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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.

Key Partnerships

The DataJoint team have been in contact with the following teams who are eager to engage and adopt DataJoint-based workflows in their labs.

  • Adrien Peyrache Lab, McGill University
  • Peyman Golshani Lab, UCLA
  • Daniel Aharoni Lab, UCLA
  • Anne Churchland Lab, UCLA
  • Fan Wang Lab, MIT
  • Antoine Adamantidis Lab, University of Bern
  • Manolis Froudaraki Lab, FORTH
  • Allan Basbaum Lab, UCSF

Element Architecture

Each of the DataJoint Elements are a set of tables for common neuroinformatics modalities to organize, preprocess, and analyze data. Each node in the following diagram is either a table in the Element itself or a table that would be connected to the Element.

element-miniscope diagram

subject schema (API docs)

Although not required, most choose to connect the Session table to a Subject table.

Table Description
Subject Basic information of the research subject.

session schema (API docs)

Table Description
Session Unique experimental session identifier.

miniscope schema (API docs)

Tables related to importing, analyzing, and exporting miniscope data.

Table Description
Recording A table containing information about the equipment used (e.g. the acquisition hardware information).
RecordingInfo The metadata about this recording from the Miniscope DAQ software (e.g. frame rate, number of channels, frames, etc.).
MotionCorrection A table with information about motion correction performed on a recording.
MotionCorrection.RigidMotionCorrection A table with details of rigid motion correction (e.g. shifting in x, y).
MotionCorrection.NonRigidMotionCorrection and MotionCorrection.Block These tables describe the non-rigid motion correction.
MotionCorrection.Summary A table containing summary images after motion correction.
Segmentation This table specifies the segmentation step and its outputs, following the motion correction step.
Segmentation.Mask This table contains the image mask for the segmented region of interest.
MaskClassification This table contains information about the classification of Segmentation.Mask into a type (e.g. soma, axon, dendrite, artifact, etc.).
Fluorescence This table contains the fluorescence traces extracted from each Segmentation.Mask.
ActivityExtractionMethod A table with information about the activity extraction method (e.g. deconvolution) applied on the fluorescence trace.
Activity A table with neuronal activity traces from fluorescence trace (e.g. spikes).

miniscope_report schema (API docs)

Tables related to summary reports of miniscope data.

Table Description
QualityMetrics A table containing information about CaImAn estimates.

The above QualityMetrics table includes the following for each component in the CaImAn analysis:

  • r_values: Space correlation.
  • snr: Trace SNR.
  • cnn_preds: CNN predictions.

Pipeline Development

With assistance from Dr. Peyman Golshani's Lab (UCLA) we have added support for the UCLA Miniscope DAQ V3 acquisition tool and MiniscopeAnalysis preprocessing tool in element-miniscope and workflow-miniscope. They have provided example data for development.

Based on interviews, we are considering adding support for the tools listed below. The deciding factors include the number of users, long term support, quality controls, and python programming language (so that the preprocessing tool can be triggered within the element).

  • Acquisition tools + Miniscope DAQ V4 + Inscopix Data Acquisition Software (IDAS)
  • Preprocessing tools + Inscopix Data Processing Software (IDPS) + Inscopix Multimodal Image Registration and Analysis (MIRA) + MiniAn + CaImAn + CNMF-E + CellReg

Roadmap

Further development of this Element is community driven. Upon user requests and based on guidance from the Scientific Steering Group we will add features to this Element, such as:

  • Acquisition & Preprocessing tools
  • Inscopix
    • Data Acquisition Software (IDAS)
    • Data Processing Software (IDPS)
    • Multimodal Image Registration and Analysis (MIRA)
  • MiniAn
  • CaImAn
  • CNMF-E
  • CellReg

  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. ↩↩

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  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. ↩