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Two-way communication with neural networks in vivo using focused light

Nature Protocols volume 8, pages 1184–1203 (2013)Cite this article

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Abstract

Neuronal networks process information in a distributed, spatially heterogeneous manner that transcends the layout of electrodes. In contrast, directed and steerable light offers the potential to engage specific cells on demand. We present a unified framework for adapting microscopes to use light for simultaneous in vivo stimulation and recording of cells at fine spatiotemporal resolutions. We use straightforward optics to lock onto networks in vivo, to steer light to activate circuit elements and to simultaneously record from other cells. We then actualize this 'free' augmentation on both an 'open' two-photon microscope and a leading commercial one. By following this protocol, setup of the system takes a few days, and the result is a noninvasive interface to brain dynamics based on directed light, at a network resolution that was not previously possible and which will further improve with the rapid advance in development of optical reporters and effectors. This protocol is for physiologists who are competent with computers and wish to extend hardware and software to interface more fluidly with neuronal networks.

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Figure 1: A general template for open, all-optical physiology on custom and commercial microscopes.
Figure 2: Steps necessary for the reduction of all-optical technology to routine practice in the laboratory.
Figure 3: Step-by-step guide to computer-guided imaging with higher signal to noise.
Figure 4: Enhancement of fidelity of action potential detection via computer-guided imaging in vitro and in vivo.
Figure 5: Direct methods for validation of optical signals as action potentials.
Figure 6: Optimization of reliable optogenetic activation for all-optical physiology.
Figure 7: Selective optical activation of single neurons in vivo during concurrent optical reporting.

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Acknowledgements

This work was supported by postdoctoral fellowships from the US National Institutes of Health (NIH) and the Simons Foundation (N.R.W.), an NIH predoctoral fellowship (C.A.R.) and grants from the NIH and the Simons Foundation (M.S.). We sincerely thank the ScanImage team at Janelia Farm, particularly V. Iyer, for providing an open platform on which others can build on and learn from. We thank K. Wang for assistance with the cell detection algorithm. We thank M. Goard for help with mirror feedback computation and S. El-Boustani for help with deconvolution. We thank V. Nikolenko for technical advice and Prairie Technologies, especially J. Rafter, E. Heins, P. Gustafson, M. Nazir and C. McCallum for working closely with us during the development of all of these techniques and providing a stable commercial platform with numerous points of entry for customization. Finally, we thank B. Land as a model in scientific communication, whose public sharing of documentation helped in our early days to learn key principles of computer control that could then be extended here.

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Author notes

  1. Nathan R Wilson and James Schummers: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA

    Nathan R Wilson, Caroline A Runyan, Sherry X Yan, Robert E Chen, Yuting Deng & Mriganka Sur

  2. Cellular Organization of Cortical Circuit Function Group, Max Planck Florida Institute, Jupiter, Florida, USA

    James Schummers

  3. Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA

    Caroline A Runyan

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  1. Nathan R Wilson
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Contributions

N.R.W. and J.S. contributed equally to the work. N.R.W. built the systems, wrote the software, performed experiments and analyses, and wrote the manuscript. J.S. conceived the ideas and chain of algorithms that would avail this system, performed analyses, helped develop the software and helped write the manuscript. C.A.R. improved the system and contributed critical in vivo experiments to validate it; S.X.Y. developed and calibrated the all-optical methodology; R.E.C. helped program critical algorithms for cell detection and image motion correction; Y.D. helped initiate and develop the all-optical methodology and M.S. encouraged these developments, steered their application, helped write the manuscript, and supported the refinement of a robust and general protocol applicable to other laboratories.

Corresponding author

Correspondence to Mriganka Sur.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Figure 1

Platform for high-speed, targeted neuronal activation during concurrent two-photon imaging, electrophysiology and sensory stimulation (PDF 718 kb)

Supplementary Figure 2

Further calibration of targeted cell stimulation for optical activation in vivo (PDF 6102 kb)

Supplementary Figure 3

Specificity of activation of neurons in the z-plane (PDF 2556 kb)

Supplementary Data 1

Mirror positions on some microscopes will lag command signals by a significant, variable amount that is repeatable and predictable (PDF 414 kb)

Supplementary Data 2

Landing on the cells with the help of mirror feedback (PDF 424 kb)

Supplementary Data 3

By knowing the actual position of the mirrors at the point when fluorescence is detected, it is possible to attribute the intensity of the signal back to the cell bodies that emitted them (PDF 602 kb)

Supplementary Data 4

With careful calibration and proper attribution of fluorescence to spatial locations, delivering sensory stimuli across repeated trials in vivo will result in calcium traces collected from a given cell with a consistent selectivity for those sensory stimuli (PDF 2128 kb)

Supplementary Data 5

Analysis of measured fluorescence signals and signal-to-noise ratios. (PDF 650 kb)

Supplementary Data 6

Proper calibration of the system will also allow for clean calcium transients that can be deconvolved to inferred spike trains automatically using the software (PDF 1182 kb)

Supplementary Note 1

Overview of the Software (PDF 429 kb)

Supplementary Note 2

Controlling a Prairie Microscope with ScanImage Software (PDF 233 kb)

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Wilson, N., Schummers, J., Runyan, C. et al. Two-way communication with neural networks in vivo using focused light. Nat Protoc 8, 1184–1203 (2013). https://doi.org/10.1038/nprot.2013.063

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