Multi-day neuron tracking in high-density electrophysiology recordings using earth mover’s distance
Augustine Xiaoran Yuan,
Jennifer Colonell,
Anna Lebedeva,
Michael Okun,
Adam S Charles,
Timothy D Harris
Affiliations
Augustine Xiaoran Yuan
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States; Department of Biomedical Engineering, Center for Imaging Science Institute, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, United States
Department of Biomedical Engineering, Center for Imaging Science Institute, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, United States
Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States; Department of Biomedical Engineering, Center for Imaging Science Institute, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, United States
Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.
