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.

Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications.

BACKGROUND: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain. METHODS: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (lf-MWP, 0-234 Hz), mid-frequency MWP (mf-MWP, 234 Hz-3.75 kHz) and high-frequency MWP (hf-MWP, >3.75 kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3 years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings. RESULTS: All neural features could stably track neural information for over 3 years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings. CONCLUSIONS: Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications. TRIAL REGISTRATION: This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125).

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.

MicroRNA expression in mice infected with seasonal H1N1, swine H1N1 or highly pathogenic H5N1.

Influenza virus infections in humans remain a healthcare concern, and the need for vaccines, therapeutics and prophylactics remains a high priority. Understanding the molecular events associated with influenza-virus-induced pathology may lead to the identification of clinical disease biomarkers and novel antiviral targets. MicroRNAs (miRNAs) are well-conserved endogenous non-coding RNAs known to regulate post-transcriptional gene expression as well as play a major role in many biological processes and pathways. Animal studies have demonstrated that miRNAs are involved in viral disease and controlling inflammation. In this study, we examined the differences in the miRNA expression profiles associated with the lung in mice infected with influenza viruses that varied in virulence and pathogenicity. A statistical model was employed that utilized changes in miRNA expression to identify the virus that was used to infect the mice. This study identified a unique fingerprint of viral pathogenicity associated with seasonal H1N1, swine H1N1 and highly pathogenic H5N1 in the mouse model, and may lead to the identification of novel therapeutic and prophylactic targets.

Integrating murine gene expression studies to understand obstructive lung disease due to chronic inhaled endotoxin.

RATIONALE: Endotoxin is a near ubiquitous environmental exposure that that has been associated with both asthma and chronic obstructive pulmonary disease (COPD). These obstructive lung diseases have a complex pathophysiology, making them difficult to study comprehensively in the context of endotoxin. Genome-wide gene expression studies have been used to identify a molecular snapshot of the response to environmental exposures. Identification of differentially expressed genes shared across all published murine models of chronic inhaled endotoxin will provide insight into the biology underlying endotoxin-associated lung disease. METHODS: We identified three published murine models with gene expression profiling after repeated low-dose inhaled endotoxin. All array data from these experiments were re-analyzed, annotated consistently, and tested for shared genes found to be differentially expressed. Additional functional comparison was conducted by testing for significant enrichment of differentially expressed genes in known pathways. The importance of this gene signature in smoking-related lung disease was assessed using hierarchical clustering in an independent experiment where mice were exposed to endotoxin, smoke, and endotoxin plus smoke. RESULTS: A 101-gene signature was detected in three murine models, more than expected by chance. The three model systems exhibit additional similarity beyond shared genes when compared at the pathway level, with increasing enrichment of inflammatory pathways associated with longer duration of endotoxin exposure. Genes and pathways important in both asthma and COPD were shared across all endotoxin models. Mice exposed to endotoxin, smoke, and smoke plus endotoxin were accurately classified with the endotoxin gene signature. CONCLUSIONS: Despite the differences in laboratory, duration of exposure, and strain of mouse used in three experimental models of chronic inhaled endotoxin, surprising similarities in gene expression were observed. The endotoxin component of tobacco smoke may play an important role in disease development.

Accumulation of CD11b⁺Gr-1⁺ cells in the lung, blood and bone marrow of mice infected with highly pathogenic H5N1 and H1N1 influenza viruses.

Infection with pathogenic influenza viruses is associated with intense inflammatory disease. Here, we investigated the innate immune response in mice infected with H5N1 A/Vietnam/1203/04 and with reassortant human H1N1 A/Texas/36/91 viruses containing the virulence genes hemagglutinin (HA), neuraminidase (NA) and NS1 of the 1918 pandemic virus. Inclusion of the 1918 HA and NA glycoproteins rendered a seasonal H1N1 virus capable of inducing an exacerbated host innate immune response similar to that observed for highly pathogenic A/Vietnam/1203/04 virus. Infection with 1918 HA/NA:Tx/91 and A/Vietnam/1203/04 were associated with severe lung pathology, increased cytokine and chemokine production, and significant immune cell changes, including the presence of CD11b(+)Gr-1(+) cells in the blood, lung and bone marrow. Significant differential gene expression in the lung included pathways for cell death, apoptosis, production and response to reactive oxygen radicals, as well as arginine and proline metabolism and chemokines associated with monocyte and neutrophil/granulocyte accumulation and/or activation. Arginase was produced in the lung of animals infected with A/Vietnam/1204. These results demonstrate that the innate immune cell response results in the accumulation of CD11b(+)Gr-1(+) cells and products that have previously been shown to contribute to T cell suppression.

Preliminary microRNA analysis in lung tissue to identify potential therapeutic targets against H5N1 infection.

Within the past decade, human infections with the highly pathogenic avian influenza H5N1 have resulted in approximately 60% mortality and increased the need for vaccines and therapeutics. Understanding the molecular events associated with pathology can aid this effort; therefore, this study was conducted to assess microRNA (miRNA) expression in mouse lungs infected with H5N1 A/Vietnam/1203/04. Intranasal administration of 1500 median tissue culture infectious dose of H5N1 promoted differences in the number and expression pattern of miRNA from lung tissue collected at 2, 4, 6, 24, and 96 h post-exposure that mapped to common biological functions. Informatics analysis identified miRNA-specific predicted genes known to be therapeutic drug targets in which Furin was common to all time periods. This study provides insight into the differential miRNA expression with respect to the host-pathogen relationship and identification of potential therapeutic drug targets.