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 (> or =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.

Abstract.Objective.Spinal cord injury (SCI) often leaves affected individuals unable to ambulate. Electroencephalogramme (EEG) based brain-computer interface (BCI) controlled lower extremity prostheses may restore intuitive and able-body-like ambulation after SCI. To test its feasibility, the authors developed and tested a novel EEG-based, data-driven BCI system for intuitive and self-paced control of the ambulation of an avatar within a virtual reality environment (VRE). Approach. Eight able-bodied subjects and one with SCI underwent the following 10-min training session: subjects alternated between idling and walking kinaesthetic motor imageries (KMI) while their EEG were recorded and analysed to generate subject-specific decoding models. Subjects then performed a goal-oriented online task, repeated over 5 sessions, in which they utilised the KMI to control the linear ambulation of an avatar and make 10 sequential stops at designated points within the VRE. Main results. The average offline training performance across subjects was 77.2±9.5%, ranging from 64.3% (p = 0.00176) to 94.5% (p = 6.26×10 −23 ), with chance performance being 50%. The average online performance was 8.4±1.0 (out of 10) successful stops and 303±53 sec completion time (perfect = 211 sec). All subjects achieved performances significantly different than those of random walk (p<0.05) in 44 of the 45 online sessions. Significance. By using a data-driven machine learning approach to decode users' KMI, this BCI-VRE system enabled intuitive and purposeful self-paced control of ambulation after only a 10-minute training. The ability to achieve such BCI control with minimal training indicates that the implementation of future BCI-lower extremity prosthesis systems may be feasible.

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.

Commonly used ECoG grids are inadequate for multi-DOF BCI arm prostheses. The performance gains by HD grids may eventually lead to independence-restoring BCI arm prosthesis.

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.