Brain segmentation, spatial censoring, and averaging techniques for optical functional connectivity imaging in mice.

Area of Science:

• Neuroimaging
• Optical Neuroimaging
• Functional Connectivity Analysis

Background:

• Resting-state functional connectivity (RSFC) analysis via optical neuroimaging offers a link between animal models and human neurological monitoring.
• Current optical neuroimaging analysis techniques are underdeveloped, and magnetic resonance imaging (MRI) algorithms are not always suitable for optical data.

Purpose of the Study:

• To develop advanced visual processing tools for optical neuroimaging.
• To enhance data quality, brain segmentation, and session averaging in optical neuroimaging studies.
• To improve the applicability of optical neuroimaging in both preclinical research and clinical settings.

Main Methods:

• Development of novel visual processing algorithms tailored for optical neuroimaging data.
• Implementation of improved brain segmentation and field-of-view averaging techniques.
• Validation using resting-state optical intrinsic signal data from normal mice.

Main Results:

• Demonstrated improved performance in resting-state optical intrinsic signal analysis.
• Significant increase in the quantity of usable neuroimaging data.
• Enhanced image fidelity and more reliable brain segmentation.

Conclusions:

• The developed visual processing tools significantly improve optical neuroimaging data quality and analysis.
• These methods enhance the utility of optical neuroimaging for studying brain function in animal models.
• The tools are translatable to human optical neuroimaging systems, potentially advancing clinical neurological monitoring.