A modular implementation to handle and benchmark drift correction for high-density extracellular recordings.

Abstract

High-density neural devices are now offering the possibility to record from neuronal populations in-vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for “spike sorting”, an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a motion correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches.

Significance statement High-density extracellular recordings allow experimentalists to get access to the spiking activity of large neuronal populations, via the procedure of spike sorting. However, it is widely known that spike sorters are affected by drifts, i.e. the fact that neurons move with respect to the recording electrodes. While several algorithms have been proposed to handle drifts, a systematic comparison on the performance of these algorithms is still lacking. In this contribution, we performed a large comparison study to benchmark and understand the limitations of state-of-the-art drift correction methods. Our results suggest that they all have some intrinsic limitations, that should be taken into account by analysis pipelines.