RESOURCES

Abstract
Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.

Abstract
Neuromuscular interfaces are required to translate bioelectronic technologies for application in clinical medicine. Here, by leveraging the robotically controlled ink-jet deposition of low-viscosity conductive inks, extrusion of insulating silicone pastes and in situ activation of electrode surfaces via cold-air plasma, we show that soft biocompatible materials can be rapidly printed for the on-demand prototyping of customized electrode arrays well adjusted to specific anatomical environments, functions and experimental models. We also show, with the monitoring and activation of neuronal pathways in the brain, spinal cord and neuromuscular system of cats, rats and zebrafish, that the printed bioelectronic interfaces allow for long-term integration and functional stability. This technology might enable personalized bioelectronics for neuroprosthetic applications.

Abstract
Bioelectronic interfaces employing arrays of sensors and bioactuators are promising tools for the study, repair and engineering of cardiac tissues. They are typically constructed from rigid and brittle materials processed in a cleanroom environment. An outstanding technological challenge is the integration of soft materials enabling a closer match to the mechanical properties of biological cells and tissues. Here we present an algorithm for direct writing of elastic membranes with embedded electrodes, optical waveguides and microfluidics using a commercial 3D printing system and a palette of silicone elastomers. As proof of principle, we demonstrate interfacing of cardiomyocytes derived from human induced pluripotent stem cells (hiPSCs), which are engineered to express Channelrhodopsin-2. We demonstrate electrical recording of cardiomyocyte field potentials and their concomitant modulation by optical and pharmacological stimulation delivered via the membrane. Our work contributes a simple prototyping strategy with potential applications in organ-on-chip or implantable systems that are multi-modal and mechanically soft.