Classification of Movement and Inhibition Using a Hybrid BCI.

Brain-computer interfaces (BCIs) are an emerging technology that are capable of turning brain electrical activity into commands for an external device. Motor imagery (MI)-when a person imagines a motion without executing it-is widely employed in BCI devices for motor control because of the endogenous origin of its neural control mechanisms, and the similarity in brain activation to actual movements. Challenges with translating a MI-BCI into a practical device used outside laboratories include the extensive training required, often due to poor user engagement and visual feedback response delays; poor user flexibility/freedom to time the execution/inhibition of their movements, and to control the movement type (right arm vs. left leg) and characteristics (reaching vs. grabbing); and high false positive rates of motion control. Solutions to improve sensorimotor activation and user performance of MI-BCIs have been explored. Virtual reality (VR) motor-execution tasks have replaced simpler visual feedback (smiling faces, arrows) and have solved this problem to an extent. Hybrid BCIs (hBCIs) implementing an additional control signal to MI have improved user control capabilities to a limited extent. These hBCIs either fail to allow the patients to gain asynchronous control of their movements, or have a high false positive rate. We propose an immersive VR environment which provides visual feedback that is both engaging and immediate, but also uniquely engages a different cognitive process in the patient that generates event-related potentials (ERPs). These ERPs provide a key executive function for the users to execute/inhibit movements. Additionally, we propose signal processing strategies and machine learning algorithms to move BCIs toward developing long-term signal stability in patients with distinctive brain signals and capabilities to control motor signals. The hBCI itself and the VR environment we propose would help to move BCI technology outside laboratory environments for motor rehabilitation in hospitals, and potentially for controlling a prosthetic.

Cutaneous cryptococcosis in a patient taking fingolimod for multiple sclerosis: Here come the opportunistic infections?

BACKGROUND: Fingolimod is an oral disease-modifying therapy for relapsing forms of multiple sclerosis, which acts by sequestering lymphocytes within lymph nodes. OBJECTIVE: To describe a case of extrapulmonary cryptococcosis in a patient taking fingolimod. METHODS: Case report. RESULTS: A 47-year-old man developed a non-healing skin lesion approximately 16 months after starting treatment with fingolimod. Biopsy revealed cryptococcosis. Fingolimod was discontinued and the lesion resolved with antifungal therapy. CONCLUSION: Despite few reported opportunistic infections in the pivotal clinical trials and first few years post-marketing, there has been a recent increase in reported AIDS-defining illnesses in patients taking fingolimod. Neurologists should be alert for opportunistic infections in their patients using this medication.

Successful treatment of paroxysmal ataxia and dysarthria in multiple sclerosis with levetiracetam.

BACKGROUND: Paroxysmal ataxia and dysarthria (PAD) is a relatively rare symptom in Multiple Sclerosis patients. PAD involves transient dysfunction in control, coordination and initiation of speech and/or limb movements. OBJECTIVE: To describe the successful use of levetiracetam for the treatment of PAD. METHODS: Case report. RESULTS: A 37-year-old woman with MS developed PAD approximately 3 months after a multifocal MS relapse. Brain MRI showed a lesion in the posterior aspect of the midbrain as well as in the right posterior internal capsule, both of which were adjacent to the red nucleus. Attack frequency was reduced after starting levetiracetam at a dose of 500mg twice daily, and attacks stopped completely once the dose was increased to 750mg twice daily. CONCLUSIONS: Given its advantages (in terms of side effects, safety profile and ease of use compared to other anticonvulsants), we suggest that levetiracetam be considered for management of PAD, and perhaps for other paroxysmal MS symptoms as well.

A Unified Frequency Domain Model to Study the Effect of Demyelination on Axonal Conduction.

Multiple sclerosis is a disease caused by demyelination of nerve fibers. In order to determine the loss of signal with the percentage of demyelination, we need to develop models that can simulate this effect. Existing time-based models does not provide a method to determine the influences of demyelination based on simulation results. Our goal is to develop a system identification approach to generate a transfer function in the frequency domain. The idea is to create a unified modeling approach for neural action potential propagation along the length of an axon containing number of Nodes of Ranvier (N). A system identification approach has been used to identify a transfer function of the classical Hodgkin-Huxley equations for membrane voltage potential. Using this approach, we model cable properties and signal propagation along the length of the axon with N node myelination. MATLAB/Simulink platform is used to analyze an N node-myelinated neuronal axon. The ability to transfer function in the frequency domain will help reduce effort and will give a much more realistic feel when compared to the classical time-based approach. Once a transfer function is identified, the conduction as a cascade of each linear time invariant system-based transfer function can be modeled. Using this approach, future studies can model the loss of myelin in various parts of nervous system.

Prefrontal neurons transmit signals to parietal neurons that reflect executive control of cognition.

Prefrontal cortex influences behavior largely through its connections with other association cortices; however, the nature of the information conveyed by prefrontal output signals and what effect these signals have on computations performed by target structures is largely unknown. To address these questions, we simultaneously recorded the activity of neurons in prefrontal and posterior parietal cortices of monkeys performing a rule-based spatial categorization task. Parietal cortex receives direct prefrontal input, and parietal neurons, like their prefrontal counterparts, exhibit signals that reflect rule-based cognitive processing in this task. By analyzing rapid fluctuations in the cognitive information encoded by activity in the two areas, we obtained evidence that signals reflecting rule-dependent categories were selectively transmitted in a top-down direction from prefrontal to parietal neurons, suggesting that prefrontal output is important for the executive control of distributed cognitive processing.

Executive control over cognition: stronger and earlier rule-based modulation of spatial category signals in prefrontal cortex relative to parietal cortex.

Human cognition is characterized by flexibility, the ability to select not only which action but which cognitive process to engage to best achieve the current behavioral objective. The ability to tailor information processing in the brain to rules, goals, or context is typically referred to as executive control, and although there is consensus that prefrontal cortex is importantly involved, at present we have an incomplete understanding of how computational flexibility is implemented at the level of prefrontal neurons and networks. To better understand the neural mechanisms of computational flexibility, we simultaneously recorded the electrical activity of groups of single neurons within prefrontal and posterior parietal cortex of monkeys performing a task that required executive control of spatial cognitive processing. In this task, monkeys applied different spatial categorization rules to reassign the same set of visual stimuli to alternative categories on a trial-by-trial basis. We found that single neurons were activated to represent spatially defined categories in a manner that was rule dependent, providing a physiological signature of a cognitive process that was implemented under executive control. We found also that neural signals coding rule-dependent categories were distributed between the parietal and prefrontal cortex--however, not equally. Rule-dependent category signals were stronger, more powerfully modulated by the rule, and earlier to emerge in prefrontal cortex relative to parietal cortex. This suggests that prefrontal cortex may initiate the switch in neural representation at a network level that is important for computational flexibility.