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Concepts

Miniscopes in Neuroscience Research

Miniature fluorescence microscopes (miniscopes) are a head-mounted calcium imaging full-frame video modality first introduced in 2005 by Mark Schnitzer's lab1. Due to their light weight, these miniscopes allow measuring the dynamic activity of populations of cortical neurons in freely behaving animals. In 2011, Inscopix Inc. was founded to support one-photon miniscopes as a commercial neuroscience research platform, providing proprietary hardware, acquisition software, and analysis software. Today, they estimate their active user base is 491 labs with a total of 1179 installs.

An open-source alternative was launched by a UCLA team led by Drs. Daniel Aharoni and Peyman Golshani23. In our conversation with Dr. Aharoni, he estimated about 700 labs currently using the UCLA system alone. The Inscopix user base is smaller but more established. Several two-photon miniscopes have been developed but lack widespread adoption likely due to the expensive hardware required for the two-photon excitation345. Due to the low costs and ability to record during natural behaviors, one-photon miniscope imaging appears to be the fastest growing calcium imaging modality in the field today.

The DataJoint team focused efforts on supporting the UCLA platform due rapid growth and limited standardization in acquisition and processing pipelines. In the future, we will reach out to Inscopix to support their platform as well.

Acquisition Tools

Dr. Daniel Aharoni's lab has developed iterations of the UCLA Miniscope platform. Based on interviews, we have found labs using the two most recent versions including Miniscope DAQ V3 and Miniscope DAQ V4. Labs also use the Bonsai OpenEphys tool for data acquisition with the UCLA miniscope. Inscopix provides the Inscopix Data Acquisition Software (IDAS) for the nVista and nVoke systems.

Preprocessing Tools

The preprocessing workflow for miniscope imaging includes denoising, motion correction, cell segmentation, and calcium event extraction (sometimes described as "deconvolution" or "spike inference"). For the UCLA Miniscopes, the following analysis packages are commonly used:

  • Miniscope Denoising, Daniel Aharoni (UCLA), Python
  • NoRMCorre, Flatiron Institute, MATLAB
  • CNMF-E, Pengcheng Zhou (Liam Paninski's Lab, Columbia University), MATLAB
  • CaImAn, Flatiron Institute, Python
  • miniscoPy, Guillaume Viejo (Adrien Peyrache's Lab, McGill University), Python
  • MIN1PIPE, Jinghao Lu (Fan Wang's Lab, MIT), MATLAB
  • CIAtah, Biafra Ahanonu, MATLAB
  • MiniAn, Phil Dong (Denise Cai's Lab, Mount Sinai), Python
  • MiniscopeAnalysis, Guillaume Etter (Sylvain Williams' Lab, McGill University), MATLAB
  • PIMPN, Guillaume Etter (Sylvain Williams's Lab, McGill University), Python
  • CellReg, Liron Sheintuch (Yaniv Ziv's Lab, Weizmann Institute of Science), MATLAB
  • Inscopix Data Processing Software (IDPS)
  • Inscopix Multimodal Image Registration and Analysis (MIRA)

Based on interviews with UCLA and Inscopix miniscope users and developers, each research lab uses a different preprocessing workflow. These custom workflows are often closed source and not tracked with version control software. For the preprocessing tools that are open source, they are often developed by an individual during their training period and lack funding for long term maintenance. These factors result in a lack of standardization for miniscope preprocessing tools, which is a major obstacle to adoption for new labs.

  1. Flusberg BA, Jung JC, Cocker ED, Anderson EP, Schnitzer MJ. In vivo brain imaging using a portable 3.9 gram two-photon fluorescence microendoscope. Opt Lett. 2005 Sep 1;30(17):2272-4. doi: 10.1364/ol.30.002272. PMID: 16190441. ↩

  2. Cai DJ, Aharoni D, Shuman T, Shobe J, Biane J, Song W, Wei B, Veshkini M, La-Vu M, Lou J, Flores SE, Kim I, Sano Y, Zhou M, Baumgaertel K, Lavi A, Kamata M, Tuszynski M, Mayford M, Golshani P, Silva AJ. A shared neural ensemble links distinct contextual memories encoded close in time. Nature. 2016 Jun 2;534(7605):115-8. doi: 10.1038/nature17955. Epub 2016 May 23. PMID: 27251287; PMCID: PMC5063500. ↩

  3. Aharoni D, Hoogland TM. Circuit Investigations With Open-Source Miniaturized Microscopes: Past, Present and Future. Front Cell Neurosci. 2019 Apr 5;13:141. doi: 10.3389/fncel.2019.00141. PMID: 31024265; PMCID: PMC6461004. ↩↩

  4. Helmchen F, Fee MS, Tank DW, Denk W. A miniature head-mounted two-photon microscope. high-resolution brain imaging in freely moving animals. Neuron. 2001 Sep 27;31(6):903-12. doi: 10.1016/s0896-6273(01)00421-4. PMID: 11580892. ↩

  5. Zong W, Wu R, Li M, Hu Y, Li Y, Li J, Rong H, Wu H, Xu Y, Lu Y, Jia H, Fan M, Zhou Z, Zhang Y, Wang A, Chen L, Cheng H. Fast high-resolution miniature two-photon microscopy for brain imaging in freely behaving mice. Nat Methods. 2017 Jul;14(7):713-719. doi: 10.1038/nmeth.4305. Epub 2017 May 29. PMID: 28553965. ↩