Luis A. Chui

CLINICAL FINDINGS IN MUSK-ANTIBODY POSITIVE MYASTHENIA GRAVIS: A U.S. EXPERIENCE

We performed a retrospective chart review on 53 muscle‐specific kinase antibody (MuSK‐Ab)‐positive myasthenia gravis (MG) patients at nine university‐based centers in the U.S. Of these, 66% were Caucasian, 85% were women, and age of onset was 9–79 years. Twenty‐seven patients were nonresponsive to anticholinesterase therapy. Myasthenia Gravis Foundation of America improvement status was achieved in 53% patients on corticosteroids, 51% with plasma exchange, and in 20% on intravenous immunoglobulin (IVIG). Thymectomy was beneficial in 7/18 patients at 3 years. Long‐term (≥3 years) outcome was very favorable in 58% of patients who achieved remission and/or minimal manifestation status. Overall, 73% improved. There was one MG‐related death. This survey reinforces several cardinal features of MuSK‐Ab‐positive MG, including prominent bulbar involvement and anticholinesterase nonresponsiveness. Facial or tongue atrophy was rare. Most patients respond favorably to immunotherapy. The best clinical response was to corticosteroids and plasma exchange, and the poorest response was to IVIG. Long‐term outcome is favorable in about 60% of cases. Muscle Nerve, 2009

Operation of a brain-computer interface walking simulator for individuals with spinal cord injury

BackgroundSpinal cord injury (SCI) can leave the affected individuals with paraparesis or paraplegia, thus rendering them unable to ambulate. Since there are currently no restorative treatments for this population, novel approaches such as brain-controlled prostheses have been sought. Our recent studies show that a brain-computer interface (BCI) can be used to control ambulation within a virtual reality environment (VRE), suggesting that a BCI-controlled lower extremity prosthesis for ambulation may be feasible. However, the operability of our BCI has not yet been tested in a SCI population.MethodsFive participants with paraplegia or tetraplegia due to SCI underwent a 10-min training session in which they alternated between kinesthetic motor imagery (KMI) of idling and walking while their electroencephalogram (EEG) were recorded. Participants then performed a goal-oriented online task, where they utilized KMI to control the linear ambulation of an avatar while making 10 sequential stops at designated points within the VRE. Multiple online trials were performed in a single day, and this procedure was repeated across 5 experimental days.ResultsClassification accuracy of idling and walking was estimated offline and ranged from 60.5% (p = 0.0176) to 92.3% (p = 1.36×10−20) across participants and days. Offline analysis revealed that the activation of mid-frontal areas mostly in the μ and low β bands was the most consistent feature for differentiating between idling and walking KMI. In the online task, participants achieved an average performance of 7.4±2.3 successful stops in 273±51 sec. These performances were purposeful, i.e. significantly different from the random walk Monte Carlo simulations (p<0.01), and all but one participant achieved purposeful control within the first day of the experiments. Finally, all participants were able to maintain purposeful control throughout the study, and their online performances improved over time.ConclusionsThe results of this study demonstrate that SCI participants can purposefully operate a self-paced BCI walking simulator to complete a goal-oriented ambulation task. The operation of the proposed BCI system requires short training, is intuitive, and robust against participant-to-participant and day-to-day neurophysiological variations. These findings indicate that BCI-controlled lower extremity prostheses for gait rehabilitation or restoration after SCI may be feasible in the future.

Tubular aggregates in subclinical alcoholic myopathy

A 34-year-old chronic alcoholic with acute alcoholic intoxication was found to have extensive aggregates on muscle biopsy performed 48 hours after admission. Forearm ischemic exercise failed to demonstrate normal generation of lactic acid. Pathologic changes in the muscle biopsy consisted of subsarcolemmal accumulations of bright purple-red material with trichrome reaction. This material stained darkly with NADH-TR but was unstained with myofibrillar ATPase and PAS. Ultrastructural studies revealed that these regions contained tubular aggregates. A second biopsy 7 days later failed to demonstrate any significant abnormalities. Two weeks later, lactate generation was normal. Previous observations by other authors that tubular aggregates may be concerned with correction of metabolic defect or detoxification of endogenous toxins could apply in our case.

A co-registration approach for electrocorticogram electrode localization using post-implantation MRI and CT of the head

Electrocorticogram (ECoG) signals are acquired from electrodes that are surgically implanted into the subdural space of the brain. Although this procedure is usually performed for clinical purposes such as defining seizure locations and/or brain mapping, ECoG signals can also be used for characterizing the electrophysiology underlying various behaviors or for brain-computer interface applications. Therefore, defining the anatomical location of ECoG electrodes is an important process for contextual interpretation of the results. Current techniques utilize semi-automated statistical methods to co-register ECoG electrodes from either post-implantation X-rays or computer tomography (CT) images with a pre-implantation magnetic resonance imaging (MRI) of the brain. However, due to brain deformation caused by surgical electrode implantation, ECoG electrode locations must be projected onto the brain surface of the pre-implantation MRI, which may result in error. The authors present an exploratory study where post-implantation MRI images were successfully used for co-registration with post-implantation CT images of ECoG electrodes without the need for projection. By using postimplantation CT and MRI images which preserve the brain deformation, error in defining ECoG electrode locations may be reduced or eliminated.

Electrocorticographic Encoding of Human Gait in the Leg Primary Motor Cortex

While prior noninvasive (e.g., electroencephalographic) studies suggest that the human primary motor cortex (M1) is active during gait processes, the limitations of noninvasive recordings make it impossible to determine whether M1 is involved in high-level motor control (e.g., obstacle avoidance, walking speed), low-level motor control (e.g., coordinated muscle activation), or only nonmotor processes (e.g., integrating/relaying sensory information). This study represents the first invasive electroneurophysiological characterization of the human leg M1 during walking. Two subjects with an electrocorticographic grid over the interhemispheric M1 area were recruited. Both exhibited generalized γ-band (40-200 Hz) synchronization across M1 during treadmill walking, as well as periodic γ-band changes within each stride (across multiple walking speeds). Additionally, these changes appeared to be of motor, rather than sensory, origin. However, M1 activity during walking shared few features with M1 activity during individual leg muscle movements, and was not highly correlated with lower limb trajectories on a single channel basis. These findings suggest that M1 primarily encodes high-level gait motor control (i.e., walking duration and speed) instead of the low-level patterns of leg muscle activation or movement trajectories. Therefore, M1 likely interacts with subcortical/spinal networks, which are responsible for low-level motor control, to produce normal human walking.

State and trajectory decoding of upper extremity movements from electrocorticogram

Electrocorticography has been widely explored as a long-term signal acquisition platform for brain-computer interface (BCI) control of upper extremity prostheses. However, a comprehensive study of elementary upper extremity movements and their relationship to electrocorticogram (ECoG) signals has yet to be performed. This study examines whether kinematic parameters of 6 elementary upper extremity movements can be decoded from ECoG signals in 3 subjects undergoing subdural electrode placement for epilepsy surgery evaluation. To this end, we propose a 2-stage decoding approach that consists of a state decoder to determine idle/move states, followed by a Kalman filter-based trajectory decoder. This proposed decoder successfully classified idle/move states with an average accuracy of 91%, and the correlation between decoded and measured trajectory averaged 0.70 for position and 0.68 for velocity. These performances represent an improvement over a simple regression-based approach.

Electrocorticogram encoding of upper extremity movement trajectories

Electrocorticogram (ECoG)-based brain computer interfaces (BCI) can potentially control upper extremity pros-theses to restore independent function to paralyzed individuals. However, current research is mostly restricted to the offline decoding of finger or 2D arm movement trajectories, and these results are modest. This study seeks to improve the fundamental understanding of the ECoG signal features underlying upper extremity movements to guide better BCI design. Subjects undergoing ECoG electrode implantation performed a series of elementary upper extremity movements in an intermittent flexion and extension manner. It was found that movement velocity, θ̇, had a high positive (negative) correlation with the instantaneous power of the ECoG high-γ band (80-160 Hz) during flexion (extension). Also, the correlation was low during idling epochs. Visual inspection of the ECoG high-γ band revealed power bursts during flexion/extension events that had a waveform that strongly resembled the corresponding flexion/extension event as seen on θ̇. These high-γ bursts were present in all elementary movements, and were spatially distributed in a somatotopic fashion. Thus, it can be concluded that the high-γ power of ECoG strongly encodes for movement trajectories, and can be used as an input feature in future BCIs.

Sensitivity and specificity of upper extremity movements decoded from electrocorticogram

Electrocorticogram (ECoG)-based brain computer interfaces (BCI) can potentially be used for control of arm prostheses. Restoring independent function to BCI users with such a system will likely require control of many degrees-of-freedom (DOF). However, our ability to decode many-DOF arm movements from ECoG signals has not been thoroughly tested. To this end, we conducted a comprehensive study of the ECoG signals underlying 6 elementary upper extremity movements. Two subjects undergoing ECoG electrode grid implantation for epilepsy surgery evaluation participated in the study. For each task, their data were analyzed to design a decoding model to classify ECoG as idling or movement. The decoding models were found to be highly sensitive in detecting movement, but not specific in distinguishing between different movement types. Since sensitivity and specificity must be traded-off, these results imply that conventional ECoG grids may not provide sufficient resolution for decoding many-DOF upper extremity movements.

Characterization of electrocorticogram high-gamma signal in response to varying upper extremity movement velocity.

The mechanism by which the human primary motor cortex (M1) encodes upper extremity movement kinematics is not fully understood. For example, human electrocorticogram (ECoG) signals have been shown to modulate with upper extremity movements; however, this relationship has not been explicitly characterized. To address this issue, we recorded high-density ECoG signals from patients undergoing epilepsy surgery evaluation as they performed elementary upper extremity movements while systematically varying movement speed and duration. Specifically, subjects performed intermittent pincer grasp/release, elbow flexion/extension, and shoulder flexion/extension at slow, moderate, and fast speeds. In all movements, bursts of power in the high-