Ionic communication for implantable bioelectronics

Zhao, Zenghua; Spyropoulos, George D.; Cea, Claudia; Gelinas, Jennifer N.; Khodagholy, Dion

doi:10.1126/sciadv.abm7851

Cited by 39 publications

(22 citation statements)

References 60 publications

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“…Therefore, this OECT platform can be used as a biocompatible implant without any side effects caused by the by-products. [66,67] Figure 5a-c shows representative activities captured by one of the channels in the transient biodegradable OECT array. The origin and interpretation of the cortical waves under different brain states are different, which can be distinguished by signal amplitude, waveform, and frequency.…”

Section: In Vivo Characterizationmentioning

confidence: 99%

Ultrathin, Soft, Bioresorbable Organic Electrochemical Transistors for Transient Spatiotemporal Mapping of Brain Activity

Yao

Huang

et al. 2023

Advanced Science

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A critical challenge lies in the development of the next‐generation neural interface, in mechanically tissue‐compatible fashion, that offer accurate, transient recording electrophysiological (EP) information and autonomous degradation after stable operation. Here, an ultrathin, lightweight, soft and multichannel neural interface is presented based on organic‐electrochemical‐transistor‐(OECT)‐based network, with capabilities of continuous high‐fidelity mapping of neural signals and biosafety active degrading after performing functions. Such platform yields a high spatiotemporal resolution of 1.42 ms and 20 µm, with signal‐to‐noise ratio up to ≈37 dB. The implantable OECT arrays can well establish stable functional neural interfaces, designed as fully biodegradable electronic platforms in vivo. Demonstrated applications of such OECT implants include real‐time monitoring of electrical activities from the cortical surface of rats under various conditions (e.g., narcosis, epileptic seizure, and electric stimuli) and electrocorticography mapping from 100 channels. This technology offers general applicability in neural interfaces, with great potential utility in treatment/diagnosis of neurological disorders.

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Section: In Vivo Characterizationmentioning

confidence: 99%

Ultrathin, Soft, Bioresorbable Organic Electrochemical Transistors for Transient Spatiotemporal Mapping of Brain Activity

Yao

Huang

et al. 2023

Advanced Science

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“…The bridge, being at the interface between such diverse worlds, should exhibit a mixed conduction mechanism. Therefore, owing both ionic and electronic conduction is essential to ensure an adequate and harmonized communication between all the parts involved [125] (figure 3(a)).…”

Section: Bidirectional Communicationmentioning

confidence: 99%

From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks

Bruno

Mariano

Rana

et al. 2023

Neuromorph. Comput. Eng.

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The highly efficient computation of the brain relies on its plastic and reconfigurable nature, enabling complex computations and maintenance of vital functions with a remarkably low power consumption of only ~20 W. First efforts to leverage brain-inspired computational principles have led to the introduction of artificial neural networks (ANNs) that revolutionized information processing and daily life. The relentless pursuit of the definitive computing platform is now pushing researchers towards the investigation of novel solutions to emulate specific brain features (such as synaptic plasticity) to allow local and energy efficient computations. The development of such devices may also be pivotal in addressing major challenges of a continuously aging world, including the treatment of neurodegenerative diseases. To date, the neuroelectronics field has been instrumental in deepening our understanding of how neurons exchange information, owing to the rapid development of silicon-based platforms for neural recordings and stimulation. However, this approach still does not allow for in loco processing of the observed biological signals due to two aspects. Firstly, silicon chips applying conventional processing are simply too power hungry to be embedded into the brain. Secondly, despite the success of silicon-based devices in electronic applications, they are ill-suited for directly interfacing with biological tissue. A cornucopia of solutions has therefore been proposed in the last years to obtain neuromorphic materials to create effective biointerfaces and enable reliable bidirectional communication with neurons. Organic conductive materials are not only highly biocompatible and able to electrochemically transduce biological signals, but also promise to include neuromorphic features, such as neuro-transmitter mediated plasticity and learning capabilities. Furthermore, organic electronics, relying on mixed electronic/ionic conduction mechanism, can be efficiently coupled with biological neural networks, while still communicating with silicon-based electronics. We envision neurohybrid systems that integrate silicon-based and organic electronics-based neuromorphic technologies to create active artificial interfaces with biological tissues.

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“…For this reason, the effective detection of neural signals is crucial for obtaining bioelectronic information about cognitive processes and pathways as well as diagnosing and treating related diseases (e.g., schizophrenia, autism, epilepsy, Parkinson, and Alzheimer's; Mcglynn et al, 2021; Oldroyd & Malliaras, 2022). Therefore, several electrically conductive devices have been lately researched and developed for the fabrication of bioelectronic devices, including brain–machine interfaces (BMIs; S. M. Won et al, 2018; Zhao et al, 2022). These systems are designed to serve as implantable interface electrodes to record and digitize the brain signals in real‐time by directly transferring them to a computer for final visualization and analysis (Song et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Conducting polymer‐based nanostructured materials for brain–machine interfaces

Ziai

Zargarian

Rinoldi

et al. 2023

WIREs Nanomed Nanobiotechnol

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As scientists discovered that raw neurological signals could translate into bioelectric information, brain–machine interfaces (BMI) for experimental and clinical studies have experienced massive growth. Developing suitable materials for bioelectronic devices to be used for real‐time recording and data digitalizing has three important necessitates which should be covered. Biocompatibility, electrical conductivity, and having mechanical properties similar to soft brain tissue to decrease mechanical mismatch should be adopted for all materials. In this review, inorganic nanoparticles and intrinsically conducting polymers are discussed to impart electrical conductivity to systems, where soft materials such as hydrogels can offer reliable mechanical properties and a biocompatible substrate. Interpenetrating hydrogel networks offer more mechanical stability and provide a path for incorporating polymers with desired properties into one strong network. Promising fabrication methods, like electrospinning and additive manufacturing, allow scientists to customize designs for each application and reach the maximum potential for the system. In the near future, it is desired to fabricate biohybrid conducting polymer‐based interfaces loaded with cells, giving the opportunity for simultaneous stimulation and regeneration. Developing multi‐modal BMIs, Using artificial intelligence and machine learning to design advanced materials are among the future goals for this field.This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease

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Ionic communication for implantable bioelectronics

Cited by 39 publications

References 60 publications

Ultrathin, Soft, Bioresorbable Organic Electrochemical Transistors for Transient Spatiotemporal Mapping of Brain Activity

Ultrathin, Soft, Bioresorbable Organic Electrochemical Transistors for Transient Spatiotemporal Mapping of Brain Activity

From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks

Conducting polymer‐based nanostructured materials for brain–machine interfaces

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