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

Area of Science:

• Neuroscience
• Computational Biology
• Medical Imaging

Background:

• Intrinsic optical signal imaging (IOSI) is a non-invasive technique for monitoring neural activity in the mouse cortex.
• Quantitative analysis of IOSI data is challenging due to low signal intensity, complex patterns, and lack of standardized benchmarks.

Purpose of the Study:

• To present MouseCortex-IOS, a novel, open-access video segmentation dataset for standardizing IOS analysis in awake rodent models.
• To establish a benchmark for developing automated analysis tools for IOS data.

Main Methods:

• Developed MouseCortex-IOS dataset with 5732 images from 14 subjects.
• Implemented an efficient processing pipeline using foundation models for consistent annotation.
• The dataset enables quantitative characterization of neural activation parameters.

Main Results:

• The dataset provides a standardized resource for IOS analysis in rodent models.
• The processing pipeline ensures annotation consistency with minimal manual effort.
• Facilitates quantitative characterization of neural signal propagation velocity and trajectory.

Conclusions:

• MouseCortex-IOS serves as a critical benchmark for advancing automated analysis tools in IOS-based neuroscience.
• This resource accelerates technique development and computational integration in neuroimaging studies.