Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods

Alexander Song; Jeff L Gauthier; Jonathan W Pillow; David W Tank; Adam S Charles

doi:10.1016/j.jneumeth.2021.109173

Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods

J Neurosci Methods. 2021 Jul 1:358:109173. doi: 10.1016/j.jneumeth.2021.109173. Epub 2021 Apr 8.

Authors

Alexander Song¹, Jeff L Gauthier², Jonathan W Pillow³, David W Tank⁴, Adam S Charles⁵

Affiliations

¹ Princeton Neuroscience Institute, Princeton University, Princeton, 08540 NJ, USA; Department of Physics, Princeton University, Princeton, 08540 NJ, USA.
² Princeton Neuroscience Institute, Princeton University, Princeton, 08540 NJ, USA.
³ Princeton Neuroscience Institute, Princeton University, Princeton, 08540 NJ, USA; Department of Psychology, Princeton University, Princeton, 08540 NJ, USA.
⁴ Princeton Neuroscience Institute, Princeton University, Princeton, 08540 NJ, USA; Bezos Center for Neural Circuit Dynamics, Princeton University, Princeton, 08540 NJ, USA; Department of Molecular Biology, Princeton University, Princeton, 08540 NJ, USA.
⁵ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA; Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, 21218, MD, USA; Center for Imaging Science, Johns Hopkins University, Baltimore, 21218, MD, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, 21218, MD, USA.

Abstract

Background: The past decade has seen a multitude of new in vivo functional imaging methodologies. However, the lack of ground-truth comparisons or evaluation metrics makes the large-scale, systematic validation vital to the continued development and use of optical microscopy impossible.

New-method: We provide a new framework for evaluating two-photon microscopy methods via in silico Neural Anatomy and Optical Microscopy (NAOMi) simulation. Our computationally efficient model generates large anatomical volumes of mouse cortex, simulates neural activity, and incorporates optical propagation and scanning to create realistic calcium imaging datasets.

Results: We verify NAOMi simulations against in vivo two-photon recordings from mouse cortex. We leverage this in silico ground truth to directly compare different segmentation algorithms and optical designs. We find modern segmentation algorithms extract strong neural time-courses comparable to estimation using oracle spatial information, but with an increase in the false positive rate. Comparison between optical setups demonstrate improved resilience to motion artifacts in sparsely labeled samples using Bessel beams, increased signal-to-noise ratio and cell-count using low numerical aperture Gaussian beams and nuclear GCaMP, and more uniform spatial sampling with temporal focusing versus multi-plane imaging.

Comparison with existing methods: NAOMi is a first-of-its kind framework for assessing optical imaging modalities. Existing methods are either anatomical simulations or do not address functional imaging. Thus there is no competing method for simulating realistic functional optical microscopy data.

Conclusions: By leveraging the rich accumulated knowledge of neural anatomy and optical physics, we provide a powerful new tool to assess and develop important methods in neural imaging.

Keywords: Large-scale recordings; Neural data analysis; Neural simulation; Two-photon microscopy.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Animals
Artifacts
Calcium*
Computer Simulation
Mice
Microscopy*

Substances

Calcium

Abstract

Publication types

MeSH terms

Substances

Grants and funding