Analysis code for data presented in Mamiya et al. (2023) "Origins of proprioceptor feature selectivity and topographic maps in the Drosophila leg" Neuron
Jupyter notebook containing the class and parameters for preprocessing all two-photon imaging data.
- Read two-photon imaging data acquired by ScanImage, demultiplex the channels, filter and register images, align two-photon images with high-speed video images.
- Shows analyses examples.
Jupyter notebook containing the class, functions, and parameters for tracking the tibia in high-speed video images.
- Read high-speed video images, detect tibia in the region of interest, track the tibia angle, and calculate the average tibia angle for each two-photon imaging frame.
- Shows analyses examples.
Jupyter notebook containg the class, functions, and parameters for tracking the cells in the two-photon fast-z-stack images.
- Make z-projected image and use marker based watershed segmentation to segment cells. Then use the centroid of the previous frame's segmenation as a marker for the segment in the next frame. Initialize the cell segmentations from different starting frame and move forward/backwards to make multiple segmentations. Use the segmentations that is robust to the initial frame and movement direction.
- Shows analyses examples.
Jupyter notebook containing the functions and parameters for calculating the DR/R, based on the cell segmentation data and the fluorescence from tdTomato and GCaMP7.
- Based on the cell segmentation data, calculate DR/R and show the activity vs position, activity vs tibia angle, etc.
- Shows analysis examples.