Computational NeuroscienceComprehensive chronic laminar single-unit, multi-unit, and local field potential recording performance with planar single shank electrode arrays

- Established new methods for analyzing depth dependent recordings.
- Optimized novel metrics for quantifying evoked MU and LFP recording quality and stability.
- Neural recording of resting state underestimates the number of neurons available for recording.
- Depth of cortical layer IV can fluctuate in the 1st wk after implantation, but stabilizes after 2 wks.
- There is strong dependence on the biological layers of the cortex on chronic recordings.

BackgroundIntracortical electrode arrays that can record extracellular action potentials from small, targeted groups of neurons are critical for basic neuroscience research and emerging clinical applications. In general, these electrode devices suffer from reliability and variability issues, which have led to comparative studies of existing and emerging electrode designs to optimize performance. Comparisons of different chronic recording devices have been limited to single-unit (SU) activity and employed a bulk averaging approach treating brain architecture as homogeneous with respect to electrode distribution.New methodIn this study, we optimize the methods and parameters to quantify evoked multi-unit (MU) and local field potential (LFP) recordings in eight mice visual cortices.ResultsThese findings quantify the large recording differences stemming from anatomical differences in depth and the layer dependent relative changes to SU and MU recording performance over 6-months. For example, performance metrics in Layer V and stratum pyramidale were initially higher than Layer II/III, but decrease more rapidly. On the other hand, Layer II/III maintained recording metrics longer. In addition, chronic changes at the level of layer IV are evaluated using visually evoked current source density.Comparison with existing method(s)The use of MU and LFP activity for evaluation and tracking biological depth provides a more comprehensive characterization of the electrophysiological performance landscape of microelectrodes.ConclusionsA more extensive spatial and temporal insight into the chronic electrophysiological performance over time will help uncover the biological and mechanical failure mechanisms of the neural electrodes and direct future research toward the elucidation of design optimization for specific applications.