Graphene-based neurotechnologies for advanced neural interfaces. (June 2018)

• Record Type: Journal Article
• Authors: Lu, Yichen; Liu, Xin; Kuzum, Duygu
• Is Part Of: Current opinion in biomedical engineering. Volume 6(2018)
• Page Start: 138
• Page End: 147
• Publication Date: 2018-06
• Subjects: Graphene -- Electrophysiology -- Chemical sensing -- Biocompatibility -- Multimodal neural interface; Biomedical engineering -- Periodicals; 610.28
• DOI: 10.1016/j.cobme.2018.06.001
• Languages: English
• ISSNs: 2468-4511

Abstract

Abstract: Understanding how neuron populations transform activities of individual neurons into complex behaviors is one of the biggest challenges of neuroscience research. However, current neural monitoring and controlling technologies provide insufficient spatiotemporal resolution to unravel neural circuit functions. To this end, multifunctional neurotechnologies combining electrical, optical and chemical sensing and stimulation modalities have been proposed to overcome resolution limits. Research in multifunctional probes has fueled the demand for new materials to build minimally invasive chronic interfaces to the brain. Graphene has recently emerged as a neural interface material offering several outstanding properties, such as optical transparency, flexibility, high conductivity, functionalization and biocompatibility. The unique combination of these properties in a single material system makes graphene an attractive choice for multi-modal probing of neural activity. In this review, we discuss recent advances in graphene-based neurotechnologies, highlight different approaches and consider emerging directions inspired by unique characteristics of graphene. Graphical abstract: Image 1 Highlights: Transparent graphene electrode arrays enable multimodal neural interfaces. Graphene with porous structure has high charge injection capacity and low impedance. Graphene field-effect transistors have intrinsic amplification effect for high signal-to-noise ratio. Graphene chemical sensors have low limit of detection and quick response. Artifact-free closed-loop systems based on transparent graphene facilitate causal investigations of neural circuit functions.