Human primary motor cortex indexes the onset of subjective intention in brain-machine-interface mediated actions.

Self-initiated behavior is accompanied by the experience of willing our actions. Here, we leverage the unique opportunity to examine the full intentional chain - from will (W) to action (A) to environmental effects (E) - in a tetraplegic person fitted with a primary motor cortex (M1) brain machine interface (BMI) generating hand movements via neuromuscular electrical stimulation (NMES). This combined BMI-NMES approach allowed us to selectively manipulate each element of the intentional chain (W, A, and E) while performing extra-cellular recordings and probing subjective experience. Our results reveal single-cell, multi-unit, and population-level dynamics in human M1 that encode W and may predict its subjective onset. Further, we show that the proficiency of a neural decoder in M1 reflects the degree of W-A binding, tracking the participant's subjective experience of intention in (near) real time. These results point to M1 as a critical node in forming the subjective experience of intention and demonstrate the relevance of intention-related signals for translational neuroprosthetics.

Sense of agency for intracortical brain-machine interfaces.

Intracortical brain-machine interfaces decode motor commands from neural signals and translate them into actions, enabling movement for paralysed individuals. The subjective sense of agency associated with actions generated via intracortical brain-machine interfaces, the neural mechanisms involved and its clinical relevance are currently unknown. By experimentally manipulating the coherence between decoded motor commands and sensory feedback in a tetraplegic individual using a brain-machine interface, we provide evidence that primary motor cortex processes sensory feedback, sensorimotor conflicts and subjective states of actions generated via the brain-machine interface. Neural signals processing the sense of agency affected the proficiency of the brain-machine interface, underlining the clinical potential of the present approach. These findings show that primary motor cortex encodes information related to action and sensing, but also sensorimotor and subjective agency signals, which in turn are relevant for clinical applications of brain-machine interfaces.

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.

A double-blind, randomized, controlled study of two dose strengths of dalfampridine extended release on walking deficits in ischemic stroke.

BACKGROUND: Stroke-induced ischemia affects both cortex and underlying white matter. Dalfampridine extended release tablets (D-ER) enhance action potential conduction in demyelinated axons, which may positively affect post-stroke recovery. OBJECTIVE: Based on promising preliminary data, we compared efficacy of D-ER administered at 7.5 mg or 10 mg with placebo on post-stroke ambulation. Primary study outcome (response) was a ≥20% increase on the 2-minute walk test (2 MinWT) at 12 weeks after first drug administration. METHODS: This was a multicenter, randomized, placebo-controlled, 3-arm, parallel-group, safety and efficacy trial. After obtaining baseline measures of 2 MinWT, Walk-12, and Timed Up and Go, subjects entered a 2-week, single-blind placebo run-in period and were randomized 1:1:1 to receive 7.5 mg D-ER, 10 mg D-ER, or placebo, dosed twice-daily for 12 weeks. Follow-up evaluations occurred at weeks 14 and 16 when subjects were off study drug. RESULTS: The study was terminated early with 377 of planned 540 patients enrolled, due to no treatment effect. At week 12, mean increase in distances walked in 2 minutes were similar among the 3 study groups (14.9±40.0 feet; 19.4±39.6 feet; and 20.4±38.3 feet for placebo, 7.5 mg D-ER, and 10 mg D-ER, respectively). The proportion of subjects who showed ≥20% improvement on 2 MinWT at week 12 was 13.5%, 14.0%, and 19.0%, for placebo, 7.5 mg D-ER, and 10 mg D-ER, respectively; these were nonsignificant changes from baseline for all groups. CONCLUSIONS: D-ER at either a 7.5-mg or 10-mg dose did not significantly increase performance on the 2 MinWT in stroke survivors with gait impairment, although this study was terminated early before full enrollment. (Clinical Trial # NCT02271217).

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.

Beyond Bones: Assessing Whether Ultrasound-Aided Instruction and Practice Improve Unassisted Soft Tissue Palpation Skills of First-Year Medical Students.

OBJECTIVES: Our purpose was to determine whether ultrasound (US)-aided instruction and practice on musculoskeletal anatomy would improve first-year medical students' ability to locate and identify specific soft tissue structures by unaided palpation in the upper and lower extremities of healthy human models. METHODS: This study was a randomized crossover design with 49 first-year medical students randomly assigned to 1 of 2 groups. Each group was provided expert instruction and hands-on practice using US to scan and study soft tissue structures. During session 1, group A learned the anatomy of the upper extremities, whereas group B learned the lower. Students were then tested on their proficiency in locating 4 soft tissue structures (2 upper and 2 lower extremities) through palpation of a human model. During session 2, group A learned lower extremities, and group B learned upper. At the end of session 2, students repeated the assessment. RESULTS: After the first instructional session, neither group performed significantly better on identifying and locating the soft tissue landmarks they learned aided by US. After the second instructional session, however, scores for both groups increased approximately 20 percentage points, indicating that both groups performed significantly better on palpating and identifying both the upper and lower extremity soft tissue landmarks (Cohen d = 0.89 and 0.82, respectively). CONCLUSIONS: Time and practice viewing soft tissue structures with US assistance seems to have a "palpation-with-eyes" effect that improves students' abilities to correctly locate, palpate, and identify limb-specific soft tissue structures once the US assistance is removed.

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.

Randomized Sham-Controlled Trial of Navigated Repetitive Transcranial Magnetic Stimulation for Motor Recovery in Stroke.

Background and Purpose- We aimed to determine whether low-frequency electric field navigated repetitive transcranial magnetic stimulation to noninjured motor cortex versus sham repetitive transcranial magnetic stimulation avoiding motor cortex could improve arm motor function in hemiplegic stroke patients when combined with motor training. Methods- Twelve outpatient US rehabilitation centers enrolled participants between May 2014 and December 2015. We delivered 1 Hz active or sham repetitive transcranial magnetic stimulation to noninjured motor cortex before each of eighteen 60-minute therapy sessions over a 6-week period, with outcomes measured at 1 week and 1, 3, and 6 months after end of treatment. The primary end point was the percentage of participants improving ≥5 points on upper extremity Fugl-Meyer score 6 months after end of treatment. Secondary analyses assessed changes on the upper extremity Fugl-Meyer and Action Research Arm Test and Wolf Motor Function Test and safety. Results- Of 199 participants, 167 completed treatment and follow-up because of early discontinuation of data collection. Upper extremity Fugl-Meyer gains were significant for experimental ( P<0.001) and sham groups ( P<0.001). Sixty-seven percent of the experimental group (95% CI, 58%-75%) and 65% of sham group (95% CI, 52%-76%) improved ≥5 points on 6-month upper extremity Fugl-Meyer ( P=0.76). There was also no difference between experimental and sham groups in the Action Research Arm Test ( P=0.80) or the Wolf Motor Function Test ( P=0.55). A total of 26 serious adverse events occurred in 18 participants, with none related to the study or device, and with no difference between groups. Conclusions- Among patients 3 to 12 months poststroke, goal-oriented motor rehabilitation improved motor function 6 months after end of treatment. There was no difference between the active and sham repetitive transcranial magnetic stimulation trial arms. Clinical Trial Registration- URL: https://www.clinicaltrials.gov . Unique identifier: NCT02089464.

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.

Does Ultrasound-Enhanced Instruction of Musculoskeletal Anatomy Improve Physical Examination Skills of First-Year Medical Students?

OBJECTIVES: Ultrasound imaging is commonly used to teach basic anatomy to medical students. The purpose of this study was to determine whether learning musculoskeletal anatomy with ultrasound improved performance on medical students' musculoskeletal physical examination skills. METHODS: Twenty-seven first-year medical students were randomly assigned to 1 of 2 instructional groups: either shoulder or knee. Both groups received a lecture followed by hands-on ultrasound scanning on live human models of the assigned joint. After instruction, students were assessed on their ability to accurately palpate 4 anatomic landmarks: the acromioclavicular joint, the proximal long-head biceps tendon, and the medial and lateral joint lines of the knee. Performance scores were based on both accuracy and time. A total physical examination performance score was derived for each joint. Scores for instructional groups were compared by a 2-way analysis of variance with 1 repeated measure. Significant findings were further analyzed with post hoc tests. RESULTS: All students performed significantly better on the knee examination, irrespective of instructional group (F = 14.9; df = 1.25; P = .001). Moreover, the shoulder instruction group performed significantly better than the knee group on the overall assessment (t = -3.0; df = 25; P < .01). Post hoc analyses revealed that differences in group performance were due to the shoulder group's higher scores on palpation of the biceps tendon (t = -2.8; df = 25; P = .01), a soft tissue landmark. Both groups performed similarly on palpation of all other anatomic structures. CONCLUSIONS: The use of ultrasound appears to provide an educational advantage when learning musculoskeletal physical examination of soft tissue landmarks.

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.

Language of Transducer Manipulation: Codifying Terms for Effective Teaching.

There is a need for consistent, repetitive, and reliable terminology to describe the basic manipulations of the ultrasound transducer. Previously, 5 basic transducer motions have been defined and used in education. However, even with this effort, there is still a lack of consistency and clarity in describing transducer manipulation and motion. In this technical innovation, we describe an expanded definition of transducer motions, which include movements to change the transducer's angle of insonation to the target as well as the location on the body to optimize the ultrasound image. This new terminology may allow for consistent teaching and improved communication in the process of image acquisition.

The Current State of Musculoskeletal Ultrasound Education in Physical Medicine and Rehabilitation Residency Programs.

BACKGROUND: Exposure to musculoskeletal ultrasound (MSUS) is now a mandatory component of physical medicine and rehabilitation (PM&R) residency training. However, reports on the extent of the implementation and efficacy of MSUS education are lacking in the literature. OBJECTIVE: To determine the extent to which PM&R residencies are implementing MSUS education. DESIGN: Cross-sectional. SETTING: Institutional. PARTICIPANTS: Thirty-six of the 78 United States PM&R residency programs accredited by the Accreditation Council for Graduate Medical Education. METHODS: All 78 programs were solicited with an online survey via the residency program director and coordinator in July 2014. The 25 questions on the survey were aimed at determining program MSUS educational characteristics and their effectiveness. MAIN OUTCOME MEASURES: Description of teaching methods used for MSUS, residency demographics, characteristics of MSUS faculty expertise, and faculty-perceived competency in MSUS examinations and procedures among residents. Data were analyzed using both descriptive statistics and tests for independence to identify correlations between program characteristics and resident MSUS competency. RESULTS: A response was received from 36 of the 78 residency programs (46.2%). Of the 36 residency programs that responded, 97.2% provide exposure to MSUS (a figure that drops to 44.9% when nonrespondents are included); 61% had mandatory MSUS training (28.2% when including nonrespondents); and 44.4% had a formal curriculum (20.5% when including nonrespondents). The most common MSUS educational tools used were lecture (88.9%), outpatient clinic (86.1%), and hands-on workshops (86.1%). Sixty-one percent of responding programs evaluate residents with formal assessment tools. Overall, faculty at 38.8% and 44.4% of programs believed that at least 50% of residents who graduate are competent in diagnostic and interventional MSUS, respectively. These rates were significantly associated with the use of formal assessment. CONCLUSION: MSUS education is growing in PM&R, but many programs still have not adopted a formal educational curriculum. Formal assessment to evaluate resident MSUS skills significantly improves faculty-perceived MSUS competency.