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<p>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.</p>

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A Mouse Cortex Video Segmentation Dataset for Intrinsic Optical Signal Tracking and Neural Activity Analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月15日

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  • Wang Zhang,
  • Guang Zeng,
  • Zelong Zheng,
  • Hui Li,
  • Wenjian Qin,
  • Jianping Lv

摘要

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.