Semi-automated registration-based anatomical labelling, voxel based morphometry and cortical thickness mapping of the mouse brain

BACKGROUND Morphoanatomical MRI methods have recently begun to be applied in the mouse. However, substantial differences in the anatomical organisation of human and rodent brain prevent a straightforward extension of clinical neuroimaging tools to mouse brain imaging. As a result, the vast majority of the published approaches rely on tailored routines that address single morphoanatomical readouts and typically lack a sufficiently-detailed description of the complex workflow required to process images and quantify structural alterations. NEW METHOD Here we provide a detailed description of semi-automated registration-based procedures for voxel based morphometry, cortical thickness estimation and automated anatomical labelling of the mouse brain. The approach relies on the sequential use of advanced image processing tools offered by ANTs, a flexible open source toolkit freely available to the scientific community. RESULTS To illustrate our procedures, we described their application to quantify morphological alterations in socially-impaired BTBR mice with respect to normosocial C57BL/6J controls, a comparison recently described by us and other research groups. We show that the approach can reliably detect both focal and large-scale grey matter alterations using complementary readouts. COMPARISON WITH EXISTING METHODS No detailed operational workflows for mouse imaging are available for direct comparison with our methods. However, empirical assessment of the mapped inter-strain differences is in good agreement with the findings of other groups using analogous approaches. CONCLUSION The detailed operational workflows described here are expected to help the implementation of rodent morphoanatomical methods by non-expert users, and ultimately promote the use of these tools across the preclinical neuroimaging community.

[1]  Brian B. Avants,et al.  Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study , 2008, NeuroImage.

[2]  Babak Rezaee,et al.  A cluster validity index for fuzzy clustering , 2010, Fuzzy Sets Syst..

[3]  Brian B. Avants,et al.  Quantitative mouse brain phenotyping based on single and multispectral MR protocols , 2012, NeuroImage.

[4]  R Mark Henkelman,et al.  MRI phenotyping of genetically altered mice. , 2011, Methods in molecular biology.

[5]  Alan C. Evans,et al.  A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human Brain , 1992, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[6]  Alan C. Evans,et al.  Cortical thickness measured from MRI in the YAC128 mouse model of Huntington's disease , 2008, NeuroImage.

[7]  Emmanuel Chereul,et al.  Differential MRI patterns of brain atrophy in double or single transgenic mice for APP and/or SOD , 2008, Journal of neuroscience research.

[8]  Brian B. Avants,et al.  An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data , 2011, Neuroinformatics.

[9]  J. Gee,et al.  The Insight ToolKit image registration framework , 2014, Front. Neuroinform..

[10]  M.-C. Su,et al.  A new cluster validity measure and its application to image compression , 2004, Pattern Analysis and Applications.

[11]  Guy B. Williams,et al.  SPMMouse: A new toolbox for SPM in the animal brain , 2009 .

[12]  Satrajit S. Ghosh,et al.  Evaluation of volume-based and surface-based brain image registration methods , 2010, NeuroImage.

[13]  Arthur W. Toga,et al.  MBAT: A scalable informatics system for unifying digital atlasing workflows , 2010, BMC Bioinformatics.

[14]  Angelo Bifone,et al.  Structural covariance networks in the mouse brain , 2016, NeuroImage.

[15]  S. Hyman,et al.  Animal models of neuropsychiatric disorders , 2010, Nature Neuroscience.

[16]  R. Mark Henkelman,et al.  High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice , 2008, NeuroImage.

[17]  Marc Dhenain,et al.  In vivo MRI and histological evaluation of brain atrophy in APP/PS1 transgenic mice , 2006, Neurobiology of Aging.

[18]  Michael I. Miller,et al.  Longitudinal characterization of brain atrophy of a Huntington's disease mouse model by automated morphological analyses of magnetic resonance images , 2010, NeuroImage.

[19]  Andrew L. Janke,et al.  A segmentation protocol and MRI atlas of the C57BL/6J mouse neocortex , 2013, NeuroImage.

[20]  Lindsay S. Cahill,et al.  Preparation of fixed mouse brains for MRI , 2012, NeuroImage.

[21]  Daniel Rueckert,et al.  Longitudinal regional brain volume changes quantified in normal aging and Alzheimer's APP×PS1 mice using MRI , 2009, Brain Research.

[22]  A. Mauro,et al.  Deletion of the Snord116/SNORD116 Alters Sleep in Mice and Patients with Prader-Willi Syndrome. , 2016, Sleep.

[23]  C. Caltagirone,et al.  Effects of Omega-3 Fatty Acid Supplementation on Cognitive Functions and Neural Substrates: A Voxel-Based Morphometry Study in Aged Mice , 2016, Front. Aging Neurosci..

[24]  Jonathon Bishop,et al.  Magnetic resonance imaging for detection and analysis of mouse phenotypes , 2005, NMR in biomedicine.

[25]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[26]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[27]  R. Mark Henkelman,et al.  Neuroanatomical analysis of the BTBR mouse model of autism using magnetic resonance imaging and diffusion tensor imaging , 2013, NeuroImage.

[28]  M. Scattoni,et al.  Erratum to: Altered functional connectivity networks in acallosal and socially impaired BTBR mice , 2014, Brain Structure and Function.

[29]  Brian B. Avants,et al.  The optimal template effect in hippocampus studies of diseased populations , 2010, NeuroImage.

[30]  Diane Stephenson,et al.  Characterizing the Regional Structural Difference of the Brain between Tau Transgenic (rTg4510) and Wild-Type Mice Using MRI , 2010, MICCAI.

[31]  S. Mori,et al.  Magnetic resonance imaging and micro-computed tomography combined atlas of developing and adult mouse brains for stereotaxic surgery , 2009, Neuroscience.

[32]  Angelo Bifone,et al.  Neuroimaging Evidence of Major Morpho-Anatomical and Functional Abnormalities in the BTBR T+TF/J Mouse Model of Autism , 2013, PloS one.

[33]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[34]  G. Allan Johnson,et al.  Morphometric analysis of the C57BL/6J mouse brain , 2007, NeuroImage.

[35]  Guy B. Williams,et al.  Voxel-based morphometry with templates and validation in a mouse model of Huntington’s disease , 2013, Magnetic resonance imaging.

[36]  Scott Hamilton,et al.  In Vivo 3D Digital Atlas Database of the Adult C57BL/6J Mouse Brain by Magnetic Resonance Microscopy , 2008, Frontiers in neuroanatomy.

[37]  Martin Styner,et al.  Automatic cortical thickness analysis on rodent brain , 2011, Medical Imaging.

[38]  Ipek Oguz,et al.  Fully automated rodent brain MR image processing pipeline on a Midas server: from acquired images to region-based statistics , 2013, Front. Neuroinform..

[39]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[40]  Urs Meyer,et al.  Prenatal Immune Challenge Is an Environmental Risk Factor for Brain and Behavior Change Relevant to Schizophrenia: Evidence from MRI in a Mouse Model , 2009, PloS one.

[41]  Robert Turner,et al.  Voxel-based cortical thickness measurements in MRI , 2008, NeuroImage.

[42]  Dinggang Shen,et al.  Intermediate templates guided groupwise registration of diffusion tensor images , 2011, NeuroImage.

[43]  G. Allan Johnson,et al.  Waxholm Space: An image-based reference for coordinating mouse brain research , 2010, NeuroImage.

[44]  Sotirios A. Tsaftaris,et al.  Large-scale analysis of neuroimaging data on commercial clouds with content-aware resource allocation strategies , 2015, Int. J. High Perform. Comput. Appl..

[45]  John G. Sled,et al.  Wanted dead or alive? The tradeoff between in-vivo versus ex-vivo MR brain imaging in the mouse , 2011, Front. Neuroinform..

[46]  Daniel Rueckert,et al.  Analysis of serial magnetic resonance images of mouse brains using image registration , 2009, NeuroImage.

[47]  Alan C. Evans,et al.  A three-dimensional MRI atlas of the mouse brain with estimates of the average and variability. , 2005, Cerebral cortex.

[48]  Brian B. Avants,et al.  Symmetric Diffeomorphic Image Registration: Evaluating Automated Labeling of Elderly and Neurodegenerative Cortex and Frontal Lobe , 2006, WBIR.

[49]  Francesco Sforazzini,et al.  Distributed BOLD and CBV-weighted resting-state networks in the mouse brain , 2014, NeuroImage.

[50]  Angelo Bifone,et al.  COMT Genetic Reduction Produces Sexually Divergent Effects on Cortical Anatomy and Working Memory in Mice and Humans. , 2015, Cerebral cortex.

[51]  Brian B. Avants,et al.  Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis , 2010, NeuroImage.

[52]  N. Mercuri,et al.  Dysfunctional dopaminergic neurotransmission in asocial BTBR mice , 2014, Translational Psychiatry.

[53]  Valter Tucci,et al.  Dominant β-catenin mutations cause intellectual disability with recognizable syndromic features. , 2014, The Journal of clinical investigation.

[54]  D Keeser,et al.  Functional and Structural MR Imaging in Neuropsychiatric Disorders, Part 2: Application in Schizophrenia and Autism , 2012, American Journal of Neuroradiology.

[55]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[56]  G. Allan Johnson,et al.  High-throughput morphologic phenotyping of the mouse brain with magnetic resonance histology , 2007, NeuroImage.

[57]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[58]  Brian B. Avants,et al.  Registration based cortical thickness measurement , 2009, NeuroImage.

[59]  Veronique D. Bohbot,et al.  Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning , 2011, NeuroImage.

[60]  Milan Sonka,et al.  LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain , 2014, IEEE Transactions on Medical Imaging.