Automated identification of mouse visual areas with intrinsic signal imaging

• Published in: Nature protocols Vol. 12; no. 1; pp. 32 - 43
• Main Authors: Juavinett, Ashley L, Nauhaus, Ian, Garrett, Marina E, Zhuang, Jun, Callaway, Edward M
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.01.2017; Nature Publishing Group
• Subjects: 14; 59; 631/136/334/1874/345; 631/1647/245/2226; 631/378/2613; 631/378/3917; Algorithms; Analytical Chemistry; Animals; Automation; Biological Techniques; Brain; Brain architecture; Brain mapping; Computational Biology/Bioinformatics; Computational neuroscience; Hemodynamics; Image segmentation; In vivo methods and tests; Innovations; Laboratories; Life Sciences; Mapping; Medical examination; Mice; Mice, Inbred C57BL; Microarrays; Neuroimaging; Neurosciences; Optical communication; Optical Imaging - instrumentation; Optical Imaging - methods; Optical tomography; Organic Chemistry; Protocol; Retina; Signal Transduction; Source code; Topography; Visual cortex; Visual Cortex - cytology; Visual Cortex - physiology; Visual field; Visual Fields; Visual pathways; Visual signals; Visual stimuli; United States > US
• ISSN: 1754-2189; 1750-2799; 1750-2799
• DOI: 10.1038/nprot.2016.158

This protocol describes how to produce retinotopic maps of mouse visual cortex using intrinsic signal optical imaging and a segmentation algorithm. Intrinsic signal optical imaging (ISI) is a rapid and noninvasive method for observing brain activity in vivo over a large area of the cortex. Here we describe our protocol for mapping retinotopy to identify mouse visual cortical areas using ISI. First, surgery is performed to attach a head frame to the mouse skull (∼1 h). The next day, intrinsic activity across the visual cortex is recorded during the presentation of a full-field drifting bar in the horizontal and vertical directions (∼2 h). Horizontal and vertical retinotopic maps are generated by analyzing the response of each pixel during the period of the stimulus. Last, an algorithm uses these retinotopic maps to compute the visual field sign and coverage, and automatically construct visual borders without human input. Compared with conventional retinotopic mapping with episodic presentation of adjacent stimuli, a continuous, periodic stimulus is more resistant to biological artifacts. Furthermore, unlike manual hand-drawn approaches, we present a method for automatically segmenting visual areas, even in the small mouse cortex. This relatively simple procedure and accompanying open-source code can be implemented with minimal surgical and computational experience, and is useful to any laboratory wishing to target visual cortical areas in this increasingly valuable model system.