Data and code from: Ultra-wide-field, deep, adaptive two-photon microscopy for multi-scale neuronal imaging
Data files
Mar 23, 2026 version files 1.16 GB
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Archive.zip
1.16 GB
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README.md
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Abstract
Observing the activity patterns of large neural populations throughout the brain is essential for understanding brain function. However, capturing neural interactions across widely distributed brain regions from both superficial and deep cortical layers remains challenging with existing microscopy technologies. Here, we introduce a state-of-the-art two-photon microscopy system, ULTRA, capable of single-cell resolution imaging across an ultra-large field of view (FOV) exceeding 50 mm2, enabling deep and wide field in vivo imaging. To demonstrate its capabilities, we conducted a series of experiments under multiple imaging conditions, successfully visualizing brain structures and neuronal activities spanning a spatial range of over 7 mm from superficial layers to depths of up to 900 μm, while covering a volume of 45.24 mm3 in the mouse brain. This versatile imaging platform overcomes traditional spatial constraints, providing a powerful tool for comprehensive exploration of neuronal circuitry over extensive spatial scales with cellular resolution. This dataset contains data sufficient to reconstruct certain of the figures in the paper.
Summary of dataset contents
Data were collected using ultra-wide-field, deep, adaptive two-photon microscopy to image the calcium signal of a large population of cells distributed over 2000 μm x 7000 μm FOV while the animal (with gcamp6s calcium indicator) was running on the air-floating chamber for 30 mins, where the locomotion information was recorded by Neurotar. Here, we used speed information for the functional connectivity analysis.
Description of the data and file structure
Regarding the calcium signals from ROIs, the recording has been split into 2 parts (upper and lower), in each folder, all preprocessed results from Suite2p were included. For the analysis, please load the spks.npy and iscell.npy accordingly. The whole FOV was segmented into functional region according to the Allen Brain Atlas (see brain segmentation for the results, first two columns are the coordinates for each cell in the FOV and the third one is the index number of the brain region), and as for the speed information, it was saved in the file named speed, each data point is the speed Neurotar measured corresponding to each calcium data point.
How to plot with code #1 and code #2 with data
1. Functional connectivity
load data "spks.npy", and "iscell.npy" in the folder of lower and upper, brain segmentation, and speed accordingly in the code #1
2. Cross-correlation by cell
load data "spks.npy", and "iscell.npy" in the folder of lower and upper, brain segmentation, and speed accordingly in the code #1
3. Cross-correlation by region
load data "spks.npy", and "iscell.npy" in the folder of lower and upper, brain segmentation, and speed accordingly in the code #1
4. Distribution of correlation as a function of distance between cells
load data "spks.npy", and "iscell.npy" in the folder of lower and upper, brain segmentation, and speed accordingly in the code #1
5. Graph theory analysis (Clustering Coefficients, Degree Distribution, Strength Distribution)
load data "spks.npy", and "iscell.npy" in the folder of lower and upper, brain segmentation, and speed accordingly in the code #2
Code/Software
All data can be viewed and processed using the code #1.py and code #2.py files with Python