Long-term intracortical microelectrode array performance in a human: a 5 year retrospective analysis.

Objective. Brain-computer interfaces (BCIs) that record neural activity using intracortical microelectrode arrays (MEAs) have shown promise for mitigating disability associated with neurological injuries and disorders. While the chronic performance and failure modes of MEAs have been well studied and systematically described in non-human primates, there is far less reported about long-term MEA performance in humans. Our group has collected one of the largest neural recording datasets from a Utah MEA in a human subject, spanning over 5 years (2014-2019). Here we present both long-term signal quality and BCI performance as well as highlight several acute signal disruption events observed during the clinical study.Approach. Long-term Utah array performance was evaluated by analyzing neural signal metric trends and decoding accuracy for tasks regularly performed across 448 clinical recording sessions. For acute signal disruptions, we identify or hypothesize the root cause of the disruption, show how the disruption manifests in the collected data, and discuss potential identification and mitigation strategies for the disruption.Main results. Neural signal quality metrics deteriorated rapidly within the first year, followed by a slower decline through the remainder of the study. Nevertheless, BCI performance remained high 5 years after implantation, which is encouraging for the translational potential of this technology as an assistive device. We also present examples of unanticipated signal disruptions during chronic MEA use, which are critical to detect as BCI technology progresses toward home usage.Significance. Our work fills a gap in knowledge around long-term MEA performance in humans, providing longevity and efficacy data points to help characterize the performance of implantable neural sensors in a human population. The trial was registered on ClinicalTrials.gov (Identifier NCT01997125) and conformed to institutional requirements for the conduct of human subjects research.

Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: A Review.

Brain-machine interfaces (BMIs) record and translate neural activity into a control signal for assistive or other devices. Intracortical microelectrode arrays (MEAs) enable high degree-of-freedom BMI control for complex tasks by providing fine-resolution neural recording. However, chronically implanted MEAs are subject to a dynamic in vivo environment where transient or systematic disruptions can interfere with neural recording and degrade BMI performance. Typically, neural implant failure modes have been categorized as biological, material, or mechanical. While this categorization provides insight into a disruption's causal etiology, it is less helpful for understanding degree of impact on BMI function or possible strategies for compensation. Therefore, we propose a complementary classification framework for intracortical recording disruptions that is based on duration of impact on BMI performance and requirement for and responsiveness to interventions: (1) Transient disruptions interfere with recordings on the time scale of minutes to hours and can resolve spontaneously; (2) Reversible disruptions cause persistent interference in recordings but the root cause can be remedied by an appropriate intervention; (3) Irreversible compensable disruptions cause persistent or progressive decline in signal quality, but their effects on BMI performance can be mitigated algorithmically; and (4) Irreversible non-compensable disruptions cause permanent signal loss that is not amenable to remediation or compensation. This conceptualization of intracortical BMI disruption types is useful for highlighting specific areas for potential hardware improvements and also identifying opportunities for algorithmic interventions. We review recording disruptions that have been reported for MEAs and demonstrate how biological, material, and mechanical mechanisms of disruption can be further categorized according to their impact on signal characteristics. Then we discuss potential compensatory protocols for each of the proposed disruption classes. Specifically, transient disruptions may be minimized by using robust neural decoder features, data augmentation methods, adaptive machine learning models, and specialized signal referencing techniques. Statistical Process Control methods can identify reparable disruptions for rapid intervention. In-vivo diagnostics such as impedance spectroscopy can inform neural feature selection and decoding models to compensate for irreversible disruptions. Additional compensatory strategies for irreversible disruptions include information salvage techniques, data augmentation during decoder training, and adaptive decoding methods to down-weight damaged channels.

Restoring the Sense of Touch Using a Sensorimotor Demultiplexing Neural Interface.

Paralyzed muscles can be reanimated following spinal cord injury (SCI) using a brain-computer interface (BCI) to enhance motor function alone. Importantly, the sense of touch is a key component of motor function. Here, we demonstrate that a human participant with a clinically complete SCI can use a BCI to simultaneously reanimate both motor function and the sense of touch, leveraging residual touch signaling from his own hand. In the primary motor cortex (M1), residual subperceptual hand touch signals are simultaneously demultiplexed from ongoing efferent motor intention, enabling intracortically controlled closed-loop sensory feedback. Using the closed-loop demultiplexing BCI almost fully restored the ability to detect object touch and significantly improved several sensorimotor functions. Afferent grip-intensity levels are also decoded from M1, enabling grip reanimation regulated by touch signaling. These results demonstrate that subperceptual neural signals can be decoded from the cortex and transformed into conscious perception, significantly augmenting function.

Toward a Consensus for Musculoskeletal Ultrasonography Education in Physical Medicine and Rehabilitation: A National Poll of Residency Directors.

OBJECTIVES: The aims of the study were to evaluate integration of musculoskeletal ultrasonography education in physical medicine and rehabilitation training programs in 2014-2015, when the American Academy of Physical Medicine & Rehabilitation and Accreditation Council for Graduate Medical Education Residency Review Committee both recognized it as a fundamental component of physiatric practice, to identify common musculoskeletal ultrasonography components of physical medicine and rehabilitation residency curricula, and to identify common barriers to integration. DESIGN: Survey of 78 Accreditation Council for Graduate Medical Education-accredited physical medicine and rehabilitation residency programs was conducted. RESULTS: The 2015 survey response rate was more than 50%, and respondents were representative of programs across the United States. Most programs (80%) reported teaching musculoskeletal ultrasonography, whereas a minority (20%) required mastery of ultrasonography skills for graduation. Ultrasonography curricula varied, although most programs agreed that the scope of resident training in physical medicine and rehabilitation should include diagnostic and interventional musculoskeletal ultrasonography, especially for key joints (shoulder, elbow, knee, wrist, hip, and ankle) and nerves (median, ulnar, fibular, tibial, radial, and sciatic). Barriers to teaching included insufficient expertise of instructors, poor access to equipment, and lack of a structured curriculum. CONCLUSIONS: Musculoskeletal ultrasonography has become a required component of physical medicine and rehabilitation residency training. Based on survey responses and expert recommendations, we propose a structure for musculoskeletal ultrasonography curricular standards and milestones for trainee competency.

A High Definition Noninvasive Neuromuscular Electrical Stimulation System for Cortical Control of Combinatorial Rotary Hand Movements in a Human With Tetraplegia.

OBJECTIVE: Paralysis resulting from spinal cord injury (SCI) can have a devastating effect on multiple arm and hand motor functions. Rotary hand movements, such as supination and pronation, are commonly impaired by upper extremity paralysis, and are essential for many activities of daily living. In this proof-of-concept study, we utilize a neural bypass system (NBS) to decode motor intention from motor cortex to control combinatorial rotary hand movements elicited through stimulation of the arm muscles, effectively bypassing the SCI of the study participant. We describe the NBS system architecture and design that enabled this functionality. METHODS: The NBS consists of three main functional components: 1) implanted intracortical microelectrode array, 2) neural data processing using a computer, and, 3) a noninvasive neuromuscular electrical stimulation (NMES) system. RESULTS: We address previous limitations of the NBS, and confirm the enhanced capability of the NBS to enable, in real-time, combinatorial hand rotary motor functions during a functionally relevant object manipulation task. CONCLUSION: This enhanced capability was enabled by accurate decoding of multiple movement intentions from the participant's motor cortex, interleaving NMES patterns to combine hand movements, and dynamically switching between NMES patterns to adjust for hand position changes during movement. SIGNIFICANCE: These results have implications for enabling complex rotary hand functions in sequence with other functionally relevant movements for patients suffering from SCI, stroke, and other sensorimotor dysfunctions.

A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent.

Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals. Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. For both decoders, we found that response time increases (i.e., slower reaction) and accuracy decreases as the number of functions increases. However, we also found that both the increase of response times and the decline in accuracy with additional functions is less for the DNN than the SVM. We also show that data preprocessing steps can affect the performance characteristics of the two decoders in drastically different ways. Finally, we evaluated the performance of our tetraplegic participant using the DNN decoder in real-time to control functional electrical stimulation (FES) of his paralyzed forearm. We compared his performance to that of able-bodied participants performing the same task, establishing a quantitative target for ideal BCI-FES performance on this task. Cumulatively, these results help quantify BCI decoder performance characteristics relevant to potential users and the complex interactions between them.

Brain Computer Interfaces in Rehabilitation Medicine.

One innovation currently influencing physical medicine and rehabilitation is brain-computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.

Meeting brain-computer interface user performance expectations using a deep neural network decoding framework.

Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices1-9. Surveys of potential end-users have identified key BCI system features10-14, including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm1,15, which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network16 decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracortical data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure3,17-20, responds faster than competing methods8, and can increase functionality with minimal retraining by using a technique known as transfer learning21. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT)22. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology.

Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications.

BACKGROUND: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain. METHODS: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (lf-MWP, 0-234 Hz), mid-frequency MWP (mf-MWP, 234 Hz-3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3 years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings. RESULTS: All neural features could stably track neural information for over 3 years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings. CONCLUSIONS: Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications. TRIAL REGISTRATION: This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125).

Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human.

Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients' own paralyzed limbs. To date, human studies have demonstrated an "all-or-none" type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate wrist flexion states that are intermediate to training levels. These innovations provide the groundwork for enabling enhanced and more natural fine motor control of paralyzed limbs by BCI-FES neuroprosthetics.

Reducing concussion symptoms among teenage youth: Evaluation of a mobile health app.

OBJECTIVE: To evaluate whether a mobile health application that employs elements of social game design could compliment medical care for unresolved concussion symptoms. DESIGN: Phase I and Phase II (open-label, non-randomized, ecological momentary assessment methodology). SETTING: Outpatient concussion clinic. PARTICIPANTS: Youth, aged 13-18 years, with concussion symptoms 3+ weeks after injury; Phase I: n = 20; Phase II: n = 19. INTERVENTIONS: Participants received standard of care for concussion. The experimental group also used a mobile health application as a gamified symptoms journal. OUTCOME MEASURES: Phase I: feasibility and satisfaction with intervention (7-point Likert scale, 1 high). Phase II: change in SCAT-3 concussion symptoms (primary), depression and optimism. RESULTS: Phase 1: A plurality of participants completed the intervention (14 of 20) with high use (110 +/- 18% play) and satisfaction (median +/- interquartile range (IQR) = 2.0+/- 0.0). Phase II: Groups were equivalent on baseline symptoms, intervention duration, gender distribution, days since injury and medication prescription. Symptoms and optimism improved more for the experimental than for the active control cohort (U = 18.5, p = 0.028, effect size r = 0.50 and U = 18.5, p = 0.028, effect size r = 0.51, respectively). CONCLUSIONS: Mobile apps incorporating social game mechanics and a heroic narrative may promote health management among teenagers with unresolved concussion symptoms.

Restoring cortical control of functional movement in a human with quadriplegia.

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5-C6) to the seventh cervical to first thoracic (C7-T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.

Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.

Subjective perceptual distortions and visual dysfunction in children with autism.

Case reports and sensory inventories suggest that autism involves sensory processing anomalies. Behavioral tests indicate impaired motion and normal form perception in autism. The present study used first-person accounts to investigate perceptual anomalies and related subjective to psychophysical measures. Nine high-functioning children with autism and nine typically-developing children were given a questionnaire to assess the frequency of sensory anomalies, as well as psychophysical tests of visual perception. Results indicated that children with autism experience increased perceptual anomalies, deficits in trajectory discrimination consistent with dysfunction in the cortical dorsal pathway or in cerebellar midsagittal vermis, and high spatial frequency contrast impairments consistent with dysfunctional parvocellular processing. Subjective visual hypersensitivity was significantly related to greater deficits across vision tests.

Personality traits in schizophrenia and related personality disorders.

We investigated whether schizophrenia spectrum disorders share common personality characteristics or traits. Participants with a diagnosis of schizophrenia or schizoaffective disorder (SZ) or with a schizophrenia spectrum personality disorder (schizophrenia spectrum PD: schizoid, paranoid, and schizotypal personality disorder) were compared with non-psychiatric control subjects on the five-factor model of personality and the psychosis-proneness scales. On the five-factor personality scales, SZ subjects showed higher levels of neuroticism, and lower levels of openness, agreeableness, extraversion, and conscientiousness than control subjects. Higher scores on openness and lower scores on neuroticism distinguished schizophrenia spectrum PD from SZ. On the psychosis-proneness scales, both PD and SZ participants scored high relative to non-psychiatric control participants on magical ideation and perceptual aberration, while PD participants scored intermediate between non-psychiatric control participants and SZ on social anhedonia. Discriminant analysis indicated that schizophrenia spectrum patients could be distinguished from PDs by more severe social withdrawal and maladjustment, while subjects with PDs could be best distinguished from control subjects on the basis of odd or novel ideation and decreased conscientiousness.

Steady state visual evoked potential abnormalities in schizophrenia.

OBJECTIVE: The steady state visual evoked potential (SSVEP) can be used to test the frequency response function of neural circuits. Previous studies have shown reduced SSVEPs to alpha and lower frequencies of stimulation in schizophrenia. We investigated SSVEPs in schizophrenia at frequencies spanning the theta (4Hz) to gamma (40Hz) range. METHODS: The SSVEPs to seven different frequencies of stimulation (4, 8, 17, 20, 23, 30 and 40Hz) were obtained from 18 schizophrenia subjects and 33 healthy control subjects. Power at stimulating frequency (signal power) and power at frequencies above and below the stimulating frequency (noise power) were used to quantify the SSVEP responses. RESULTS: Both groups showed an inverse relationship between power and frequency of stimulation. Schizophrenia subjects showed reduced signal power compared to healthy control subjects at higher frequencies (above 17Hz), but not at 4 and 8Hz at occipital region. Noise power was higher in schizophrenia subjects at frequencies between 4 and 20Hz over occipital region and at 4, 17 and 20Hz over frontal region. CONCLUSIONS: SSVEP signal power at beta and gamma frequencies of stimulation were reduced in schizophrenia. Schizophrenia subjects showed higher levels of EEG noise during photic stimulation at beta and lower frequencies. SIGNIFICANCE: Inability to generate or maintain oscillations in neural networks may contribute to deficits in visual processing in schizophrenia.