MEDiCINe: Motion Correction for Neural Electrophysiology Recordings
Name
ENEURO.0529-24.2025.full.pdf
Description
Published version
Size
1.24 MB
Format
Adobe PDF
Checksum (MD5)
9a4e76abf92c4c2f0d7bed7931f7a8da
Author(s)
Watters, Nicholas
Buccino, Alessio
Jazayeri, Mehrdad
Date Issued
March 2025
Journal
eNeuro
Publisher
Society for Neuroscience
Citation
Nicholas Watters, Alessio Buccino, Mehrdad Jazayeri
eNeuro 11 February 2025, 12 (3) ENEURO.0529-24.2025
Version
Final published version
Abstract
Electrophysiology recordings from the brain using laminar multielectrode arrays allow researchers to measure the activity of many neurons simultaneously. However, laminar microelectrode arrays move relative to their surrounding neural tissue for a variety of reasons, such as pulsation, changes in intracranial pressure, and decompression of neural tissue after insertion. Inferring and correcting for this motion stabilizes the recording and is critical to identify and track single neurons across time. Such motion correction is a preprocessing step of standard spike-sorting methods. However, estimating motion robustly and accurately in electrophysiology recordings is challenging due to the stochasticity of the neural data. To tackle this problem, we introduce MEDiCINe (Motion Estimation by Distributional Contrastive Inference for Neurophysiology), a novel motion estimation method. We show that MEDiCINe outperforms existing motion estimation methods on an extensive suite of simulated neurophysiology recordings and leads to more accurate spike sorting. We also show that MEDiCINe accurately estimates the motion in primate and rodent electrophysiology recordings with a variety of motion and stability statistics. We open-source MEDiCINe, usage instructions, examples integrating MEDiCINe with common tools for spike sorting, and data and code for reproducing our results. This open software will enable other researchers to use MEDiCINe to improve spike sorting results and get the most out of their electrophysiology datasets.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1523/ENEURO.0529-24.2025
