Reduction of edge effect on disk electrodes by optimized current waveform

Controlling Gold Morphology Using Electrodeposition for the Preparation of Electrochemical Aptamer-Based Sensors

Nanostructuring of gold surfaces to enhance electroactive surface area has proven to significantly enhance the performance of electrochemical aptamer-based (E-AB) sensors, particularly for electrodes on the microscale. Unlike for sensors fabricated on polished gold surfaces, predicting the behavior of E-AB sensors on surfaces with varied gold morphologies becomes more intricate due to the effects of surface roughness and the shapes and sizes of surface features on supporting a self-assembled monolayer. In this study, we explored the impact of gold morphology characteristics on sensor performance, evaluating parameters such as signal change in response to the addition of the target analyte, aptamer probe packing density, and continuous sensing ability. Our findings reveal that surface area enhancement can either enhance or diminish sensor performance for gold nanostructured E-AB sensors, contingent upon the surface morphology. In particular, our results indicate that the aptamer packing density and target analyte signal change results are heavily dependent on gold nanostructure size and features. Sensing surfaces with larger nanoparticle diameters, which were prepared using electrodeposition at a constant potential, had a reduced aptamer packing density and exhibited diminished sensor performance. However, the equivalent packing density of polished electrodes did not yield the equivalent signal change. Other surfaces that were prepared using pulsed waveform electrodeposition achieved optimal signal change with a deposition time, tdep, of 120 s, and increased deposition time with enhanced electroactive surface area resulted in minimized signal changes and more rapid sensor degradation. By investigating sensing surfaces with varied morphologies, we have demonstrated that enhancing the electroactive surface does not always enhance the signal change of the sensor, and aptamer packing density alone does not dictate observed signal change trends. We anticipate that understanding how electrodeposition techniques enhance or diminish sensor performance will pave the way for further exploration of nanostructure-aptamer relationships, contributing to the future development of optimized, miniaturized electrochemical aptamer-based sensors for continuous, in vivo sensing.

Adaptive octree meshes for simulation of extracellular electrophysiology

Objective.The interaction between neural tissues and artificial electrodes is crucial for understanding and advancing neuroscientific research and therapeutic applications. However, accurately modeling this space around the neurons rapidly increases the computational complexity of neural simulations.Approach.This study demonstrates a dynamically adaptive simulation method that greatly accelerates computation by adjusting spatial resolution of the simulation as needed. Use of an octree structure for the mesh, in combination with the admittance method for discretizing conductivity, provides both accurate approximation and ease of modification on-the-fly.Main results.In tests of both local field potential estimation and multi-electrode stimulation, dynamically adapted meshes achieve accuracy comparable to high-resolution static meshes in an order of magnitude less time.Significance.The proposed simulation pipeline improves model scalability, allowing greater detail with fewer computational resources. The implementation is available as an open-source Python module, providing flexibility and ease of reuse for the broader research community.

Impedance Properties of Multi-Optrode Biopotential Sensing Arrays

Recording and monitoring electrically-excitable cells is critical to understanding the complex cellular networking within organs as well as the processes underlying many electro-physiological pathologies. Biopotential recording using an optical-electrode (optrode) is a novel approach which has potential to significantly improve interface-instrumentation impedance mismatching as recording contact-sizes become smaller and smaller. Optrodes incorporate a conductive interface that can sense extracellular potential and an underlying layer of liquid crystals that passively transduces electrical signals into measurable optical signals. This study investigates the impedance properties of this optical technology by varying the diameter of recording sites and observing the corresponding changes in the impedance values. The results show that the liquid crystals in this optrode platform exhibit input impedance values (1 M 100 G) that are three orders of magnitude higher than the corresponding interface impedance, which is appropriate for voltage sensing. The automatic scaling of the input impedance enabled within the optrode system maintains a relatively constant ratio between input and total system impedance of about one for sensing areas with diameters ranging from 40 m to 1 mm, at which the calculated signal loss is predicted to be <1%. This feature preserves the interface-transducer impedance ratio, regardless of the size of the recording site, allowing development of passive optrode arrays capable of very high spatial-resolution recordings.

Role of skin tissue layers and ultra-structure in transcutaneous electrical stimulation including tDCS

BACKGROUND

During transcranial electrical stimulation (tES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), current density concentration around the electrode edges that is predicted by simplistic skin models does not match experimental observations of erythema, heating, or other adverse events. We hypothesized that enhancing models to include skin anatomical details, would alter predicted current patterns to align with experimental observations.

METHOD

We develop a high-resolution multi-layer skin model (epidermis, dermis, and fat), with or without additional ultra-structures (hair follicles, sweat glands, and blood vessels). Current flow patterns across each layer and within ultra-structures were predicted using finite element methods considering a broad range of modeled tissue parameters including 78 combinations of skin layer conductivities (S m^-1): epidermis (standard: 1.05 × 10^-5; range: 1.05 × 10^-6 to 0.465); dermis (standard: 0.23; range: 0.0023 to 23), fat (standard: 2 × 10^-4; range: 0.02 to 2 × 10^-5). The impact of each ultra-structures in isolation and combination was evaluated with varied basic geometries. An integrated final model is then developed.

RESULTS

Consistent with prior models, current flow through homogenous skin was annular (concentrated at the electrode edges). In multi-layer skin, reducing epidermis conductivity and/or increasing dermis conductivity decreased current near electrode edges, however no realistic tissue layer parameters produced non-annular current flow at both epidermis and dermis. Addition of just hair follicles, sweat glands, or blood vessels resulted in current peaks around each ultrastructure, irrespective of proximity to electrode edges. Addition of only sweat glands was the most effective approach in reducing overall current concentration near electrode edges. Representation of blood vessels resulted in a uniform current flow across the vascular network. Finally, we ran the first realistic model of current flow across the skin.

CONCLUSION

We confirm prior models exhibiting current concentration near hair follicles or sweat glands, but also exhibit that an overall annular pattern of current flow remains for realistic tissue parameters. We model skin blood vessels for the first time and show that this robustly distributes current across the vascular network, consistent with experimental erythema patterns. Only a state-of-the-art precise model of skin current flow predicts lack of current concentration near electrode edges across all skin layers.

Harmonic-balance circuit analysis for electro-neural interfaces

OBJECTIVE

Avoidance of the adverse electrochemical reactions at the electrode-electrolyte interface defines the voltage safety window and limits the charge injection capacity (CIC) of an electrode material. For an electrode that is not ideally capacitive, the CIC depends on the waveform of the stimulus. We study the modeling of the charge injection dynamics to optimize the waveforms for efficient neural stimulation within the electrochemical safety limits.

APPROACH

The charge injection dynamics at the electrode-electrolyte interface is typically characterized by the electrochemical impedance spectrum, and is often approximated by discrete-element circuit models. We compare the modeling of the complete circuit, including a non-linear driver such as a photodiode, based on the harmonic-balance (HB) analysis with the analysis based on various - (discrete-element) approximations. To validate the modeling results, we performed experiments with iridium-oxide electrodes driven by a current source with diodes in parallel, which mimics a photovoltaic circuit.

MAIN RESULTS

Application of HB analysis based on a full impedance spectrum eliminates the complication of finding the discrete-element circuit model in traditional approaches. HB-based results agree with the experimental data better than the discrete-element circuit. HB technique can be applied not only to demonstrate the circuit response to periodic stimulation, but also to describe the initial transient behavior when a burst waveform is applied.

SIGNIFICANCE

HB-based circuit analysis accurately describes the dynamics of electrode-electrolyte interfaces and driving circuits for all pulsing schemes. This allows optimizing the stimulus waveform to maximize the CIC, based on the impedance spectrum alone.

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Neuroprosthetics and neuromodulation represent a promising field for several related applications in the central and peripheral nervous system, such as the treatment of neurological disorders, the control of external robotic devices, and the restoration of lost tactile functions. These actions are allowed by the neural interface, a miniaturized implantable device that most commonly exploits electrical energy to fulfill these operations. A neural interface must be biocompatible, stable over time, low invasive, and highly selective; the challenge is to develop a safe, compact, and reliable tool for clinical applications. In case of anatomical impairments, neuroprosthetics is bound to the need of exploring the surrounding environment by fast-responsive and highly sensitive artificial tactile sensors that mimic the natural sense of touch. Tactile sensors and neural interfaces are closely interconnected since the readouts from the first are required to convey information to the neural implantable apparatus. The role of these devices is pivotal hence technical improvements are essential to ensure a secure system to be eventually adopted in daily life. This review highlights the fundamental criteria for the design and microfabrication of neural interfaces and artificial tactile sensors, their use in clinical applications, and future enhancements for the release of a second generation of devices.

Intradural Spinal Cord Stimulation: Performance Modeling of a New Modality

Introduction: Intradural spinal cord stimulation (SCS) may offer significant therapeutic benefits for those with intractable axial and extremity pain, visceral pain, spasticity, autonomic dysfunction and related disorders. A novel intradural electrical stimulation device, limited by the boundaries of the thecal sac, CSF and spinal cord was developed to test this hypothesis. In order to optimize device function, we have explored finite element modeling (FEM).

Methods: COMSOL^®Multiphysics Electrical Currents was used to solve for fields and currents over a geometric model of a spinal cord segment. Cathodic and anodic currents are applied to the center and tips of the T-cross component of the electrode array to shape the stimulation field and constrain charge-balanced cathodic pulses to the target area.

Results: Currents from the electrode sites can move the effective stimulation zone horizontally across the cord by a linear step method, which can be diversified considerably to gain greater depth of penetration relative to standard epidural SCS. It is also possible to prevent spread of the target area with no off-target action potential.

Conclusion: Finite element modeling of a T-shaped intradural spinal cord stimulator predicts significant gains in field depth and current shaping that are beyond the reach of epidural stimulators. Future studies with in vivo models will investigate how this approach should first be tested in humans.

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.

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.

Analysis of the Peak Resistance Frequency Method

OBJECTIVE

This study analyzes the peak resistance frequency (PRF) method described by Mercanzini et al., a method that can easily extract the tissue resistance from impedance spectroscopy for many neural engineering applications but has no analytical description thus far.

METHODS

Mathematical analyses and computer simulations were used to explore underlying principles, accuracy, and limitations of the PRF method.

RESULTS

The mathematical analyses demonstrated that the PRF method has an inherent but correctable deviation dependent on the idealness of the electrode-tissue interface, which is validated by simulations. Further simulations show that both frequency sampling and noise affect the accuracy of the PRF method, and in general, it performs less accurately than least squares methods. However, the PRF method achieves simplicity and reduced measurement and computation time at the expense of accuracy.

CONCLUSION

From the qualitative results, the PRF method can work with reasonable precision and simplicity, although its limitation and the idealness of the electrode-tissue interface involved should be taken into consideration.

SIGNIFICANCE

This paper provides a mathematical foundation for the PRF method and its practical implementation.