Objective. Micro-electrocorticography (μECoG) offers a minimally invasive neural interface with high spatial resolution over large areas of cortex. However, electrode arrays with many contacts that are individually wired to external recording systems are cumbersome and make recordings in freely behaving rodents challenging. We report a novel high-density 60-electrode system for μECoG recording in freely moving rats. Approach. Multiplexed headstages overcome the problem of wiring complexity by combining signals from many electrodes to a smaller number of connections. We have developed a low-cost, multiplexed recording system with 60 contacts at 406 μm spacing. We characterized the quality of the electrode signals using multiple metrics that tracked spatial variation, evoked-response detectability, and decoding value. Performance of the system was validated both in anesthetized animals and freely moving awake animals. Main results. We recorded μECoG signals over the primary auditory cortex, measuring responses to acoustic stimuli across all channels. Single-trial responses had high signal-to-noise ratios (SNR) (up to 25 dB under anesthesia), and were used to rapidly measure network topography within ∼10 s by constructing all single-channel receptive fields in parallel. We characterized evoked potential amplitudes and spatial correlations across the array in the anesthetized and awake animals. Recording quality in awake animals was stable for at least 30 days. Finally, we used these responses to accurately decode auditory stimuli on single trials. Significance. This study introduces (1) a μECoG recording system based on practical hardware design and (2) a rigorous analytical method for characterizing the signal characteristics of μECoG electrode arrays. This methodology can be applied to evaluate the fidelity and lifetime of any μECoG electrode array. Our μECoG-based recording system is accessible and will be useful for studies of perception and decision-making in rodents, particularly over the entire time course of behavioral training and learning.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Cellular and Molecular Neuroscience
Insanally, M., Trumpis, M., Wang, C., Chiang, C-H., Woods, V., Palopoli-Trojani, K., Bossi, S., Froemke, R. C., & Viventi, J. (2016). A low-cost, multiplexed μeCoG system for high-density recordings in freely moving rodents. Journal of Neural Engineering, 13(2), -. . https://doi.org/10.1088/1741-2560/13/2/026030