Microelectrode Arrays for Neuronal Recordings: Developing Novel Technology and Applications
Date
2015
Authors
Charkhkar, Hamid
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Abstract
Over the last few decades, there has been a large growth in developing the technology for use in brain sciences. The advancement in such technology has found applications in neuroscience research as well as emergence of novel diagnostic and therapeutic devices for neurological diseases. The majority of these techniques relies on monitoring or modulating the bioelectric activity of the excitable brain cells, neurons. To record the bioelectrical activity of network of neurons, microelectrode arrays (MEAs) patterned and fabricated by photolithography have been widely utilized. Despite the extensive use of MEAs for neural applications, major constraints have limited the utility of the MEAs. The extracellular potentials detected by MEAs are typically in the range of 40 to 500 microvolts and the signal magnitude decreases proportionally to the inverse of the distance between the neuronal source and the electrode. To achieve high spatial resolution and selective recordings in the brain, designing microscale MEAs seems necessary. However, with the reduction in the electrode size, the impedance increases for the conventional metal electrodes resulting in high thermal noise and potentially lower signal-to-noise ratios (SNRs). For in vitro applications, compared to disposable plastic chambers and cultured dishes, the MEAs are typically reused in experiments since they are expensive. We present new techniques to overcome some of the challenges with the current MEA technology to 1) make MEAs more suitable for large-scale cell-based testing experiments and 2) establish a stable interface for in vivo chronic recordings. Moreover, we demonstrate the utility of the in vitro MEA-based approach as an emergent platform for new applications in brain research such as assessing the functional toxicity of novel material for brain implants and determining the response of neuronal populations to a biomarker of Alzheimer’s disease, amyloid beta.
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Keywords
Electrical engineering, Biomedical engineering, Neurosciences, Brain-machine interface, Functional assay, Microelectrode arrays, Neural interface, Neuronal recordings, Neurotechnology