigm with visual and spatiotemporal tactile synchronized stimulation (VSTSS) to provide vivid MI guidance to help subjects perform lower-limb MI tasks and improve MI-based brain-computer interface
(MI-BCI) performance, with a focus on poorly performing subjects. The proposed paradigm provided subjects with the natural spatiotemporal tactile sensation associated with the visual guidance of the foot movement process during MI. Experiments: Fourteen healthy subjects were recruited to participate in the MI and Rest tasks and divided into good and poor performers. Furthermore, electrophysiological features and classification performance were analyzed to assess motor cortical activation and MI-BCI performance under no VS (NVS), VS, and VSTSS. Results: The phenomenon of event-related desynchronization (ERD) in the sensorimotor cortex during MI under the VSTSS was more pronounced compared to the NVS and VS. Specifically, the VSTSS could improve the average ERD values in the motor cortex during the task segment by 34.70% and 14.28% than the NVS and VS in the alpha rhythm for poor performers, respectively. Additionally, the VSTSS could significantly enhance the classification accuracy between the MI and Rest tasks by 12.52% and 4.05% compared to
NVS and VS for poor performers, respectively. Conclusion: The proposed paradigm could enhance motor cortical activation during MI and classification performance by providing vivid MI guidance for subjects, offering a crucial promise for practical applications of lower-limb MI-BCI.

N/A

Objective: Brain-controlled functional electrical stimulation (FES) of the upper limb has been used to restore arm function to paralyzed individuals in the lab. Able-bodied individuals naturally modulate limb stiffness throughout movements and in anticipation of perturbations. Our goal is to develop, via simulation, a framework for incorporating stiffness modulation into the currently-used 'lookup-table-based' FES control systems while addressing several practical issues: 1) optimizing stimulation across muscles with overlap in function, 2) coordinating stimulation across joints, and 3) minimizing errors due to fatigue. Our calibration process also needs to account for when current spread causes additional muscles to become activated.
Approach: We developed an analytical framework for building a lookup-table-based FES controller and simulated the clinical process of calibrating and using the arm. A computational biomechanical model of a human paralyzed arm responding to stimulation was used for simulations with six muscles controlling the shoulder and elbow in the horizontal plane. Both joints had multiple muscles with overlapping functional effects, as well as biarticular muscles to reflect complex interactions between joints. Performance metrics were collected in silico, and real-time use was demonstrated with a Rhesus macaque using its cortical signals to control the computational arm model in real time.
Main Results: By explicitly including stiffness as a definable degree of freedom in the lookup table, our analytical approach was able to achieve all our performance criteria. While using more empirical data during controller parameterization produced more accurate lookup tables, interpolation between sparsely sampled points (e.g., 20 degree angular intervals) still produced good results with median endpoint position errors of less than 1 cm—a range that should be easy to correct for with real-time visual feedback.
Significance: Our simplified process for generating an effective FES controller now makes translating upper limb FES systems into mainstream clinical practice closer to reality. 

Transcranial magnetic resonance-guided focused ultrasound (MRgFUS) represents a transformative modality in treating neurological disorders and diseases, offering precise, minimally invasive interventions for conditions such as essential tremor and Parkinson's disease. This paper presents an industry-focused perspective on the current state of MRgFUS, highlighting recent advancements, challenges, and emerging opportunities within the field. We review key clinical applications and therapeutic mechanisms, focusing on targeted ablation, while discussing technological innovations that support new indications. Current regulatory frameworks, challenges in device development, and market trends are examined to provide an understanding of the industry landscape. Additionally, we indicate some limitations in MRgFUS and suggest potential strategies for overcoming these limitations to optimize treatment outcomes. We conclude with an outlook on promising developments, including AI-enhanced targeting, low and high-field MRI integration, and multimodal imaging techniques, that could potentially drive further innovation and adoption of MRgFUS in brain therapy.

Objective&#xD;To evaluate the effectiveness of a novel depth-based vision processing (VP) method, Local Background Enclosure (LBE), in comparison to the comprehensive VP method, Lanczos2 (L2), in suprachoroidal retinal prosthesis implant recipients during navigational tasks in laboratory and real-world settings. &#xD;Approach&#xD;Four participants were acclimatized to both VP methods. Participants were asked to detect and navigate past five of eight possible obstacles in a white corridor across 20-30 trials. Randomized obstacles included black or white mannequins, black or white overhanging boxes, black or white bins and black or white stationary boxes. The same four participants underwent trials at three different real-word urban locations using both VP methods (randomized order). They were tasked with navigating a complex, dynamic pre-determined scene while detecting, verbally identifying, and avoiding obstacles in their path. &#xD;Main results&#xD;The indoor obstacle course showed that the LBE method (63.6 ± 10.7%, mean ± SD) performed significantly better than L2 (48.5 ± 11.2%), for detection of obstacles (p<0.001, Mack-Skillings). The real-world assessment showed that of the objects detected, 50.2% (138/275) were correctly identified using LBE and 41.7% (138/331) using L2, corresponding to a risk difference of 8 percentage points, p=0.081). &#xD;Significance&#xD;Real world outcomes can be improved using an enhanced vision processing algorithm, providing depth-based visual cues (#NCT05158049). &#xD;&#xD;

Objective. Modern neural devices allow to interact with degenerated tissue in order to restore sensoric loss function and to suppress symptoms of neurodegenerative diseases using microelectronic arrays (MEA). They have a bidirectional interface for performing electrical stimulation to write-in new information and for recording the neural activity to read-out a neural task, e.g. movement ambitions. For both applications, the electrical impedance of the electrode-tissue interface (ETI) is crucial. However, the ETI can change during run-time due to encapsulation effects and changes of the neuronal structures. We investigated if an impedance spectrum can be reliably extracted from recordings during stimulation with microelectrode arrays. Approach. We present a measurement method for characterizing the electrical impedance spectrum during stimulation. We performed charge-controlled stimulation with a penetrating microelectrode array in an electrolyte solution. From the stimulation recordings, we extracted the impedance. Furthermore, a numerical model (digital twin) of the stimulation electrodes is established. Main results. We obtained consistent results for relevant electrochemical using electrochemical impedance spectroscopy, time-domain analysis and Fourier-transform-based impedance estimation. Moreover, the numerical simulations confirmed that the measured microelectrode had the expected properties. Significance. Our results pave the way to enable a live assessment of the impedance in future MEA-based neural devices. This will enable adaptive electrical stimulation or (re-)selection of recording electrodes by taking the actual state of the electrode into account.

Objective. Resting electroencephalographic activity is typically indistinguishable from a filtered linear random process across a diverse range of behavioural and pharmacological states, suggesting that the power spectral density of the resting electroencephalogram (EEG) can be modelled as the superposition of multiple, stochastically driven and independent, alpha band (8–13 Hz) relaxation oscillators. This simple model can account for variations in alpha band power and '1/f scaling' in eyes-open/eyes-closed conditions in terms of alterations in the distribution of the alpha band oscillatory relaxation rates. As changes in alpha band power and '1/f scaling' have been reported in anaesthesia we hypothesise that such changes may also be accounted for by alterations in alpha band relaxation oscillatory rate distributions. Approach. On this basis we choose to study the EEG activity of xenon and nitrous oxide, gaseous anaesthetic agents that have been reported to produce different EEG effects, notable given they are both regarded as principally acting via N-methyl-D-aspartate (NMDA) receptor antagonism. By recording high density EEG from participants receiving equilibrated step-level increases in inhaled concentrations of xenon (n = 24) and nitrous oxide (n = 20), alpha band relaxation rate (damping) distributions were estimated by solving an inhomogeneous integral equation describing the linear superposition of multiple alpha-band relaxation oscillators having a continuous distribution of dampings. Main results. For both agents, level-dependent reductions in alpha band power and spectral slope exponent (15–40 Hz) were observed, that were accountable by increases in mean alpha band damping. Significance. These shared increases suggest that, consistent with their identified molecular targets of action, xenon and nitrous oxide are mechanistically similar, a conclusion further supported by neuronal population modelling in which NMDA antagonism is associated with increases in damping and reductions in peak alpha frequency. Alpha band damping may provide an important link between experiment and theories of consciousness, such as the global neuronal network theory, where the likelihood of a globally excited state ('conscious percept'), is inversely related to mean damping.

Objective. Intracortical brain–computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as 'click-and-hold' or 'drag-and-drop'. Approach. Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 s in duration. We then designed a novel 'latch decoder' that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task. Main results. Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 s. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control. Significance. This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI.

N/A

Objective. Evoked compound action potentials (ECAPs) during spinal cord stimulation (SCS) may be useful in the treatment of chronic pain as a control signal for closed-loop neuromodulation. However, considerable inter-individual variability in evoked responses requires robust methods in order to realize effective, personalized pain management. These methods include artifact removal, feature extraction, classification, and prediction. Approach. We recorded ECAPs from eight participants with chronic pain undergoing an externalized trial with two percutaneous leads. The two most caudal electrodes were used for stimulation and the remaining electrodes were used for recording. Artifact-cleaned waveforms were clustered using principal component analysis and classified using a K-Nearest Neighbors classifier as non-ECAPs, ECAPs, or outlier (i.e. artifacts) to determine how well different features, including area under the curve (AUC) and peak-to-peak amplitude (P2P), discriminate between waveform classes. Finally, we used generalized linear mixed effects models to predict evoked response features and the probability of observing artifacts or ECAPs following individual stimulation pulses for different stimulation amplitudes, pulse widths, and polarities. Main results. AUC was better at discriminating between ECAPs and non-ECAPs than P2P (d' = 2.44 vs d' = 2.27) while most features were good at discriminating between ECAPs and artifacts (d' > 1.5). The application of an optimal AUC threshold was then used to analyze individual ECAPs at different stimulation amplitudes, pulse widths, and polarities. Interestingly, ECAPs could be evoked using ∼1.25 mA less current when using participant-specific, preferred stimulation polarities. Conversely, N1 latency consistently correlated with the location of the cathode. Significance. We developed an automated analysis pipeline for individual ECAPs during SCS. AUC was better than the widely used P2P for characterizing evoked responses. Furthermore, our modeling results provide a method for predicting how various stimulation parameters affect SCS responses in individual participants. The study registered on ClinicalTrials.gov (#NCT04938245).

Objective. Neurons exhibit deterministic behavior influenced by stochastic cellular or extracellular components. Estimating this random component is challenging due to unknown underlying deterministic dynamics. In this study, we aim to estimate the neural random component, termed intrinsic dynamic neural noise, from experimental time series without prior assumptions on the underlying neural model. Approach. The method relies on the nonlinear approximate entropy profile and was evaluated using synthetic data from Izhikevich's models and simulated calcium dynamics driven by dynamical noise. We then applied the method to experimental time series from calcium imaging in mice and zebrafish brain regions, as well as electrophysiological data from a 128-channel cortical probe in anesthetized rats. Main results. The results show region-specific behavior, with higher dynamic neural noise in the somatosensory cortex of mice and anterior telencephalic area of zebrafish. Furthermore, neuronal stochasticity is greater in genetically encoded $\text{Ca}^{2+}$ indicators than in $\text{Ca}^{2+}$ dyes, and neural noise increases with recording depth. Significance. These findings offer insights into neural dynamics and suggest dynamic noise as a key biomarker.

Spinal cord injury (SCI) affects over 250 000 individuals in the US. Brain–computer interfaces (BCIs) may improve quality of life by controlling external devices. Invasive intracortical BCIs have shown promise in clinical trials but degrade in the chronic period and tether patients to acquisition hardware. Alternatively, electrocorticography (ECoG) records data from electrodes on the cortex, and studies evaluating fully implanted BCI-ECoG systems are scarce. Objective. We seek to address this need using a fully implanted ECoG-based BCI that allows for home use in SCI.Approach. The patient used a long-term BCI system, initially controlling an functional electrical stimulation orthosis in the lab and later using an external mechanical orthosis at home. To evaluate its long-term viability, electrode contact impedance, signal quality, and decoder performance were measured. Signal quality was assessed using signal-to-noise ratio and maximum bandwidth of the signal. Decoder performance was monitored using the area under the receiver operator characteristic curve (AUROC). Main results. The study analyzed data from the patient's home environment over 54 months, revealing that the device was used at home for 38 ± 24 min on average daily. After six months, we observed stable event-related desynchronization that aided in determining the onset of motor intention. The decoder's average AUROC across months was 0.959. Importantly, 40 months of the data collected was gather from the subject's home or community environment. The results indicate long-term ECoG recordings were stable for motor-imagery classification and motor control in the community environment in a case of an individual with SCI. Significance. This study presents the long-term feasibility and viability of an ECoG-based BCI system that persists in the home environment in a case of SCI. Future research should explore larger electrode counts with more participants to confirm this stability. Understanding these trends is crucial for clinical utility and chronic viability in broader patient populations.

Objective. Elucidating neurological processes in the mammalian brain requires improved methods for imaging and detecting neuronal subtypes. Transgenic mouse models utilizing Cre/lox recombination have been developed to selectively label neuronal subtypes with fluorophores, however, light-scattering attenuation of both excitation light and emission light limits their effective range of detection. Approach. To overcome these limitations, this study investigates the use of a near-infrared fluorophore, iRFP713, for subtype labeling of neurons found within brain regions that are typically inaccessible by optical methods. Towards this goal, a custom photoacoustic (PA) system is developed for detection of iRFP in neurons in brain slices, expressed via Cre/lox, and within in vitro cell culture. Main results. In this study, a custom system is developed to detect iRFP in neuronal cells both in brain slices and in vitro. Furthermore, this work validates iRFP expression in the brains of transgenic mice and neuronal cell culture. Significance. Combining iRFP with advanced imaging and detection strategies, such as PA microscopy, is critical for expanding the type and variety of neurons that scientists can observe within the mammalian brain.

Objective. Voluntary control of motor actions requires precise regulation of proprioceptive and somatosensory functions. While aging is known to impair sensory processing, its effect on proprioception remains unclear. Previous studies report conflicting findings on whether passive proprioception (i.e. during externally driven movements) declines with age, and research on age-related changes in active proprioception (i.e. during voluntary movements) remains limited, particularly in the upper limb. Understanding these changes is critical for identifying and preventing impairments that may affect movement performance and mobility, particularly in neurological conditions such as stroke or Parkinson's disease. Approach. We refined a robotic protocol to assess upper-limb active proprioception and validated its robustness and reliability over multiple sessions. Using this protocol, we compared the performance between young and elderly neurologically healthy adults during both active and passive proprioceptive tasks. Main results. Elderly participants exhibited a significant decline in accuracy when sensing limb position in both active and passive proprioceptive tasks, whereas their precision remained unchanged. These findings indicate that aging primarily affects proprioceptive accuracy rather than variability in position sense. Significance. Our findings contribute to the ongoing debate on age-related proprioceptive decline and highlight the importance of distinguishing between active and passive proprioception. Furthermore, our validated robotic protocol provides a reliable tool for assessing proprioception, with potential applications in studying neurological conditions in clinical settings.

Objective. Non-invasive spinal stimulation has the potential to modulate spinal excitability. This study explored the modulatory capacity of sub-motor grid-based transcutaneous spinal cord stimulation (tSCS) applied to the lumbar spinal cord in neurologically intact participants. Our objective was to examine the effect of grid spinal stimulation on polysynaptic reflex pathways involving motoneurons and interneurons likely activated by Aβ/δ fiber-mediated cutaneous afferents. Approach. Stimulation was delivered using two grid electrode montages, generating a net electric field in transverse or diagonal directions. We administered tSCS with the center of the grid aligned with the T10–T11 spinous process. Participants were seated for the 20 min stimulation duration. At 30 min after the cessation of spinal stimulation, we examined neuromodulatory effects on spinal circuit excitability in the tibialis anterior muscle by employing the classical flexion reflex paradigms. Additionally, we evaluated spinal motoneuron excitability using the H-reflex paradigm in the soleus muscle to explore the differential effects of tSCS on the polysynaptic versus monosynaptic reflex pathway and to test the spatial extent of the grid stimulation. Main results. Our findings indicated significant neuromodulatory effects on the flexion reflex, resulting in a net inhibitory effect, regardless of the grid electrode montages. Our data further indicated that the flexion reflex duration was significantly shortened only by the diagonal montage. Significance. Our results suggest that grid-based tSCS may specifically modulate spinal activities associated with polysynaptic flexion reflex pathways, with the potential for grid-specific targeted neuromodulation.