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Automated identification of mouse visual areas with intrinsic signal imaging

Nature Protocols volume 12, pages 32–43 (2017)Cite this article

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Abstract

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.

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Figure 1: Intrinsic imaging setup.
Figure 2: Example data from ISI experiments.
Figure 3: Mouse head frame surgery to prepare for intrinsic imaging (Steps 1–12).
Figure 4: Preparation of mouse for imaging (Steps 16–29).

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Acknowledgements

We thank the Callaway laboratory, especially P. Li, T. Ito, and E. Kim, for their help in developing this setup and protocol; D. Ringach (University of California, Los Angeles) for writing the initial version of the image acquisition code; and O. Odoemene (Cold Spring Harbor Laboratory) for encouraging the inception of this article. We would also like to thank the Allen Institute for Brain Science founders, Paul G. Allen and Jody Allen, for their vision, encouragement, and support. This work was supported by National Institutes of Health grants EY022577, MH063912, and EY019005, as well as grants from the Gatsby Charitable Foundation and the Rose Hills Foundation to M.E.G. A.L.J. was supported by the National Science Foundation and the Martinet Foundation.

Author information

Authors and Affiliations

  1. Salk Institute for Biological Studies, La Jolla, California, USA

    Ashley L Juavinett & Edward M Callaway

  2. The University of Texas at Austin, Austin, Texas, USA

    Ian Nauhaus

  3. The Allen Institute for Brain Science, Seattle, Washington, USA

    Marina E Garrett & Jun Zhuang

Authors
  1. Ashley L Juavinett
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  2. Ian Nauhaus
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  3. Marina E Garrett
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  4. Jun Zhuang
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  5. Edward M Callaway
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Contributions

A.L.J., I.N., M.E.G., and E.M.C. designed the experiments, developed the protocol, and wrote the manuscript. A.L.J. and M.E.G. collected and analyzed intrinsic signal imaging data. I.N., M.E.G., and A.L.J. wrote MATLAB code. J.Z. wrote Python code.

Corresponding author

Correspondence to Edward M Callaway.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Imager and Stimulator GUIs

a) Screenshot of Imager GUI for acquisition of intrinsic signal images b) Screenshot of Stimulator GUI, with three windows for stimulus modification and presentation. The ‘Main Window’ contains experiment information, the ‘Looper’ window allows you to choose which parameters to change from trial to trial, and the ‘paramSelect’ window shows a list of parameters that can be changed for each stimulus type.

Supplementary Figure 2 Analyzer File Structure

Diagram showing the structure of the .analyzer file that is saved during acquisition and contains all information about the experiment.

Supplementary information

Supplementary Figures and Text

Supplementary Figures 1 and 2 (PDF 479 kb)

Supplementary Data 1

Head frame 3D drawing file. Design file to allow reproduction of mouse head frame. (ZIP 1 kb)

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Juavinett, A., Nauhaus, I., Garrett, M. et al. Automated identification of mouse visual areas with intrinsic signal imaging. Nat Protoc 12, 32–43 (2017). https://doi.org/10.1038/nprot.2016.158

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