Automated Computational Processing of 3‐D MR Images of Mouse Brain for Phenotyping of Living Animals

Christopher S. Medina1, Brett Manifold‐Wheeler1, Aaron Gonzales1, Elaine L. Bearer2

1 University of New Mexico Health Sciences Center, Albuquerque, 2 Division of Biology, California Institute of Technology, Pasadena, California
Publication Name:  Current Protocols in Molecular Biology
Unit Number:  Unit 29A.5
DOI:  10.1002/cpmb.40
Online Posting Date:  July, 2017
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Abstract

Magnetic resonance (MR) imaging provides a method to obtain anatomical information from the brain in vivo that is not typically available by optical imaging because of this organ's opacity. MR is nondestructive and obtains deep tissue contrast with 100‐µm3 voxel resolution or better. Manganese‐enhanced MRI (MEMRI) may be used to observe axonal transport and localized neural activity in the living rodent and avian brain. Such enhancement enables researchers to investigate differences in functional circuitry or neuronal activity in images of brains of different animals. Moreover, once MR images of a number of animals are aligned into a single matrix, statistical analysis can be done comparing MR intensities between different multi‐animal cohorts comprising individuals from different mouse strains or different transgenic animals, or at different time points after an experimental manipulation. Although preprocessing steps for such comparisons (including skull stripping and alignment) are automated for human imaging, no such automated processing has previously been readily available for mouse or other widely used experimental animals, and most investigators use in‐house custom processing. This protocol describes a stepwise method to perform such preprocessing for mouse. © 2017 by John Wiley & Sons, Inc.

Keywords: magnetic resonance imaging (MRI); manganese‐enhanced magnetic resonance imaging (MEMRI); computational analysis and preprocessing; mouse and rodent brain; phenotyping brain anatomy

     
 
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Table of Contents

  • Commentary
  • Literature Cited
  • Figures
     
 
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Materials

Basic Protocol 1:

  Materials
  • Unix‐based computer (to run skullstripper.py): Mac OS system with Xcode installed
    • Xcode, X11, or Xquartz are Apple's versions of the X server component of the X Windows system for Mac OS X. Xcode has become the Xquartz project, an open‐source effort to develop an X.org X Windows for Mac OS X. Xcode is required to run MATLAB, and is available for free download at https://xquartz.macosforge.org/trac/. A virtual Linux machine running on a PC‐type Windows computer also is compatible as long as it has a 64‐bit processor.
  • 3D MR Image Viewing Program
    • This is needed to review images as they move through the processing environment. We do not recommend any particular 3‐D program viewer, as the user's preferences should serve as a good indicator of which program(s) to choose. Examples of such programs include Statistical Parametric Mapping (SPM), FMRIB Software Library (FSL; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki; Jenkinson, Beckmann, Behrens, Woolrich, & Smith, ; Smith et al., ; Woolrich et al., ), Fiji (or ImageJ; http://imagej.nih.gov/ij; Paletzki & Gerfen, ; Schindelin et al., ), and MRIcron (http://people.cas.sc.edu/rorden/mricron/index.html; Rorden, Karnath, & Bonilha, ).
  • MATLAB
    • MATLAB is necessary for various processing steps and for the functioning of various other processing programs. MATLAB is an abbreviation for “matrix laboratory.” While other programming languages usually work with numbers one at a time, MATLAB operates on whole matrices and arrays. Language fundamentals include basic operations, such as creating variables, array indexing, arithmetic, and data types. MATLAB is a matrix‐based language from Mathworks for science and engineering applications, developed at the University of New Mexico. It is usually available for researchers on university campuses, although it may require purchase of a license, and can be downloaded at http://www.mathworks.com/products/matlab/.
  • FMRIB Software Library (FSL)
    • FSL is a comprehensive library of analytical tools for fMRI, MRI, and DTI brain imaging data that was created by the Analysis Group, FMRIB, Oxford, U.K. FSL runs on Apple computers and PCs (both Linux, and Windows via a Virtual Machine), and is very easy to install. Some processing components of FSL rely on MATLAB (see below). FSL is useful as a 3‐D viewer, and for various processing steps, and is available for free download at http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/.
  • Medical Image Processing, Analysis and Visualization (MIPAV)
    • MIVPAV was developed to facilitate human brain MR imaging and is a product of the U.S. National Institutes of Health. Some features of the program are also useful for rodent brain imaging. MIPAV is useful for N3 correction, and, when necessary, image transformations. It is available for free download at https://mipav.cit.nih.gov.
  • NiftyReg
    • NiftyReg is an alignment tool co‐created at University College London. NiftyReg also requires CMake (an open‐source cross‐platform build system; https://cmake.org/download/), and a compiler, such as XCode in MacOS (https://cmake.org/download/). Currently NiftyReg can use an accelerated graphics processing unit (GPU) with CUDA from NVIDIA. CUDA is a parallel computing platform and programming module developed by NVIDIA. CUDA speeds up image processing and can be downloaded from https://developer.nvidia.com/cuda‐downloads. NiftyReg is necessary for our in‐house skull‐stripping programs (Delora et al., ), and as an option for alignments later in this protocol. NiftyReg is available for free download at http://cmictig.cs.ucl.ac.uk/wiki/index.php/NiftyReg_install.
  • modal_scale_nii.m
    • The above is a MATLAB routine developed in the Jacobs’ lab, and has been applied in a variety of papers (Bearer et al., , b; Delora et al., ; Gallagher et al., , ; Malkova et al., ; Zhang et al., ), although the actual processing code has been unpublished up to now. The modal_scale code is provided here with this protocol for the first time (see Supplemental Materials, filename 160928_ModalScale.rtf). This short program normalizes the grayscale between MR images in NIFTI format (.nii) based on a proportional scaling of the mode of the intensity histogram to a user‐specified template image. It also produces a graph of the adjusted histogram for visual validation. This routine is useful for intensity scaling, and is provided here in the Supplemental Materials.
  • skullstripper.py
    • The above is a program that we created (Delora et al., ). It can be downloaded from our publication (Delora et al., ) or from the STMC Web site (http://stmc.health.unm.edu/tools‐and‐data/index.html). The computer code, installation instructions and application methods are found in the supplemental materials for that paper. This script is dependent on NiftyReg. Instructions to set up NiftyReg are included in the application from the journal Web site. skullstripper.py is useful for automated masking of non‐brain tissue and can be applied in batch so that multiple images are stripped in one speedy step.
  • Statistical Parametric Mapping (SPM)
    • This is an amazing software tool that allows voxel‐wise unbiased comprehensive analysis of the intensity pattern over the whole brain. SPM requires MATLAB to run. A new toolkit in SPM for mouse brain processing was developed in 2009 and can be loaded into earlier versions of SPM as an extension; this toolkit was presented at the ISMRM conference in Hawaii in 2009 (http://www.spmmouse.org; Sawiak, Wood, Williams, Morton, & Carpenter, ). With this plugin, SPM allows statistical analysis to be performed on brains with different voxel sizes. It is useful for reviewing alignments, viewing intensity histograms, checking image headers, linear and non‐linear warping, and smoothing images before application of statistical analysis. SPM is available for free download at http://www.fil.ion.ucl.ac.uk/spm/software/spm12/. SPM12 takes NIFTI‐format images (*.nii), whereas an earlier version, SPM8, uses Analyze format (*.img with *.hdr).
  • Example MR images
    • Example images are needed to verify that the protocol works and are provided in Supplemental Materials. These images are from datasets used to generate figures in this paper. They are in NIFTI (.nii) format in little‐endian byte order and 16‐bit signed integer type. Capture of these images is described in detail in our previous publications (Bearer et al., , ; Gallagher et al., , ; Manifold‐Wheeler, Gonzales, Chaves, Jacobs, & Bearer, ; Medina et al., ; Zhang et al., ). Images were captured at 24 hr after stereotaxic injection of Mn2+ into the CA3 of the right hippocampus with a 11.7 T Bruker biospin vertical bore MR scanner. These images are 160 × 128 × 88 voxels in dimension with voxel size of 0.1 mm, isotropic. All experimental procedures received prior approval by Caltech and UNM IACUCs.
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Figures

Videos

Literature Cited

 
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Key Reference
  Delora et al, (2016). See above.
  This paper provides the skullstripper.py program and more specific dialog on its applications and instructions on how to use it.
Internet Resources
  http://fsl.fmrib.ox.ac.uk/fsl/fslwiki
  Provides information on FMRIB Software Library (FSL), such as download instructions and instructions for use.
  http://imagej.nih.gov/ij
  Provides information on Fiji/ImageJ, such as download instructions and instructions for use.
  http://people.cas.sc.edu/rorden/mricron/index.html
  Provides information on MRIcron, such as download instructions and instructions for use.
  https://mipav.cit.nih.gov
  MIPAV instructions for N3 corrections.
  http://brainsuite.org/processing/surfaceextraction/bse/
  Provides information on BrainSuite, such as download instructions and instructions for use.
  http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET/UserGuide
  FSL user guide for Brain Extraction Toolkit (BET).
  http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf
  SPM12 Manual.
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