Method feasibility for cross-species testing, qualification, and validation of the Filovirus Animal Nonclinical Group anti-Ebola virus glycoprotein immunoglobulin G enzyme-linked immunosorbent assay for non-human primate serum samples.

An anti-Zaire Ebola virus (EBOV) glycoprotein (GP) immunoglobulin G (IgG) enzyme linked immunosorbent assay (ELISA) was developed to quantify the serum levels of anti-EBOV IgG in human and non-human primate (NHP) serum following vaccination and/or exposure to EBOV. This method was validated for testing human serum samples as previously reported. However, for direct immunobridging comparability between humans and NHPs, additional testing was warranted. First, method feasibility experiments were performed to assess cross-species reactivity and parallelism between human and NHP serum samples. During these preliminary assessments, the goat anti-human IgG secondary antibody conjugate used in the previous human validation was found to be favorably cross-reactive with NHP samples when tested at the same concentrations previously used in the validated assay for human sample testing. Further, NHP serum samples diluted in parallel with human serum when tested side-by-side in the ELISA. A subsequent NHP matrix qualification and partial validation in the anti-GP IgG ELISA were performed based on ICH and FDA guidance, to characterize assay performance for NHP test samples and supplement the previous validation for human sample testing. Based on our assessments, the anti-EBOV GP IgG ELISA method is considered suitable for the intended use of testing with both human and NHP serum samples in the same assay for immunobridging purposes.

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.

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.

Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia.

Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with >95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.