Electrochemical and electrophysiological considerations for clinical high channel count neural interfaces

TL;DR: The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages from multiplexed neural signals, thereby achieving 3.6× and 2.8× savings in both area and power, respectively, in the EOV filter module.

Abstract: ABSTRACT Neural interfaces that unify diagnostic and therapeutic functionalities hold particular promise for advancing both fundamental neuroscience and clinical neurotechnology. Functional ultrasound imaging (fUSI) has recently emerged as a powerful modality for high-resolution, non-invasive monitoring of brain function and structure. However, conventional metal-based microelectrodes typically impede ultrasound propagation, limiting compatibility with fUSI. Here, we present flexible, ultrasound-transparent neural interfaces that retain practical metal thicknesses while achieving high acoustic transparency. We introduce a theoretical and simulation-based framework to investigate the conditions under which commonly used polymers and metals in neural interfaces can become acoustically transparent. Based on these insights, we propose design guidelines that maximize ultrasound transmission through soft neural interfaces. We experimentally validate our approach through immersion experiments and by demonstrating the acoustic transparency of a suitably engineered interface using fUSI in phantom and in vivo experiments. Finally, we discuss the potential extension of this approach to therapeutic focused ultrasound (FUS). This work establishes a foundation for the development of multimodal neural interfaces with enhanced diagnostic and therapeutic capabilities, enabling both scientific discovery and translational impact.

Abstract: The creation of personalized and GEAMs has revolutionized preclinical validation of next-generation surgical devices due to the increased physiological and predictive relevance. The present review outlines the qualitative and quantitative evaluations of implant performance in bespoke animal models and focuses on bone, cardiovascular, neural, and soft tissue implants. The CRISPR/Cas9 and transgenic techniques allow transgenic modifications in donor PSCs to generate humanized immune responses, better disease modeling, and in situ biomimicry to develop tissue-organotropism. The biomechanically Engineered Genetic Model (EGM) scaffolds promote bone development under quantification from the osteoporotic rat models, whereby decreasing RUNX2 by over 45% in the early season of post-implantation. Similarly, humanized porcine models for cardiac implants exhibit a 30% increase in the rate of endothelialization, decreasing thrombosis risk. Immune-humanized mouse models show that qualitative evaluations suggest improved integration and longevity of the implant and decreased rejection, inflammatory responses, and formation of fibrous capsules. For example, smart implants equipped with biosensors and drug-delivery systems in genetically modified diabetic rodent models achieve 60% faster wound healing, showcasing the potential of combined strategies between bioengineered implants and disease-specific animal models. DiStAff (Disease-Specific Animal Models for Affinity-Based Functional Frameworks) experimental or clinical challenges include genetic drift, ethical considerations and translational gaps. The review highlights preclinical progress, regulatory considerations, and future blueprints to ensure personalized implant technology is in line with its clinical effectiveness and patient-specific requirements.

TL;DR: A novel approach to constructing a neural interface, comprising conformable thin-film electrode arrays and a minimally invasive surgical delivery system that together facilitate communication with large portions of the cortical surface in bidirectional fashion, that promises to accelerate efforts to better decode and encode neural signals and to expand the patient population that could benefit from neural interface technology.

TL;DR: In this article, the effective surface area calculated based on the Randles-Sevcik equation for a bare screen-printed carbon electrode (SPCE), a graphene oxide (GO)-poly (3,4-ethylenedioxythiophene): polystyrenesulfonic acid (PEDOT:PSS) modified electrode (GO-PEDot:PSs/SPCE, and glucose oxidase (GOx) crosslinked with glutaraldehyde on partially reduced graphene oxide-Pedot: PSS-modified electrodes (GOex/prGO-