Yuan Augustine Xiaoran, Colonell Jennifer, Lebedeva Anna, Okun Michael, Charles Adam S, Harris Timothy D
Janelia Research Campus, Howard Hughes Medical Institute, USA.
Department of Biomedical Engineering, Center for Imaging Science Institute, Kavli Neuroscience Discovery Institute, Johns Hopkins University, USA.
bioRxiv. 2024 Apr 28:2023.08.03.551724. doi: 10.1101/2023.08.03.551724.
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 one to 47 days, with an 84% average recovery rate.
在多天内准确追踪同一神经元对于研究学习和适应过程中神经元活动的变化至关重要。高密度细胞外电生理记录探针(如Neuropixels)的进展为实现这一目标提供了一条有前景的途径。然而,由于组织相对于记录位点的非刚性移动(漂移)以及一些神经元信号的丢失,在多个记录中识别同一神经元变得复杂。在这里,我们提出了一种神经元追踪方法,该方法可以独立于大多数现有方法所使用的放电统计来识别相同的细胞。我们的方法基于尖峰分类簇的日间非刚性对齐。我们使用测量的视觉感受野在小鼠中验证了相同的细胞身份。该方法在间隔1至47天的数据集上取得了成功,平均恢复率为84%。