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
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library


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

PDF or HTML at Wiley Online Library

Table of Contents

  • Commentary
  • Literature Cited
  • Figures
PDF or HTML at Wiley Online Library


Basic Protocol 1:

  • Unix‐based computer (to run 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 Windows for Mac OS X. Xcode is required to run MATLAB, and is available for free download at 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;; Jenkinson, Beckmann, Behrens, Woolrich, & Smith, ; Smith et al., ; Woolrich et al., ), Fiji (or ImageJ;; Paletzki & Gerfen, ; Schindelin et al., ), and MRIcron (; Rorden, Karnath, & Bonilha, ).
    • 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
  • 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
  • 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
  • NiftyReg
    • NiftyReg is an alignment tool co‐created at University College London. NiftyReg also requires CMake (an open‐source cross‐platform build system;, and a compiler, such as XCode in MacOS ( 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‐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
  • 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.
    • 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 (‐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. 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 (; 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 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.
PDF or HTML at Wiley Online Library



Literature Cited

  Balaban, R. S., & Hampshire, V. A. (2001). Challenges in small animal noninvasive imaging. ILAR Journal/National Research Council, Institute of Laboratory Animal Resources, 42, 248–262. doi: 10.1093/ilar.42.3.248.
  Bearer, E. L., Falzone, T. L., Zhang, X., Biris, O., Rasin, A., & Jacobs, R. E. (2007a). Role of neuronal activity and kinesin on tract tracing by manganese‐enhanced MRI (MEMRI). NeuroImage, 37(Suppl 1), S37–46. doi: 10.1016/j.neuroimage.2007.04.053.
  Bearer, E. L., Zhang, X., & Jacobs, R. E. (2007b). Live imaging of neuronal connections by magnetic resonance: Robust transport in the hippocampal‐septal memory circuit in a mouse model of Down syndrome. NeuroImage, 37, 230–242. doi: 10.1016/j.neuroimage.2007.05.010.
  Bearer, E. L., Zhang, X., Janvelyan, D., Boulat, B., & Jacobs, R. E. (2009). Reward circuitry is perturbed in the absence of the serotonin transporter. NeuroImage, 46, 1091–1104. doi: 10.1016/j.neuroimage.2009.03.026.
  Chuang, K. H., Lee, J. H., Silva, A. C., Belluscio, L., & Koretsky, A. P. (2009). Manganese enhanced MRI reveals functional circuitry in response to odorant stimuli. NeuroImage, 44, 363–372. doi: 10.1016/j.neuroimage.2008.08.046.
  Delora, A., Gonzales, A., Medina, C. S., Mitchell, A., Mohed, A. F., Jacobs, R. E., & Bearer, E. L. (2016). A simple rapid process for semi‐automated brain extraction from magnetic resonance images of the whole mouse head. Journal of Neuroscience Methods, 257, 185–193. doi: 10.1016/j.jneumeth.2015.09.031.
  Dinov, I., Lozev, K., Petrosyan, P., Liu, Z., Eggert, P., Pierce, J., … Toga, A. (2010). Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline. PloS One, 5, e13070. doi: 10.1371/journal.pone.0013070.
  Dinov, I. D., Van Horn, J. D., Lozev, K. M., Magsipoc, R., Petrosyan, P., Liu, Z., … Toga, A. W. (2009). Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline. Frontiers in Neuroinformatics, 3, 22. doi: 10.3389/neuro.11.022.2009.
  Drapeau, P., & Nachshen, D. A. (1984). Manganese fluxes and manganese‐dependent neurotransmitter release in presynaptic nerve endings isolated from rat brain. The Journal of Physiology, 348, 493–510. doi: 10.1113/jphysiol.1984.sp015121.
  Ellegood, J., Anagnostou, E., Babineau, B. A., Crawley, J. N., Lin, L., Genestine, M., … Lerch, J. P. (2015). Clustering autism: Using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity. Molecular Psychiatry, 20, 118–125. doi: 10.1038/mp.2014.98.
  Eschenko, O., Evrard, H. C., Neves, R. M., Beyerlein, M., Murayama, Y., & Logothetis, N. K. (2012). Tracing of noradrenergic projections using manganese‐enhanced MRI. NeuroImage, 59, 3252–3265. doi: 10.1016/j.neuroimage.2011.11.031.
  Fornasiero, D., Bellen, J. C., Baker, R. J., & Chatterton, B. E. (1987). Paramagnetic complexes of manganese(II), iron(III), and gadolinium(III) as contrast agents for magnetic resonance imaging. The influence of stability constants on the biodistribution of radioactive aminopolycarboxylate complexes. Investigative Radiology, 22, 322–327. doi: 10.1097/00004424‐198704000‐00008.
  Friston, K. J. (2005). Models of brain function in neuroimaging. Annual Review of Psychology, 56, 57–87. doi: 10.1146/annurev.psych.56.091103.070311.
  Gallagher, J. J., Zhang, X., Hall, F. S., Uhl, G. R., Bearer, E. L., & Jacobs, R. E. (2013). Altered reward circuitry in the norepinephrine transporter knockout mouse. PloS One, 8, e57597. doi: 10.1371/journal.pone.0057597.
  Gallagher, J. J., Zhang, X., Ziomek, G. J., Jacobs, R. E., & Bearer, E. L. (2012). Deficits in axonal transport in hippocampal‐based circuitry and the visual pathway in APP knock‐out animals witnessed by manganese enhanced MRI. NeuroImage, 60, 1856–1866. doi: 10.1016/j.neuroimage.2012.01.132.
  Geraldes, C. F., Sherry, A. D., Brown, R. D. 3rd, & Koenig, S. H. (1986). Magnetic field dependence of solvent proton relaxation rates induced by Gd3+ and Mn2+ complexes of various polyaza macrocyclic ligands: Implications for NMR imaging. Magnetic Resonance in Medicine, 3, 242–250. doi: 10.1002/mrm.1910030207.
  Gillespy, T. 3rd, Richardson, M. L., & Rowberg, A. H. (1994). Displaying radiologic images on personal computers: Practical applications and uses. Journal of Digital Imaging, 7, 101–106. doi: 10.1007/BF03168502.
  Gillespy, T. 3rd, & Rowberg, A. H. (1993). Radiological images on personal computers: Introduction and fundamental principles of digital images. Journal of Digital Imaging, 6, 81–87. doi: 10.1007/BF03168434.
  Henkelman, R. M. (2010). Systems biology through mouse imaging centers: Experience and new directions. Annual Review of Biomedical Engineering, 12, 143–166. doi: 10.1146/annurev‐bioeng‐070909‐105343.
  Hoyer, C., Gass, N., Weber‐Fahr, W., & Sartorius, A. (2014). Advantages and challenges of small animal magnetic resonance imaging as a translational tool. Neuropsychobiology, 69, 187–201. doi: 10.1159/000360859.
  Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. NeuroImage, 62, 782–790. doi: 10.1016/j.neuroimage.2011.09.015.
  Kochunov, P., Lancaster, J. L., Thompson, P., Woods, R., Mazziotta, J., Hardies, J., & Fox, P. (2001). Regional spatial normalization: Toward an optimal target. Journal of Computer Assisted Tomography, 25, 805–816. doi: 10.1097/00004728‐200109000‐00023.
  Koretsky, A. P., & Silva, A. C. (2004). Manganese‐enhanced magnetic resonance imaging (MEMRI). NMR in Biomedicine, 17, 527–531. doi: 10.1002/nbm.940.
  Kovacevic, N., Henderson, J. T., Chan, E., Lifshitz, N., Bishop, J., Evans, A. C., … Chen, X. J. (2005). A three‐dimensional MRI atlas of the mouse brain with estimates of the average and variability. Cerebral Cortex, 15, 639–645. doi: 10.1093/cercor/bhh165.
  Larsen, C. T., Iglesias, J. E., & Van Leemput, K. (2014). N3 Bias field correction explained as a bayesian modeling method. In M.J. Cardoso, I. Simpson, T. Arbel, D. Precup, & A. Ribbens (Eds.) Bayesian and graphical models for biomedical imaging, (pp. 1–12. Lecture Notes in Computer Science, vol. 8677). Switzerland: Springer International Publishing. doi: 10.1007/978‐3‐319‐12289‐2_1.
  Lerch, J. P., Sled, J. G., & Henkelman, R. M. (2011). MRI phenotyping of genetically altered mice. Methods in Molecular Biology, 711, 349–361. doi: 10.1007/978‐1‐61737‐992‐5_17.
  Lin, Y. J., & Koretsky, A. P. (1997). Manganese ion enhances T1‐weighted MRI during brain activation: An approach to direct imaging of brain function. Magnetic Resonance in Medicine, 38, 378–388. doi: 10.1002/mrm.1910380305.
  Lindsey, J. D., Grob, S. R., Scadeng, M., Duong‐Polk, K., & Weinreb, R. N. (2013). Ocular integrity following manganese labeling of the visual system for MRI. Magnetic Resonance Imaging, 31, 865–874. doi: 10.1016/j.mri.2012.11.012.
  Ma, D., Cardoso, M. J., Modat, M., Powell, N., Wells, J., Holmes, H., … Ourselin, S. (2014). Automatic structural parcellation of mouse brain MRI using multi‐atlas label fusion. PLoS One, 9, e86576. doi: 10.1371/journal.pone.0086576.
  Malkova, N. V., Gallagher, J. J., Yu, C. Z., Jacobs, R. E., & Patterson, P. H. (2014). Manganese‐enhanced magnetic resonance imaging reveals increased DOI‐induced brain activity in a mouse model of schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 111, E2492–2500. doi: 10.1073/pnas.1323287111.
  Manifold‐Wheeler, B., Gonzales, A., Chaves, F., Jacobs, R. E., & Bearer, E. L. (2016). Alterations in the hippocampal‐forebrain circuitry with aging and with plaque formation. Neurobiology of Aging, in preparation.
  Massaad, C. A., & Pautler, R. G. (2011). Manganese‐enhanced magnetic resonance imaging (MEMRI). Methods in Molecular Biology, 711, 145–174. doi: 10.1007/978‐1‐61737‐992‐5_7.
  Medina, C. S., Biris, O., Falzone, T. L., Zhang, X., Zimmerman, A. J., & Bearer, E. L. (2017). Hippocampal to basal forebrain transport of Mn2+ is impaired by deletion of KLC1, a subunit of the conventional kinesin microtubule‐based motor. NeuroImage, 145, 44–57. doi: 10.1016/j.neuroimage.2016.09.035.
  Mendonca‐Dias, M. H., Gaggelli, E., & Lauterbur, P. C. (1983). Paramagnetic contrast agents in nuclear magnetic resonance medical imaging. Seminars in Nuclear Medicine, 13, 364–376. doi: 10.1016/S0001‐2998(83)80048‐8.
  Merritt, J. E., Jacob, R., & Hallam, T. J. (1989). Use of manganese to discriminate between calcium influx and mobilization from internal stores in stimulated human neutrophils. The Journal of Biological Chemistry, 264, 1522–1527.
  Modat, M., Ridgway, G. R., Taylor, Z. A., Lehmann, M., Barnes, J., Hawkes, D. J., … Ourselin, S. (2010). Fast free‐form deformation using graphics processing units. Computer Methods and Programs in Biomedicine, 98, 278–284. doi: 10.1016/j.cmpb.2009.09.002.
  Narita, K., Kawasaki, F., & Kita, H. (1990). Mn and Mg influxes through Ca channels of motor nerve terminals are prevented by verapamil in frogs. Brain Research, 510, 289–295. doi: 10.1016/0006‐8993(90)91379‐U.
  Nieman, B. J., Bishop, J., Dazai, J., Bock, N. A., Lerch, J. P., Feintuch, A., … Henkelman, R. M. (2007). MR technology for biological studies in mice. NMR in Biomedicine, 20, 291–303. doi: 10.1002/nbm.1142.
  Nieman, B. J., Bock, N. A., Bishop, J., Chen, X. J., Sled, J. G., Rossant, J., & Henkelman, R. M. (2005). Magnetic resonance imaging for detection and analysis of mouse phenotypes. NMR in Biomedicine, 18, 447–468. doi: 10.1002/nbm.981.
  Ourselin, S., Roche, A., Subsol, G., Pennec, X., & Ayache, N. (2001). Reconstructing a 3D structure from serial histological sections. Image and Vision Computing, 19, 25–31. doi: 10.1016/S0262‐8856(00)00052‐4.
  Paletzki, R., & Gerfen, C. R. (2015). Whole mouse brain image reconstruction from serial coronal sections using FIJI (ImageJ). Current Protocols in Neuroscience, 73, 1 25 21–21. doi: 10.1002/0471142301.ns0125s73.
  Pautler, R. G. (2004). In vivo, trans‐synaptic tract‐tracing utilizing manganese‐enhanced magnetic resonance imaging (MEMRI). NMR in Biomedicine, 17, 595–601. doi: 10.1002/nbm.942.
  Pautler, R. G., & Koretsky, A. P. (2002). Tracing odor‐induced activation in the olfactory bulbs of mice using manganese‐enhanced magnetic resonance imaging. NeuroImage, 16, 441–448. doi: 10.1006/nimg.2002.1075.
  Pautler, R. G., Mongeau, R., & Jacobs, R. E. (2003). In vivo trans‐synaptic tract tracing from the murine striatum and amygdala utilizing manganese enhanced MRI (MEMRI). Magnetic Resonance in Medicine, 50, 33–39. doi: 10.1002/mrm.10498.
  Pautler, R. G., Silva, A. C., & Koretsky, A. P. (1998). In vivo neuronal tract tracing using manganese‐enhanced magnetic resonance imaging. Magnetic Resonance in Medicine, 40, 740–748. doi: 10.1002/mrm.1910400515.
  Pitman, R. K., Rasmusson, A. M., Koenen, K. C., Shin, L. M., Orr, S. P., Gilbertson, M. W., … Liberzon, I. (2012). Biological studies of post‐traumatic stress disorder. Nature Reviews Neuroscience, 13, 769–787. doi: 10.1038/nrn3339.
  Rajeswari, R., & Rajesh, R. (2009). On the efficient compression of FMRI data series of brain. The Neuroradiology Journal, 21, 737–743. doi: 10.1177/197140090802100601.
  Rex, D. E., Ma, J. Q., & Toga, A. W. (2003). The LONI pipeline processing environment. NeuroImage, 19, 1033–1048. doi: 10.1016/S1053‐8119(03)00185‐X.
  Rorden, C., Karnath, H. O., & Bonilha, L. (2007). Improving lesion‐symptom mapping. Journal of Cognitive Neuroscience, 19, 1081–1088. doi: 10.1162/jocn.2007.19.7.1081.
  Sawiak, S. J., Wood, N. I., Williams, G. B., Morton, A. J., & Carpenter, T. A. (2009). Voxel‐based morphometry in the R6/2 transgenic mouse reveals differences between genotypes not seen with manual 2D morphometry. Neurobiology of Disease, 33, 20–27. doi: 10.1016/j.nbd.2008.09.016.
  Schindelin, J., Arganda‐Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., … Cardona, A. (2012). Fiji: An open‐source platform for biological‐image analysis. Nature Methods, 9, 676–682. doi: 10.1038/nmeth.2019.
  Scholz, J., LaLiberte, C., van Eede, M., Lerch, J. P., & Henkelman, M. (2016). Variability of brain anatomy for three common mouse strains. NeuroImage, 142, 656–662. doi: 10.1016/j.neuroimage.2016.03.069.
  Shattuck, D. W., & Leahy, R. M. (2001). Automated graph‐based analysis and correction of cortical volume topology. IEEE Transactions on Medical Imaging, 20, 1167–1177. doi: 10.1109/42.963819.
  Shattuck, D. W., & Leahy, R. M. (2002). BrainSuite: An automated cortical surface identification tool. Medical Image Analysis, 6, 129–142. doi: 10.1016/S1361‐8415(02)00054‐3.
  Silva, A. C., & Bock, N. A. (2008). Manganese‐enhanced MRI: An exceptional tool in translational neuroimaging. Schizophrenia Bulletin, 34, 595–604. doi: 10.1093/schbul/sbn056.
  Silva, A. C., Lee, J. H., Aoki, I., & Koretsky, A. P. (2004). Manganese‐enhanced magnetic resonance imaging (MEMRI): Methodological and practical considerations. NMR in Biomedicine, 17, 532–543. doi: 10.1002/nbm.945.
  Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17, 87–97. doi: 10.1109/42.668698.
  Sloot, W. N., & Gramsbergen, J. B. (1994). Axonal transport of manganese and its relevance to selective neurotoxicity in the rat basal ganglia. Brain Research, 657, 124–132. doi: 10.1016/0006‐8993(94)90959‐8.
  Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17, 143–155. doi: 10.1002/hbm.10062.
  Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen‐Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Suppl 1), S208–219. doi: 10.1016/j.neuroimage.2004.07.051.
  Smith, K. D., Kallhoff, V., Zheng, H., & Pautler, R. G. (2007). In vivo axonal transport rates decrease in a mouse model of Alzheimer's disease. NeuroImage, 35, 1401–1408. doi: 10.1016/j.neuroimage.2007.01.046.
  Smith, K. D., Paylor, R., & Pautler, R. G. (2011). R‐flurbiprofen improves axonal transport in the Tg2576 mouse model of Alzheimer's disease as determined by MEMRI. Magnetic Resonance in Medicine, 65, 1423–1429. doi: 10.1002/mrm.22733.
  Takeda, A. (2003). Manganese action in brain function. Brain Research Brain Research Reviews, 41, 79–87. doi: 10.1016/S0165‐0173(02)00234‐5.
  Takeda, A., Kodama, Y., Ishiwatari, S., & Okada, S. (1998). Manganese transport in the neural circuit of rat CNS. Brain Research Bulletin, 45, 149–152. doi: 10.1016/S0361‐9230(97)00330‐4.
  Van der Linden, A., Van Meir, V., Tindemans, I., Verhoye, M., & Balthazart, J. (2004). Applications of manganese‐enhanced magnetic resonance imaging (MEMRI) to image brain plasticity in song birds. NMR in Biomedicine, 17, 602–612. doi: 10.1002/nbm.936.
  Wadghiri, Y. Z., Blind, J. A., Duan, X., Moreno, C., Yu, X., Joyner, A. L., & Turnbull, D. H. (2004). Manganese‐enhanced magnetic resonance imaging (MEMRI) of mouse brain development. NMR in Biomedicine, 17, 613–619. doi: 10.1002/nbm.932.
  Wang, F. H., Appelkvist, P., Klason, T., Gissberg, O., Bogstedt, A., Eliason, K., … Sandin, J. (2012). Decreased axonal transport rates in the Tg2576 APP transgenic mouse: Improvement with the gamma‐secretase inhibitor MRK‐560 as detected by manganese‐enhanced MRI. The European Journal of Neuroscience, 36, 3165–3172. doi: 10.1111/j.1460‐9568.2012.08258.x.
  Watanabe, T., Michaelis, T., & Frahm, J. (2001). Mapping of retinal projections in the living rat using high‐resolution 3D gradient‐echo MRI with Mn2+‐induced contrast. Magnetic Resonance in Medicine, 46, 424–429. doi: 10.1002/mrm.1209.
  Woods, R. P., Grafton, S. T., Holmes, C. J., Cherry, S. R., & Mazziotta, J. C. (1998). Automated image registration: I. General methods and intrasubject, intramodality validation. Journal of Computer Assisted Tomography, 22, 139–152. doi: 10.1097/00004728‐199801000‐00027.
  Woolrich, M. W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., … Smith, S. M. (2009). Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45, S173–186. doi: 10.1016/j.neuroimage.2008.10.055.
  Yu, X., Wadghiri, Y. Z., Sanes, D. H., & Turnbull, D. H. (2005). In vivo auditory brain mapping in mice with Mn‐enhanced MRI. Nature Neuroscience, 8, 961–968. doi: 10.1038/nn1477.
  Zhang, X., Bearer, E. L., Boulat, B., Hall, F. S., Uhl, G. R., & Jacobs, R. E. (2010a). Altered neurocircuitry in the dopamine transporter knockout mouse brain. PloS One, 5, e11506. doi: 10.1371/journal.pone.0011506.
  Zhang, X., Bearer, E. L., Perles‐Barbacaru, A. T., & Jacobs, R. E. (2010b). Increased anatomical detail by in vitro MR microscopy with a modified Golgi impregnation method. Magnetic Resonance in Medicine, 63, 1391–1397. doi: 10.1002/mrm.22322.
Key Reference
  Delora et al, (2016). See above.
  This paper provides the program and more specific dialog on its applications and instructions on how to use it.
Internet Resources
  Provides information on FMRIB Software Library (FSL), such as download instructions and instructions for use.
  Provides information on Fiji/ImageJ, such as download instructions and instructions for use.
  Provides information on MRIcron, such as download instructions and instructions for use.
  MIPAV instructions for N3 corrections.
  Provides information on BrainSuite, such as download instructions and instructions for use.
  FSL user guide for Brain Extraction Toolkit (BET).
  SPM12 Manual.
PDF or HTML at Wiley Online Library