RT Journal Article SR Electronic T1 A modular approach to handle in-vivo drift correction for high-density extracellular recordings JF bioRxiv FD Cold Spring Harbor Laboratory SP 2023.06.29.546882 DO 10.1101/2023.06.29.546882 A1 Garcia, Samuel A1 Windolf, Charlie A1 Boussard, Julien A1 Dichter, Benjamin A1 Buccino, Alessio P. A1 Yger, Pierre YR 2023 UL http://biorxiv.org/content/early/2023/06/29/2023.06.29.546882.abstract AB 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 an exhaustive 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 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.Competing Interest StatementThe authors have declared no competing interest.