Spatial Tracking Across Time (STAT): Tracking Neurons Across In-Vivo Imaging Sessions through Optimizing Local Neighborhood Motion Consistency

Chronic calcium imaging has become a powerful and indispensable tool for analyzing the long-term stability and plasticity of neuronal activity. One crucial step of the data processing pipeline is to register individual neurons across imaging sessions, which usually extend over a few days or even months, and show various levels of spatial deformation of the imaged field of view (FOV). Previous solutions align FOVs of all sessions first and then register the same neurons according to their shapes and locations [[1][1], [2][2]]. However, the FOV registration is computational intensive, especially in the case of nonrigid case. Here we propose a cell tracking method that does not require FOV image registration. Specifically, the algorithm STAT (short for S tay T ogether, A lign T ogether, and for S patial T racking A cross T ime) represents neurons from two sessions as two sets of neuronal centroids, uses point set registration (PSR) to find a spatially smooth transformation to align them while assigning correspondences. The optimization method iteratively updates between the general motion and individual neuron identity tracking, an idea seen in the computer vision literatures [[3][3], [4][4]]. Our method can be thought of as a specialization and simplification of these more general methods to calcium imaging neuron tracking. We validate STAT on datasets with simulated nonrigid motion that is hard to motion correct without extensive manual intervention. Next, we test STAT on experimental data from singing birds collected on three different days, and observe stable song-locked activity across days. An example use case of this package is reference [[5][5]]. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5