Abstract
Intrinsic optical signal imaging (IOSI) enables non-invasive monitoring of neural activity in the mouse cortex, yet quantitative analysis remains hindered by low signal intensity, complex spatiotemporal patterns, and the lack of standardized benchmarks. To tackle this challenge, we present MouseCortex-IOS, a novel open accessible video segmentation dataset specifically designed for standardizing IOS analysis in awake rodent models, comprising 5732 images from 14 experimental subjects. Meanwhile, we implement an efficient processing pipeline leveraging foundation models to ensure annotation consistency while minimizing manual intervention. The dataset supports quantitative characterization of neural activation parameters including signal propagation velocity and trajectory, serving as a critical benchmark for developing automated analysis tools. Furthermore, this resource facilitates technique development in neuroimaging studies and accelerates the integration of computational approaches in IOS-based neuroscience research.
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Data availability
The dataset is publicly available on figshare: https://doi.org/10.6084/m9.figshare.28601813.
Code availability
The preprocessing code and other scripts that have been used in the pipeline are accessible at: https://github.com/ZhangWang-hub/MouseCortex-IOS. The open-source labeling tool can be download at: https://github.com/yatengLG/ISAT_with_segment_anything.
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Acknowledgements
The research was funded by Guangdong Basic and Applied Basic Research Foundation (2021A1515220151), Guangzhou Science and Technology Planning Program (No. 202201020518), Guangzhou Science and Technology Projects (2023A03J0958), Guangzhou Municipal Key R and D Program, China (2024B03J0947). Additionally, the authors would like to express sincere gratitude to the developers of the open-sourced AI toolkit ISAT_with_segment_anything (ISAT) for making this valuable resource publicly available.
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W.Z. created the dataset, performed the analysis and post-processing, and wrote the manuscript. G.Z. designed and constructed the optical acquisition platform, conducted experimental data acquisition, and performed partial data preprocessing. Z.Z., H.L., W.Q. and J.L. analyzed the results and provided suggestions. All authors actively participated in revising the manuscript.
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Zhang, W., Zeng, G., Zheng, Z. et al. A Mouse Cortex Video Segmentation Dataset for Intrinsic Optical Signal Tracking and Neural Activity Analysis. Sci Data (2026). https://doi.org/10.1038/s41597-026-06580-1
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DOI: https://doi.org/10.1038/s41597-026-06580-1
