Recording the spiking activity from subcellular compartments of neurons such as axons and dendrites during mouse behavior with 2-photon calcium imaging is increasingly common yet remains challenging due to low signal-to-noise, inaccurate region-of-interest (ROI) identification, movement artifacts, and difficulty in grouping ROIs from the same neuron. To address these issues, we present a computationally efficient preprocessing pipeline for subcellular signal detection, movement artifact identification, and ROI grouping. For subcellular signal detection, we capture the frequency profile of calcium transient dynamics by applying fast Fourier transform (FFT) on smoothed time-series calcium traces collected from axon ROIs. We then apply bandpass filtering methods (e.g., 0.05-0.12 Hz) to select ROIs that contain frequencies that match the power band of transients. To remove motion artifacts from z-plane movement, we apply principal component analysis on all calcium traces and use a bottom-up segmentation change-point detection model on the first principal component. After removing movement artifacts, we further identify calcium transients from noise by analyzing their prominence and duration. Finally, ROIs with high activity correlation are grouped using hierarchical or k-means clustering. Using axon ROIs in the CA1 region, we confirm that both clustering methods effectively determine the optimal number of clusters in pairwise correlation matrices, yielding similar groupings to "ground truth" data. Our approach provides a guideline for standardizing the extraction of physiological signals from subcellular compartments during rodent behavior with 2-photon calcium imaging.
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