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, 54 (1), 203-12

Simultaneous Recording of ECoG and Intracortical Neuronal Activity Using a Flexible Multichannel Electrode-Mesh in Visual Cortex

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Simultaneous Recording of ECoG and Intracortical Neuronal Activity Using a Flexible Multichannel Electrode-Mesh in Visual Cortex

Haruo Toda et al. Neuroimage.

Abstract

Electrocorticogram (ECoG) is a well-balanced methodology for stably mapping brain surface local field potentials (LFPs) over a wide cortical region with high signal fidelity and minimal invasiveness to the brain tissue. To directly compare surface ECoG signals with intracortical neuronal activity immediately underneath, we fabricated a flexible multichannel electrode array with mesh-form structure using micro-electro-mechanical systems. A Parylene-C-based "electrode-mesh" for rats contained a 6×6 gold electrode array with 1-mm interval. Specifically, the probe had 800×800 μm(2) fenestrae in interelectrode spaces, through which simultaneous penetration of microelectrode was capable. This electrode-mesh was placed acutely or chronically on the dural/pial surface of the visual cortex of Long-Evans rats for up to 2 weeks. We obtained reliable trial-wise profiles of visually evoked ECoG signals through individual eye stimulation. Visually evoked ECoG signals from the electrode-mesh exhibited as well or larger signal amplitudes as intracortical LFPs and less across-trial variability than conventional silver-ball ECoG. Ocular selectivity of ECoG responses was correlated with that of intracortical spike/LFP activities. Moreover, single-trial ECoG signals carried sufficient information for predicting the stimulated eye with a correct performance approaching 90%, and the decoding was significantly generalized across sessions over 6 hours. Electrode impedance or signal quality did not obviously deteriorate for 2 weeks following implantation. These findings open up a methodology to directly explore ECoG signals with reference to intracortical neuronal sources and would provide a key to developing minimally invasive next-generation brain-machine interfaces.

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