A brain-spinal interface (BSI) system-on-chip (SoC) for closed-loop cortically-controlled intraspinal microstimulation

This paper reports on a fully miniaturized brain-spinal interface system for closed-loop cortically-controlled intraspinal microstimulation (ISMS). Fabricated in AMS 0.35 µm two-poly four-metal complementary metal–oxide–semiconductor technology, this system-on-chip measures ~ 3.46 mm × 3.46 mm and incorporates two identical 4-channel modules, each comprising a spike-recording front-end, embedded digital signal processing (DSP) unit, and programmable stimulating back-end. The DSP unit is capable of generating multichannel trigger signals for a wide array of ISMS triggering patterns based on real-time discrimination of a programmable number of intracortical neural spikes within a pre-specified time-bin duration via thresholding and user-adjustable time–amplitude windowing. The system is validated experimentally using an anesthetized rat model of a spinal cord contusion injury at the T8 level. Multichannel neural spikes are recorded from the cerebral cortex and converted in real time into electrical stimuli delivered to the lumbar spinal cord below the level of the injury, resulting in distinct patterns of hindlimb muscle activation

Application of Micro-Electro-Mechanical Systems as Neural Interface

Micro-Electro-Mechanical Systems (MEMS) technology comprises of developing miniaturized mechanical and electro-mechanical elements such that the physical dimensions of these devices vary from micron to few millimeters in size.In various human disease disorders, the neural or body regulatory tissues are incapable of conveying commands directly to the target organ and unable to receive appropriate information from receptor mechanism to decide the future course of action. The MEMS based devices are playing important assistive role by becoming crucial interface in treating such disorders. These devices are increasingly being deployed inside the body at sub tissue levels to fulfill information receipt or command transmission gap, thereby enabling the governing tissue opportunity and environment to work effectively, leading to improvement in the neural signal recording and quality of life of the concerned individual. The aim of this paper is to review the present and future of MEMS based devices widely being employed as neural interface in penetrating probes, nerve regeneration, neuron culture and drug delivery devices depending on type of treatment provided to specific neural disorders. Further, they have been recently employed in developing advanced neuro-computer, nerve stimulators, wheel chair control based on head and hand movements and in medical robotics. Due to their stability, biocompatibility, usage and wider acceptability these MEMS based neural interface devices are providing future hope for their deployment in conquering various neurological disorders

Inducing Neural Plasticity After Spinal Cord Injury To Recover Impaired Voluntary Movement

Spinal cord injury (SCI) is often an incapacitating neural injury most commonly caused by a traumatic blow to the spine. A SCI causes damage to the axons that carry sensory and motor signals between the brain and spinal cord, and in turn, the rest of the body. Depending on the severity and location of a SCI, many corticospinal axons and other descending motor pathways can remain intact. Moderate spontaneous functional recovery occurs in patients and animal models following incomplete SCI. This recovery is linked to changes occurring via the remaining pathways and throughout the entire nervous system, which is generally referred to as neuronal plasticity. It has been shown that plasticity can be induced via electrical stimulation of the brain and spinal cord targeting specific descending pathways, which can further improve impaired motor function. Most importantly, it has been shown that activity dependent stimulation (ADS), which is based on mechanisms of spike timing-dependent plasticity, can strengthen remaining pathways and promote functional recovery in various preclinical injury models of the central nervous system. The purpose of this dissertation was to determine if precisely-timed stimulation of the spinal cord triggered by the firing of neurons in the hindlimb motor cortex would result in potentiation of corticospinal connections as well as enhance hindlimb motor recovery after spinal cord contusion. In order to achieve this, we needed to determine the optimal neurophysiological conditions which would allow activity dependent stimulation (ADS) to facilitate enhanced communication between the cerebral cortex and spinal cord motor neurons. Thus, this dissertation project investigated three specific aims. The first study determined the effects of a contusive spinal cord injury on spinal motor neuron activity, corticospinal coupling, and conduction time in rats. It was discovered that spinal cord responses could still be evoked after spinal cord contusion, most likely via the cortico-reticulo-spinal pathway. The second study determined the optimal spike-stimulus delay for increasing synaptic efficacy in descending motor pathways using an ADS paradigm in an acute, anesthetized rat model of SCI. It was discovered that bouts of ADS conditioning can increase synaptic efficacy in intact descending motor pathways, as measured by cortically evoked activity in the spinal cord, after SCI. The third study determined whether spike-triggered intraspinal microstimulation (ISMS), using optimized spike-stimulus delays, results in improved motor performance in an ambulatory rat model of SCI. It was determined that ADS therapy can enhance the behavioral recovery of locomotor function after spinal cord injury. The results from this study indicate that activity-dependent stimulation is an effective treatment for behavioral recovery following a moderate spinal cord contusion in the rodent. The implications of these results have the potential to lead to a novel treatment for a variety of neurological disease and disorders

Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization

Neurological diseases can be studied by performing bio-hybrid experiments using a real-time biomimetic Spiking Neural Network (SNN) platform. The Hodgkin-Huxley model offers a set of equations including biophysical parameters which can serve as a base to represent different classes of neurons and affected cells. Also, connecting the artificial neurons to the biological cells would allow us to understand the effect of the SNN stimulation using different parameters on nerve cells. Thus, designing a real-time SNN could useful for the study of simulations of some part of the brain. Here, we present a different approach to optimize the Hodgkin-Huxley equations adapted for Field Programmable Gate Array (FPGA) implementation. The equations of the conductance have been unified to allow the use of same functions with different parameters for all ionic channels. The low resources and high-speed implementation also include features, such as synaptic noise using the Ornstein–Uhlenbeck process and different synapse receptors including AMPA, GABAa, GABAb, and NMDA receptors. The platform allows real-time modification of the neuron parameters and can output different cortical neuron families like Fast Spiking (FS), Regular Spiking (RS), Intrinsically Bursting (IB), and Low Threshold Spiking (LTS) neurons using a Digital to Analog Converter (DAC). Gaussian distribution of the synaptic noise highlights similarities with the biological noise. Also, cross-correlation between the implementation and the model shows strong correlations, and bifurcation analysis reproduces similar behavior compared to the original Hodgkin-Huxley model. The implementation of one core of calculation uses 3% of resources of the FPGA and computes in real-time 500 neurons with 25,000 synapses and synaptic noise which can be scaled up to 15,000 using all resources. This is the first step toward neuromorphic system which can be used for the simulation of bio-hybridization and for the study of neurological disorders or the advanced research on neuroprosthesis to regain lost function

Modelling Artificial Stimulation and Response in Peripheral Nerves Including Ephaptic Interactions

This research aims to (1) extend our knowledge on the response of peripheral nerves to artificial stimulation for sensory feedback provision from neural interfaces, and (2) create a computational tool to facilitate this study. We were interested in studying how ephaptic coupling between myelinated fibers influences activity in nerve trunks under artificial stimulation and during action potential propagation. Ephaptic interaction simulations in nerve trunks were performed to quantify this influence. For this, we created peripheral nerve models containing electrodes for electrical stimulation and recording within a tool that can be further used in electrode design optimisation and neural activity research. The created model can use a self-contained or a hybrid field-neuron method. The self-contained method uses a resistor network that electrically couples all axons, tissues, electrodes, and surrounding medium, and is solved by the NEURON simulation environment. The resistor network uses weighted Voronoi tessellations in the Laguerre geometry to define the electrical connections between all nerve elements given any cross-sectional anatomy. The hybrid field-neuron approach also uses the resistor network to compute the fields, but uses them stimulate fiber in a separate simulation. The self-contained model was designed so that it could simulate artificial stimulation, neural activity with ephaptic coupling and electrode recordings simultaneously. Researchers often assume ephaptic coupling is weak among myelinated axons, and therefore, tend to ignore it. Simulations carried out in this work, however, show that ephaptic coupling increases axon recruitment during artificial stimulation. This effect should be taken into account in further research. On the other hand, ephaptic coupling during propagation in realistic bundles with large numbers of heterogeneous myelinated fibers is weaker, unstable, and more complex than what is known from previous studies on bundles of few homogeneous fibers. This research provides detailed results and insights on these aspects of peripheral neural activity