An Accurate and Rapidly Calibrating Speech Neuroprosthesis.

BACKGROUND: Brain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy. METHODS: A 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice. RESULTS: On the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours. CONCLUSIONS: In a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.).

Distinct Biomarkers of ANT Stimulation and Seizure Freedom in an Epilepsy Patient with Ambulatory Hippocampal Electrocorticography.

INTRODUCTION: Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) and responsive neurostimulation (RNS) of the hippocampus are the predominant approaches to brain stimulation for treating mesial temporal lobe epilepsy (MTLE). Both are similarly effective at reducing seizures in drug-resistant patients, but the underlying mechanisms are poorly understood. In rare cases where it is clinically indicated to use RNS and DBS simultaneously, ambulatory electrophysiology from RNS may provide the opportunity to measure the effects of ANT DBS in the putative seizure onset zone and identify biomarkers associated with clinical improvement. Here, one such patient became seizure free, allowing us to identify and compare the changes in hippocampal electrophysiology associated with ANT stimulation and seizure freedom. METHODS: Ambulatory electrocorticography and clinical history were retrospectively analyzed for a patient treated with RNS and DBS for MTLE. DBS artifacts were used to identify ANT stimulation periods on RNS recordings and measure peri-stimulus electrographic changes. Clinical history was used to determine the chronic electrographic changes associated with seizure freedom. RESULTS: ANT stimulation acutely suppressed hippocampal gamma (25-90Hz) power, with minimal theta (4-8Hz) suppression and without clear effects on seizure frequency. Eventually, the patient became seizure free alongside the emergence of chronic gamma increase and theta suppression, which started at the same time as clobazam was introduced. Both seizure freedom and the associated electrophysiology persisted after inadvertent DBS discontinuation, further implicating the clobazam relationship. Unexpectedly, RNS detections and long episodes increased, although they were not considered to be electrographic seizures, and the patient remained clinically seizure free. CONCLUSION: ANT stimulation and seizure freedom were associated with distinct, dissimilar spectral changes in RNS-derived electrophysiology. The time course of these changes supported a new medication as the most likely cause of clinical improvement. Broadly, this work showcases the use of RNS recordings to interpret the effects of multimodal therapy. Specifically, it lends additional credence to hippocampal theta suppression as a biomarker previously associated with seizure reduction in RNS patients.

The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models.

The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p(observation|state) is nonlinear. We argue that in many cases, a model for p(state|observation) proves both easier to learn and more accurate for latent state estimation. Approximating p(state|observation) as gaussian leads to a new filtering algorithm, the discriminative Kalman filter (DKF), which can perform well even when p(observation|state) is highly nonlinear and/or nongaussian. The approximation, motivated by the Bernstein-von Mises theorem, improves as the dimensionality of the observations increases. The DKF has computational complexity similar to the Kalman filter, allowing it in some cases to perform much faster than particle filters with similar precision, while better accounting for nonlinear and nongaussian observation models than Kalman-based extensions. When the observation model must be learned from training data prior to filtering, off-the-shelf nonlinear and nonparametric regression techniques can provide a gaussian model for p(observation|state) that cleanly integrates with the DKF. As part of the BrainGate2 clinical trial, we successfully implemented gaussian process regression with the DKF framework in a brain-computer interface to provide real-time, closed-loop cursor control to a person with a complete spinal cord injury. In this letter, we explore the theory underlying the DKF, exhibit some illustrative examples, and outline potential extensions.

The clinical utility of the Spinal Instability Neoplastic Score (SINS) system in spinal epidural metastases: a retrospective study.

STUDY DESIGN: A retrospective study. OBJECTIVES: This study assessed the clinical utility of the Spinal Instability Neoplastic Score (SINS) in relation to the surgical treatment of spinal epidural metastasis and factors important for surgical decision-making. These factors include epidural spinal cord compression (ESCC), patient prognosis and neurologic status. SETTING: Queen Elizabeth II Health Sciences Centre, Halifax, Canada. METHODS: We identified 285 patients with spinal metastatic disease. Data were extracted through a retrospective review. SINS and ESCC were scored based on CT and MRI, respectively. RESULTS: Patients were grouped into stable (35%), potentially unstable (52%), and unstable (13%) groups. The overall incidence of metastatic spinal deformity was 9%. Surgical interventions were performed in 21% of patients, including decompression and instrumented fusion (70%), decompression alone (17%), percutaneous vertebral augmentation (9%), and instrumented vertebral augmentation (5%). The use of spinal instrumentation was significantly associated with unstable SINS (p = 0.005). Grade 3 ESCC was also significantly associated with unstable SINS (p < 0.001). Kaplan-Meier analysis revealed that SINS was not a predictor of survival (p = 0.98). In the radiotherapy-alone group, a significant proportion of patients with potentially unstable SINS (30%) progressed into unstable SINS category at an average 364 ± 244 days (p < 0.001). CONCLUSION: This study demonstrated that more severe categories of SINS were associated with higher degrees of ESCC, and surgical interventions were more often utilized in this group with more frequent placement of spinal instrumentation. Although SINS did not predict patient prognosis, it correlates with the progression of metastatic instability in patients treated with radiotherapy.

Modelling the backlog of COVID-19 cases for a surgical group.

Summary: The coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact on health care delivery and has resulted in a backlog of patients needing surgery. There is a lack of experience and guidance in dealing with this increased demand on an already overburdened health care system. We created an online tool (www.covidbacklog.com) that helps surgeons explore how resource allocation within their group will affect wait times for patients. After inputting a handful of readily available variables, the computer program generates a forecast of how long it will take to see the backlog of patients. This information could be used to allow surgical groups to run simulations to explore different resource allocation strategies in order to help prevent downstream consequences of delayed patient care.

Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression.

Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testing data sets. This includes changes to the neural tuning profiles and baseline shifts in firing rates of recorded neurons, as well as nonphysiological noise. While progress has been made toward providing long-term user control via decoder recalibration, relatively little work has been dedicated to making the decoding algorithm more resilient to signal nonstationarities. Here, we describe how principled kernel selection with gaussian process regression can be used within a Bayesian filtering framework to mitigate the effects of commonly encountered nonstationarities. Given a supervised training set of (neural features, intention to move in a direction)-pairs, we use gaussian process regression to predict the intention given the neural data. We apply kernel embedding for each neural feature with the standard radial basis function. The multiple kernels are then summed together across each neural dimension, which allows the kernel to effectively ignore large differences that occur only in a single feature. The summed kernel is used for real-time predictions of the posterior mean and variance under a gaussian process framework. The predictions are then filtered using the discriminative Kalman filter to produce an estimate of the neural intention given the history of neural data. We refer to the multiple kernel approach combined with the discriminative Kalman filter as the MK-DKF. We found that the MK-DKF decoder was more resilient to nonstationarities frequently encountered in-real world settings yet provided similar performance to the currently used Kalman decoder. These results demonstrate a method by which neural decoding can be made more resistant to nonstationarities.

Spike train SIMilarity Space (SSIMS): a framework for single neuron and ensemble data analysis.

Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large-scale ensemble activity beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how spike train SIMilarity space (SSIMS) analysis captures the relationship between goal directions for an eight-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.

Needs Assessment for Incoming PGY-1 Residents in Neurosurgical Residency.

BACKGROUND: Residents must develop a diverse range of skills in order to practice neurosurgery safely and effectively. The purpose of this study was to identify the foundational skills required for neurosurgical trainees as they transition from medical school to residency. METHODS: Based on the CanMEDS competency framework, a web-based survey was distributed to all Canadian academic neurosurgical centers, targeting incoming and current PGY-1 neurosurgical residents as well as program directors. Using Likert scale and free-text responses, respondents rated the importance of various cognitive (e.g. management of raised intracranial pressure), technical (e.g. performing a lumbar puncture) and behavioral skills (e.g. obtaining informed consent) required for a PGY-1 neurosurgical resident. RESULTS: Of 52 individuals contacted, 38 responses were received. Of these, 10 were from program directors (71%), 11 from current PGY-1 residents (58%) and 17 from incoming PGY-1 residents (89%). Respondents emphasized operative skills such as proper sterile technique and patient positioning; clinical skills such as lesion localization and interpreting neuro-imaging; management skills for common scenarios such as raised intracranial pressure and status epilepticus; and technical skills such as lumbar puncture and external ventricular drain placement. Free text answers were concordant with the Likert scale results. DISCUSSION: We surveyed Canadian neurosurgical program directors and PGY-1 residents to identify areas perceived as foundational to neurosurgical residency education and training. This information is valuable for evaluating the appropriateness of a training program's goals and objectives, as well as for generating a national educational curriculum for incoming PGY-1 residents.

Teaching for the Transition: the Canadian PGY-1 Neurosurgery 'Rookie Camp'.

BACKGROUND: Transitioning from medical school to residency is difficult and stressful, necessitating innovation in easing this transition. In response, a Canadian neurosurgical Rookie Camp was designed and implemented to foster acquisition of technical, cognitive and behavioral skills among incoming Canadian post graduate year one (PGY-1) neurosurgery residents. METHODS: The inaugural Rookie Camp was held in July 2012 in Halifax. The curriculum was developed based on a national needs-assessment and consisted of a pre-course manual, 7 case-based stations, 4 procedural skills stations and 2 group discussions. The content was clinically focused, used a variety of teaching methods, and addressed multiple CanMEDS competencies. Evaluation included participant and faculty surveys and a pre-course, post-course, and 3-month retention knowledge test. RESULTS: 17 of 23 PGY-1 Canadian neurosurgical residents participated in the Camp. All agreed the course content was relevant for PGY-1 training and the experience prepared them for residency. All participants would recommend the course to future neurosurgical residents. A statistically significant improvement was observed in knowledge related to course content (F(2,32) = 7.572, p<0.002). There were no significant differences between post-test and retention-test scores at three months. CONCLUSION: The inaugural Canadian Neurosurgery Rookie Camp for PGY-1 residents was successfully delivered, with engagement from participants, training programs, the Canadian Neurosurgical Society, and the Royal College. In addition to providing fundamental knowledge, which was shown to be retained, the course eased junior residents' transition to residency by fostering camaraderie and socialization within the specialty.

An instantaneous voice synthesis neuroprosthesis.

Brain computer interfaces (BCIs) have the potential to restore communication to people who have lost the ability to speak due to neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text1-3. However, text communication fails to capture the nuances of human speech such as prosody, intonation and immediately hearing one's own voice. Here, we demonstrate a "brain-to-voice" neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding neural activity from 256 microelectrodes implanted into the ventral precentral gyrus of a man with amyotrophic lateral sclerosis and severe dysarthria. We overcame the challenge of lacking ground-truth speech for training the neural decoder and were able to accurately synthesize his voice. Along with phonemic content, we were also able to decode paralinguistic features from intracortical activity, enabling the participant to modulate his BCI-synthesized voice in real-time to change intonation, emphasize words, and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.

BRAND: a platform for closed-loop experiments with deep network models.

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1 ms chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 ms of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.Significance.By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.

An accurate and rapidly calibrating speech neuroprosthesis.

Brain-computer interfaces can enable rapid, intuitive communication for people with paralysis by transforming the cortical activity associated with attempted speech into text on a computer screen. Despite recent advances, communication with brain-computer interfaces has been restricted by extensive training data requirements and inaccurate word output. A man in his 40's with ALS with tetraparesis and severe dysarthria (ALSFRS-R = 23) was enrolled into the BrainGate2 clinical trial. He underwent surgical implantation of four microelectrode arrays into his left precentral gyrus, which recorded neural activity from 256 intracortical electrodes. We report a speech neuroprosthesis that decoded his neural activity as he attempted to speak in both prompted and unstructured conversational settings. Decoded words were displayed on a screen, then vocalized using text-to-speech software designed to sound like his pre-ALS voice. On the first day of system use, following 30 minutes of attempted speech training data, the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. On the second day, the size of the possible output vocabulary increased to 125,000 words, and, after 1.4 additional hours of training data, the neuroprosthesis achieved 90.2% accuracy. With further training data, the neuroprosthesis sustained 97.5% accuracy beyond eight months after surgical implantation. The participant has used the neuroprosthesis to communicate in self-paced conversations for over 248 hours. In an individual with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore naturalistic communication after a brief training period.

Responsive neurostimulation as a treatment for super-refractory focal status epilepticus: a systematic review and case series.

OBJECTIVE: Super-refractory status epilepticus (SRSE) has high rates of morbidity and mortality. Few published studies have investigated neurostimulation treatment options in the setting of SRSE. This systematic literature review and series of 10 cases investigated the safety and efficacy of implanting and activating the responsive neurostimulation (RNS) system acutely during SRSE and discusses the rationale for lead placement and selection of stimulation parameters. METHODS: Through a literature search (of databases and American Epilepsy Society abstracts that were last searched on March 1, 2023) and direct contact with the manufacturer of the RNS system, 10 total cases were identified that utilized RNS acutely during SE (9 SRSE cases and 1 case of refractory SE [RSE]). Nine centers obtained IRB approval for retrospective chart review and completed data collection forms. A tenth case had published data from a case report that were referenced in this study. Data from the collection forms and the published case report were compiled in Excel. RESULTS: All 10 cases presented with focal SE: 9 with SRSE and 1 with RSE. Etiology varied from known lesion (focal cortical dysplasia in 7 cases and recurrent meningioma in 1) to unknown (2 cases, with 1 presenting with new-onset refractory focal SE [NORSE]). Seven of 10 cases exited SRSE after RNS placement and activation, with a time frame ranging from 1 to 27 days. Two patients died of complications due to ongoing SRSE. Another patient's SE never resolved but was subclinical. One of 10 cases had a device-related significant adverse event (trace hemorrhage), which did not require intervention. There was 1 reported recurrence of SE after discharge among the cases in which SRSE resolved up to the defined endpoint. CONCLUSIONS: This case series offers preliminary evidence that RNS is a safe and potentially effective treatment option for SRSE in patients with 1-2 well-defined seizure-onset zone(s) who meet the eligibility criteria for RNS. The unique features of RNS offer multiple benefits in the SRSE setting, including real-time electrocorticography to supplement scalp EEG for monitoring SRSE progress and response to treatment, as well as numerous stimulation options. Further research is indicated to investigate the optimal stimulation settings in this unique clinical scenario.

A mosaic of whole-body representations in human motor cortex.

Understanding how the body is represented in motor cortex is key to understanding how the brain controls movement. The precentral gyrus (PCG) has long been thought to contain largely distinct regions for the arm, leg and face (represented by the "motor homunculus"). However, mounting evidence has begun to reveal a more intermixed, interrelated and broadly tuned motor map. Here, we revisit the motor homunculus using microelectrode array recordings from 20 arrays that broadly sample PCG across 8 individuals, creating a comprehensive map of human motor cortex at single neuron resolution. We found whole-body representations throughout all sampled points of PCG, contradicting traditional leg/arm/face boundaries. We also found two speech-preferential areas with a broadly tuned, orofacial-dominant area in between them, previously unaccounted for by the homunculus. Throughout PCG, movement representations of the four limbs were interlinked, with homologous movements of different limbs (e.g., toe curl and hand close) having correlated representations. Our findings indicate that, while the classic homunculus aligns with each area's preferred body region at a coarse level, at a finer scale, PCG may be better described as a mosaic of functional zones, each with its own whole-body representation.

Using Automatic Speech Recognition to Measure the Intelligibility of Speech Synthesized from Brain Signals.

Brain-computer interfaces (BCIs) can potentially restore lost function in patients with neurological injury. A promising new application of BCI technology has focused on speech restoration. One approach is to synthesize speech from the neural correlates of a person who cannot speak, as they attempt to do so. However, there is no established gold-standard for quantifying the quality of BCI-synthesized speech. Quantitative metrics, such as applying correlation coefficients between true and decoded speech, are not applicable to anarthric users and fail to capture intelligibility by actual human listeners; by contrast, methods involving people completing forced-choice multiple-choice questionnaires are imprecise, not practical at scale, and cannot be used as cost functions for improving speech decoding algorithms. Here, we present a deep learning-based "AI Listener" that can be used to evaluate BCI speech intelligibility objectively, rapidly, and automatically. We begin by adapting several leading Automatic Speech Recognition (ASR) deep learning models - Deepspeech, Wav2vec 2.0, and Kaldi - to suit our application. We then evaluate the performance of these ASRs on multiple speech datasets with varying levels of intelligibility, including: healthy speech, speech from people with dysarthria, and synthesized BCI speech. Our results demonstrate that the multiple-language ASR model XLSR-Wav2vec 2.0, trained to output phonemes, yields superior performance in terms of speech transcription accuracy. Notably, the AI Listener reports that several previously published BCI output datasets are not intelligible, which is consistent with human listeners.

BRAND: A platform for closed-loop experiments with deep network models.

Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++). To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed nodes , which communicate with each other in a graph via streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes. In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1-millisecond chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 milliseconds of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems. By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.

Percutaneous trigeminal tractotomy for trigeminal neuralgia: Postoperative MRI findings.

BACKGROUND AND PURPOSE: Percutaneous trigeminal tractotomy is an ablative procedure that can be used to treat trigeminal neuralgia in patients who have failed prior pharmacologic and surgical treatments. Using perioperative computed tomography (CT) guidance, ablation of the descending spinal trigeminal nucleus and trigeminal tract can be performed precisely to mitigate damage to surrounding structures. These patients are subsequently followed with postoperative imaging and clinical visits to assess long-term pain relief. METHODS: In this report, we present a series of four patients with trigeminal neuralgia who were had refractory disease after prior medical and surgical interventions. These patients underwent CT-guided percutaneous trigeminal tractotomy for pain relief. The patients underwent postoperative MRI and were followed for up to 6 months for long-term clinical outcomes. RESULTS: For intraoperative CT, we find that preprocedure lumbar contrast injection enables better visualization of the cord during placement of the ablation probe. On postoperative imaging, we find that all four patients have hyperintense lesions on T2-weighted MRI that correspond with the location of the trigeminal nucleus and tract. Three patients had short-term pain relief, one of which continued to have long-term relief. CONCLUSION: Intraoperative CT and postoperative MRI serve as useful modalities for confirming localization, evaluating complications, and can be used as a metric for quality control.

Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia.

OBJECTIVE: Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-and-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI. METHODS: Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial completed on-screen item selection tasks to assess iBCI-enabled cursor control. RESULTS: Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home. SIGNIFICANCE: Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to high-performance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment.

Raloxifene prevents stress granule dissolution, impairs translational control and promotes cell death during hypoxia in glioblastoma cells.

Glioblastoma (GBM) is the most common primary malignant brain tumor, and it has a uniformly poor prognosis. Hypoxia is a feature of the GBM microenvironment, and previous work has shown that cancer cells residing in hypoxic regions resist treatment. Hypoxia can trigger the formation of stress granules (SGs), sites of mRNA triage that promote cell survival. A screen of 1120 FDA-approved drugs identified 129 candidates that delayed the dissolution of hypoxia-induced SGs following a return to normoxia. Amongst these candidates, the selective estrogen receptor modulator (SERM) raloxifene delayed SG dissolution in a dose-dependent manner. SG dissolution typically occurs by 15 min post-hypoxia, however pre-treatment of immortalized U251 and U3024 primary GBM cells with raloxifene prevented SG dissolution for up to 2 h. During this raloxifene-induced delay in SG dissolution, translational silencing was sustained, eIF2α remained phosphorylated and mTOR remained inactive. Despite its well-described role as a SERM, raloxifene-mediated delay in SG dissolution was unaffected by co-administration of ß-estradiol, nor did ß-estradiol alone have any effect on SGs. Importantly, the combination of raloxifene and hypoxia resulted in increased numbers of late apoptotic/necrotic cells. Raloxifene and hypoxia also demonstrated a block in late autophagy similar to the known autophagy inhibitor chloroquine (CQ). Genetic disruption of the SG-nucleating proteins G3BP1 and G3BP2 revealed that G3BP1 is required to sustain the raloxifene-mediated delay in SG dissolution. Together, these findings indicate that modulating the stress response can be used to exploit the hypoxic niche of GBM tumors, causing cell death by disrupting pro-survival stress responses and control of protein synthesis.

Decoding speech from spike-based neural population recordings in secondary auditory cortex of non-human primates.

Direct electronic communication with sensory areas of the neocortex is a challenging ambition for brain-computer interfaces. Here, we report the first successful neural decoding of English words with high intelligibility from intracortical spike-based neural population activity recorded from the secondary auditory cortex of macaques. We acquired 96-channel full-broadband population recordings using intracortical microelectrode arrays in the rostral and caudal parabelt regions of the superior temporal gyrus (STG). We leveraged a new neural processing toolkit to investigate the choice of decoding algorithm, neural preprocessing, audio representation, channel count, and array location on neural decoding performance. The presented spike-based machine learning neural decoding approach may further be useful in informing future encoding strategies to deliver direct auditory percepts to the brain as specific patterns of microstimulation.