Kip A. Ludwig

Using a Common Average Reference to Improve Cortical Neuron Recordings From Microelectrode Arrays

In this study, we propose and evaluate a technique known as common average referencing (CAR) to generate a more ideal reference electrode for microelectrode recordings. CAR is a computationally simple technique, and therefore amenable to both on-chip and real-time applications. CAR is commonly used in EEG, where it is necessary to identify small signal sources in very noisy recordings. To study the efficacy of common average referencing, we compared CAR to both referencing with a stainless steel bone-screw and a single microelectrode site. Data consisted of in vivo chronic recordings in anesthetized Sprague-Dawley rats drawn from prior studies, as well as previously unpublished data. By combining the data from multiple studies, we generated and analyzed one of the more comprehensive chronic neural recording datasets to date. Reference types were compared in terms of noise level, signal-to-noise ratio, and number of neurons recorded across days. Common average referencing was found to drastically outperform standard types of electrical referencing, reducing noise by >30%. As a result of the reduced noise floor, arrays referenced to a CAR yielded almost 60% more discernible neural units than traditional methods of electrical referencing. CAR should impart similar benefits to other microelectrode recording technologies-for example, chemical sensing-where similar differential recording concepts apply. In addition, we provide a mathematical justification for CAR using Gauss-Markov theorem and therefore help place the application of CAR into a theoretical context.

Poly(3,4-ethylenedioxythiophene) (PEDOT) polymer coatings facilitate smaller neural recording electrodes.

We investigated using poly(3,4-ethylenedioxythiophene) (PEDOT) to lower the impedance of small, gold recording electrodes with initial impedances outside of the effective recording range. Smaller electrode sites enable more densely packed arrays, increasing the number of input and output channels to and from the brain. Moreover, smaller electrode sizes promote smaller probe designs; decreasing the dimensions of the implanted probe has been demonstrated to decrease the inherent immune response, a known contributor to the failure of long-term implants. As expected, chronically implanted control electrodes were unable to record well-isolated unit activity, primarily as a result of a dramatically increased noise floor. Conversely, electrodes coated with PEDOT consistently recorded high-quality neural activity, and exhibited a much lower noise floor than controls. These results demonstrate that PEDOT coatings enable electrode designs 15 µm in diameter.

Tissue damage thresholds during therapeutic electrical stimulation.

OBJECTIVE Recent initiatives in bioelectronic modulation of the nervous system by the NIH (SPARC), DARPA (ElectRx, SUBNETS) and the GlaxoSmithKline Bioelectronic Medicines effort are ushering in a new era of therapeutic electrical stimulation. These novel therapies are prompting a re-evaluation of established electrical thresholds for stimulation-induced tissue damage. APPROACH In this review, we explore what is known and unknown in published literature regarding tissue damage from electrical stimulation. MAIN RESULTS For macroelectrodes, the potential for tissue damage is often assessed by comparing the intensity of stimulation, characterized by the charge density and charge per phase of a stimulus pulse, with a damage threshold identified through histological evidence from in vivo experiments as described by the Shannon equation. While the Shannon equation has proved useful in assessing the likely occurrence of tissue damage, the analysis is limited by the experimental parameters of the original studies. Tissue damage is influenced by factors not explicitly incorporated into the Shannon equation, including pulse frequency, duty cycle, current density, and electrode size. Microelectrodes in particular do not follow the charge per phase and charge density co-dependence reflected in the Shannon equation. The relevance of these factors to tissue damage is framed in the context of available reports from modeling and in vivo studies. SIGNIFICANCE It is apparent that emerging applications, especially with microelectrodes, will require clinical charge densities that exceed traditional damage thresholds. Experimental data show that stimulation at higher charge densities can be achieved without causing tissue damage, suggesting that safety parameters for microelectrodes might be distinct from those defined for macroelectrodes. However, these increased charge densities may need to be justified by bench, non-clinical or clinical testing to provide evidence of device safety.

Flavopiridol reduces the impedance of neural prostheses in vivo without affecting recording quality

We hypothesized that re-entry into the cell cycle may be associated with reactive gliosis surrounding neural prostheses, and that administration of a cell cycle inhibitor (flavopiridol) at the time of surgery would reduce this effect. We investigated the effects of flavopiridol on recording quality and impedance over a 28-day time period and conducted histology at 3 and 28 days post-implantation. Flavopiridol reduced the expression of a cell cycle protein (cyclin D1) in microglia surrounding probes at the 3-day time point. Impedance at 1 kHz was decreased by drug administration across the study period compared to vehicle controls. Correlations between recording (SNR, units) and impedance metrics revealed a small, but statistically significant, inverse relationship between these variables. However, the relationship between impedance and recording quality was not sufficiently strong for flavopiridol to result in an improvement in SNR or the number of units detected. Our data indicate that flavopiridol is an effective, easily administered treatment for reducing impedance in vivo, potentially through inhibiting microglial encapsulation of implanted devices. This strategy may be useful in stimulation applications, where reduced impedance is desirable for achieving activation thresholds and prolonging the lifetime of the implanted power supply. While improvements in recording quality were not observed, a combination of flavopiridol with a second strategy which enhances neuronal signal detection may enhance these results in future studies.

A Materials Roadmap to Functional Neural Interface Design

Advancement in neurotechnologies for electrophysiology, neurochemical sensing, neuromodulation, and optogenetics are revolutionizing scientific understanding of the brain while enabling treatments, cures, and preventative measures for a variety of neurological disorders. The grand challenge in neural interface engineering is to seamlessly integrate the interface between neurobiology and engineered technology, to record from and modulate neurons over chronic timescales. However, the biological inflammatory response to implants, neural degeneration, and long-term material stability diminish the quality of interface overtime. Recent advances in functional materials have been aimed at engineering solutions for chronic neural interfaces. Yet, the development and deployment of neural interfaces designed from novel materials have introduced new challenges that have largely avoided being addressed. Many engineering efforts that solely focus on optimizing individual probe design parameters, such as softness or flexibility, downplay critical multi-dimensional interactions between different physical properties of the device that contribute to overall performance and biocompatibility. Moreover, the use of these new materials present substantial new difficulties that must be addressed before regulatory approval for use in human patients will be achievable. In this review, the interdependence of different electrode components are highlighted to demonstrate the current materials-based challenges facing the field of neural interface engineering.

Glial responses to implanted electrodes in the brain

The use of implants that can electrically stimulate or record electrophysiological or neurochemical activity in nervous tissue is rapidly expanding. Despite remarkable results in clinical studies and increasing market approvals, the mechanisms underlying the therapeutic effects of neuroprosthetic and neuromodulation devices, as well as their side effects and reasons for their failure, remain poorly understood. A major assumption has been that the signal-generating neurons are the only important target cells of neural-interface technologies. However, recent evidence indicates that the supporting glial cells remodel the structure and function of neuronal networks and are an effector of stimulation-based therapy. Here, we reframe the traditional view of glia as a passive barrier, and discuss their role as an active determinant of the outcomes of device implantation. We also discuss the implications that this has on the development of bioelectronic medical devices.This Review discusses the role of glia as an effector of the performance and integration of devices implanted in the brain, and the implications of this for device development.

Brain–computer interface devices for patients with paralysis and amputation: a meeting report

OBJECTIVE The Food and Drug Administrations (FDA) Center for Devices and Radiological Health (CDRH) believes it is important to help stakeholders (e.g., manufacturers, health-care professionals, patients, patient advocates, academia, and other government agencies) navigate the regulatory landscape for medical devices. For innovative devices involving brain-computer interfaces, this is particularly important. APPROACH Towards this goal, on 21 November, 2014, CDRH held an open public workshop on its White Oak, MD campus with the aim of fostering an open discussion on the scientific and clinical considerations associated with the development of brain-computer interface (BCI) devices, defined for the purposes of this workshop as neuroprostheses that interface with the central or peripheral nervous system to restore lost motor or sensory capabilities. MAIN RESULTS This paper summarizes the presentations and discussions from that workshop. SIGNIFICANCE CDRH plans to use this information to develop regulatory considerations that will promote innovation while maintaining appropriate patient protections. FDA plans to build on advances in regulatory science and input provided in this workshop to develop guidance that provides recommendations for premarket submissions for BCI devices. These proceedings will be a resource for the BCI community during the development of medical devices for consumers.

Use of a Bayesian maximum-likelihood classifier to generate training data for brain-machine interfaces.

Brain-machine interface decoding algorithms need to be predicated on assumptions that are easily met outside of an experimental setting to enable a practical clinical device. Given present technological limitations, there is a need for decoding algorithms which (a) are not dependent upon a large number of neurons for control, (b) are adaptable to alternative sources of neuronal input such as local field potentials (LFPs), and (c) require only marginal training data for daily calibrations. Moreover, practical algorithms must recognize when the user is not intending to generate a control output and eliminate poor training data. In this paper, we introduce and evaluate a Bayesian maximum-likelihood estimation strategy to address the issues of isolating quality training data and self-paced control. Six animal subjects demonstrate that a multiple state classification task, loosely based on the standard center-out task, can be accomplished with fewer than five engaged neurons while requiring less than ten trials for algorithm training. In addition, untrained animals quickly obtained accurate device control, utilizing LFPs as well as neurons in cingulate cortex, two non-traditional neural inputs.

Computational Modeling of Neurotransmitter Release Evoked by Electrical Stimulation: Nonlinear Approaches to Predicting Stimulation-Evoked Dopamine Release

Neurochemical changes evoked by electrical stimulation of the nervous system have been linked to both therapeutic and undesired effects of neuromodulation therapies used to treat obsessive-compulsive disorder, depression, epilepsy, Parkinsons disease, stroke, hypertension, tinnitus, and many other indications. In fact, interest in better understanding the role of neurochemical signaling in neuromodulation therapies has been a focus of recent government- and industry-sponsored programs whose ultimate goal is to usher in an era of personalized medicine by creating neuromodulation therapies that respond to real-time changes in patient status. A key element to achieving these precision therapeutic interventions is the development of mathematical modeling approaches capable of describing the nonlinear transfer function between neuromodulation parameters and evoked neurochemical changes. Here, we propose two computational modeling frameworks, based on artificial neural networks (ANNs) and Volterra kernels, that can characterize the input/output transfer functions of stimulation-evoked neurochemical release. We evaluate the ability of these modeling frameworks to characterize subject-specific neurochemical kinetics by accurately describing stimulation-evoked dopamine release across rodent (R2 = 0.83 Volterra kernel, R2 = 0.86 ANN), swine (R2 = 0.90 Volterra kernel, R2 = 0.93 ANN), and non-human primate (R2 = 0.98 Volterra kernel, R2 = 0.96 ANN) models of brain stimulation. Ultimately, these models will not only improve understanding of neurochemical signaling in healthy and diseased brains but also facilitate the development of neuromodulation strategies capable of controlling neurochemical release via closed-loop strategies.

Calcium activation of frequency dependent temporally phasic, localized, and dense population of cortical neurons by continuous electrical stimulation

Abstract Electrical stimulation of the brain has become a mainstay of fundamental neuroscience research and an increasingly prevalent clinical therapy. Despite decades of use in basic neuroscience research and the growing prevalence of neuromodulation therapies, gaps in knowledge regarding activation or inactivation of neural elements over time have limited its ability to adequately interpret evoked downstream responses or fine-tune stimulation parameters to focus on desired responses. In this work, in vivo two-photon microscopy was used to image neuronal calcium activity in layer 2/3 neurons of somatosensory cortex (S1) in male C57BL/6J-Tg(Thy1-GCaMP6s)GP4.3Dkim/J mice during 30 s of continuous electrical stimulation at varying frequencies. We show frequency-dependent differences in spatial and temporal somatic responses during continuous stimulation. Our results elucidate conflicting results from prior studies reporting either dense spherical activation of somas biased towards those near the electrode, or sparse activation of somas at a distance via axons near the electrode. These findings indicate that the neural element specific temporal response local to the stimulating electrode changes as a function of applied charge density and frequency. These temporal responses need to be considered to properly interpret downstream circuit responses or determining mechanisms of action in basic science experiments or clinical therapeutic applications. Significance Statement Microstimulation of small populations of neurons has the potential to ameliorate symptoms associated with several neurological disorders. However, the specific mechanisms by which microstimulation elicits therapeutic responses are unclear. This work examines the effects of continuous microstimulation on the local population of neurons surrounding the implanted electrode. Stimulation was found to elicit spatiotemporal neuronal responses in a frequency dependent manner. These findings suggest that stimulation frequency may be an important consideration for applications in research or therapy. Further research aimed at understanding these neuronal activation properties may provide insight into the mechanistic mode of action of continuous microstimulation.Electrical stimulation of the brain has become a mainstay of fundamental neuroscience research and an increasingly prevalent clinical therapy. Despite decades of use in basic neuroscience research over acute time scales, and the growing prevalence of neuromodulation therapies, gaps in knowledge regarding activation or inactivation of neural elements over time have limited its ability to adequately interpret evoked downstream responses or fine-tune stimulation parameters to focus on desired responses. In this work, in vivo two-photon microscopy was used to image Thy1-GCaMP activity in layer 2/3 neurons of S1 cortex during 30 s of continuous electrical stimulation at varying frequencies. We show that during continuous stimulation, stimulation frequency influences a distinct spatial and temporal pattern of somatic activation. Our results elucidate conflicting results from prior studies reporting either dense spherical activation of somas biased towards somas near the electrode, or sparse activation of somas at a distance via axons near the electrode. These findings indicate that the neural element specific temporal response local to the stimulating electrode changes as a function of applied charge density and frequency. This temporal patterning needs to be considered to properly interpret downstream circuit responses or determining mechanisms of action in basic science experiments or clinical therapeutic applications. Significance Statement Microstimulation of small populations of neurons has the potential to ameliorate symptoms associated with several neurological disorders. However, the specific mechanisms by which microstimulation elicits therapeutic responses are unclear. This work examines the effects of continuous microstimulation on the local population of neurons surrounding the implanted electrode. Stimulation was found to elicit spatiotemporal neuronal responses in a frequency dependent manner. These findings suggest that stimulation frequency may be an important consideration for applications in research or therapy. Further research aimed at understanding these neuronal activation properties may provide insight into the mechanistic action of continuous microstimulation.