Marcia Bockbrader

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.

Role of intensity-modulated radiation therapy in gastrointestinal cancer

Intensity-modulated radiation therapy (IMRT) represents a powerful advance in the planning and delivery of radiation therapy owing to its ability to deliver highly conformal treatment doses while sparing normal tissues. Dosimetric studies have shown the feasibility and theoretical benefit of treating with IMRT over 3D-conformal radiation therapy in gastrointestinal malignancies. Early clinical experience with IMRT in the treatment of gastric, pancreatic, rectal and anal cancers corroborates the dosimetric analyses, with some series reporting lower normal tissue toxicities. This article reviews the radiobiological, physical, technical and clinical aspects of IMRT for gastric, pancreatic, rectal and anal cancer, and summarizes the dosimetric and outcome studies to date.

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.

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.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.

A feasibility study using interactive graphic art feedback to augment acute neurorehabilitation therapy

BACKGROUND Interactive arts technologies, designed to augment the acute neurorehabilitation provided by expert therapists, may overcome existing barriers of access for patients with low motor and cognitive function. OBJECTIVES Develop an application prototype to present movement feedback interactively and creatively. Evaluate feasibility of use within acute neurorehabilitation. METHODS Record demographics and Functional Independent Measure™ scores among inpatients who used the technology during physical, occupational or recreational therapy. Record exercises performed with the technology, longest exercise duration performed (calculated from sensor data), user feedback, and therapist responses to a validated technology assessment questionnaire. RESULTS Inpatients (n = 21) between the ages of 19 and 86 (mean 57 ± 18; 12 male/9 female) receiving treatment for motor deficits associated with neuropathology used the application in conjunction with occupational, recreational, or physical therapy during 1 to 7 sessions. Patients classified on the Functional Independence Measure™ as requiring 75%+ assistance for cognitive and motor function were able to use the interactive application. CONCLUSIONS Customized interactive arts applications are appropriate for further study as a therapeutic modality. In addition to providing interactivity to individuals with low motor function, interactive arts applications might serve to augment activity-based medicine among inpatients with low problem-solving and memory function.

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 transducers 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.

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

ABSTRACT 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.

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.Intracortical activity data recorded over 2 years in a tetraplegic patient is used to develop an artificial intelligence algorithm that achieves fast, accurate, and stable movement decoding to reenable real-time control of the paralyzed forearm.

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 users 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 users 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.

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.