Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis

Diffusion kurtosis imaging (DKI), is an imaging modality that yields novel disease biomarkers and in combination with nervous tissue modeling, provides access to microstructural parameters. Recently, DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and associated animal models. In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to study the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals with varied grades of disability and scanned in a high-field MRI scanner along with five control specimen. Diffusion weighted data were acquired together with high resolution T2* images. Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model. T2* images were used to delineate focal demyelination/inflammation. Our results reveal a strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. Relationships between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity were similar to what has been found in other hypomyelinating MS models, and in patients. However, the changes in biophysical modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies employing other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability.

[1]  Carl-Fredrik Westin,et al.  Resolution limit of cylinder diameter estimation by diffusion MRI: The impact of gradient waveform and orientation dispersion , 2017, NMR in biomedicine.

[2]  Christopher D. Kroenke,et al.  Determination of Axonal and Dendritic Orientation Distributions Within the Developing Cerebral Cortex by Diffusion Tensor Imaging , 2012, IEEE Transactions on Medical Imaging.

[3]  Hsiao-Fang Liang,et al.  Formalin fixation alters water diffusion coefficient magnitude but not anisotropy in infarcted brain , 2005, Magnetic resonance in medicine.

[4]  Yaniv Assaf,et al.  Improved detectability of experimental allergic encephalomyelitis in excised swine spinal cords by high b-value q-space DWI , 2005, Experimental Neurology.

[5]  K. Muller,et al.  An R2 statistic for fixed effects in the linear mixed model , 2008, Statistics in medicine.

[6]  Daowei Li,et al.  Application value of diffusional kurtosis imaging (DKI) in evaluating microstructural changes in the spinal cord of patients with early cervical spondylotic myelopathy , 2017, Clinical Neurology and Neurosurgery.

[7]  H. Lassmann,et al.  Multiple sclerosis: experimental models and reality , 2016, Acta Neuropathologica.

[8]  R. Grossman,et al.  Quantification of normal-appearing white matter tract integrity in multiple sclerosis: a diffusion kurtosis imaging study , 2016, Journal of Neurology.

[9]  M. Inglese,et al.  Diffusion imaging in multiple sclerosis: research and clinical implications , 2010 .

[10]  C. Stam,et al.  Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study , 2018, Human brain mapping.

[11]  Massimo Filippi,et al.  Normal-appearing white and grey matter damage in MS , 2007, Journal of Neurology.

[12]  Bailey A. Box,et al.  Multi‐compartmental diffusion characterization of the human cervical spinal cord in vivo using the spherical mean technique , 2018, NMR in biomedicine.

[13]  Nikos Evangelou,et al.  Quantitative pathological evidence for axonal loss in normal appearing white matter in multiple sclerosis , 2000, Annals of neurology.

[14]  C. E. Rogers,et al.  Symbolic Description of Factorial Models for Analysis of Variance , 1973 .

[15]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[16]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[17]  Rafael Delgado y Palacios,et al.  Diffusion Kurtosis Imaging and High-Resolution MRI Demonstrate Structural Aberrations of Caudate Putamen and Amygdala after Chronic Mild Stress , 2014, PloS one.

[18]  Response to the comments on the paper by Horowitz et al. (2014) , 2015, Brain Structure and Function.

[19]  J. Dunn,et al.  Neuroimage: Clinical Understanding Disease Processes in Multiple Sclerosis through Magnetic Resonance Imaging Studies in Animal Models , 2022 .

[20]  F. Ståhlberg,et al.  On the effects of a varied diffusion time in vivo: is the diffusion in white matter restricted? , 2009, Magnetic resonance imaging.

[21]  P. Starewicz,et al.  A 24‐channel shim array for the human spinal cord: Design, evaluation, and application , 2016, Magnetic resonance in medicine.

[22]  Brian Hansen,et al.  Precision and accuracy of diffusion kurtosis estimation and the influence of b‐value selection , 2017, NMR in biomedicine.

[23]  N. Kampen,et al.  Stochastic processes in physics and chemistry , 1981 .

[24]  M. Reisert,et al.  A unique analytical solution of the white matter standard model using linear and planar encodings , 2018, Magnetic resonance in medicine.

[25]  T. Owens,et al.  Comparison of microglia and infiltrating CD11c+ cells as antigen presenting cells for T cell proliferation and cytokine response , 2014, Journal of Neuroinflammation.

[26]  Massimo Filippi,et al.  MR imaging of multiple sclerosis. , 2011, Radiology.

[27]  Derek K. Jones,et al.  Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter , 2016, NeuroImage.

[28]  Risto Lehtonen,et al.  Multilevel Statistical Models , 2005 .

[29]  M. F. Falangola,et al.  Preliminary observations of increased diffusional kurtosis in human brain following recent cerebral infarction , 2011, NMR in biomedicine.

[30]  P. Basser,et al.  New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter , 2004, Magnetic resonance in medicine.

[31]  Bibek Dhital,et al.  Gibbs‐ringing artifact removal based on local subvoxel‐shifts , 2015, Magnetic resonance in medicine.

[32]  Rohit Bakshi,et al.  MRI in multiple sclerosis: current status and future prospects , 2008, The Lancet Neurology.

[33]  Masaya Takahashi,et al.  [Diffusion imaging]. , 2003, Nihon Igaku Hoshasen Gakkai zasshi. Nippon acta radiologica.

[34]  R. Baayen,et al.  Mixed-effects modeling with crossed random effects for subjects and items , 2008 .

[35]  D. Sherrington Stochastic Processes in Physics and Chemistry , 1983 .

[36]  P. Basser,et al.  In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. , 2009, Brain : a journal of neurology.

[37]  Edward S Hui,et al.  Histological correlation of diffusional kurtosis and white matter modeling metrics in cuprizone‐induced corpus callosum demyelination , 2014, NMR in biomedicine.

[38]  C. Tench,et al.  Measurement of Spinal Cord Atrophy in Multiple Sclerosis , 2004, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[39]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[40]  Mollie E. Brooks,et al.  Generalized linear mixed models: a practical guide for ecology and evolution. , 2009, Trends in ecology & evolution.

[41]  J. Berger Functional improvement and symptom management in multiple sclerosis: clinical efficacy of current therapies. , 2011, The American journal of managed care.

[42]  I. V. Allen,et al.  Pathological abnormalities in the normal-appearing white matter in multiple sclerosis , 2001, Neurological Sciences.

[43]  J. Helpern,et al.  MRI quantification of non‐Gaussian water diffusion by kurtosis analysis , 2010, NMR in biomedicine.

[44]  V. Kiselev The Cumulant Expansion: An Overarching Mathematical Framework For Understanding Diffusion NMR , 2010 .

[45]  Alejandro F. Frangi,et al.  Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding , 2018, Magnetic resonance in medicine.

[46]  Brian Hansen,et al.  Experimentally and computationally fast method for estimation of a mean kurtosis , 2013, Magnetic resonance in medicine.

[47]  S. Amor,et al.  Multiple sclerosis animal models: a clinical and histopathological perspective , 2017, Brain pathology.

[48]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[49]  Bachir Taouli,et al.  Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice , 2015, Journal of magnetic resonance imaging : JMRI.

[50]  D. Barr,et al.  Random effects structure for confirmatory hypothesis testing: Keep it maximal. , 2013, Journal of memory and language.

[51]  David H. Miller,et al.  Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: a pilot study. , 2017, Functional neurology.

[52]  Alejandro F. Frangi,et al.  Double Diffusion Encoding Prevents Degeneracy in Parameter Estimation of Biophysical Models in Diffusion MRI , 2018, 1809.05059.

[53]  Carl-Fredrik Westin,et al.  The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE) , 2016, NeuroImage.

[54]  W. Rooney,et al.  MR Imaging of Inflammation during Myelin-Specific T Cell-Mediated Autoimmune Attack in the EAE Mouse Spinal Cord , 2010, Molecular Imaging and Biology.

[55]  R. Harald Baayen,et al.  Analyzing linguistic data: a practical introduction to statistics using R, 1st Edition , 2008 .

[56]  Frauke Zipp,et al.  Mouse model mimics multiple sclerosis in the clinico‐radiological paradox , 2007, The European journal of neuroscience.

[57]  V. Kiselev,et al.  Effective medium theory of a diffusion‐weighted signal , 2010, NMR in biomedicine.

[58]  R. Luechinger,et al.  Whole-Body Diffusion Kurtosis Imaging: Initial Experience on Non-Gaussian Diffusion in Various Organs , 2014, Investigative radiology.

[59]  E. Wu,et al.  MR diffusion kurtosis imaging for neural tissue characterization , 2010, NMR in biomedicine.

[60]  A. J. Thompson,et al.  Magnetic resonance studies of abnormalities in the normal appearing white matter and grey matter in multiple sclerosis , 2003, Journal of Neurology.

[61]  Yi-Hsin Weng,et al.  Parkinson disease: diagnostic utility of diffusion kurtosis imaging. , 2011, Radiology.

[62]  Joseph A. Helpern,et al.  Diffusion distinguishes between axonal loss and demyelination in brain white matter , 2011 .

[63]  D. Alexander,et al.  Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology? , 2017, Annals of clinical and translational neurology.

[64]  F. Barkhof The clinico‐radiological paradox in multiple sclerosis revisited , 2002, Current opinion in neurology.

[65]  W. L. Benedict,et al.  Multiple Sclerosis , 2007, Journal - Michigan State Medical Society.

[66]  David Baker,et al.  Experimental autoimmune encephalomyelitis is a good model of multiple sclerosis if used wisely. , 2014, Multiple sclerosis and related disorders.

[67]  J. Helpern,et al.  A Better Characterization of Spinal Cord Damage in Multiple Sclerosis: A Diffusional Kurtosis Imaging Study , 2013, American Journal of Neuroradiology.

[68]  Yulin Ge,et al.  Thalamus and cognitive impairment in mild traumatic brain injury: a diffusional kurtosis imaging study. , 2012, Journal of neurotrauma.

[69]  U. Bogdahn,et al.  Experimental autoimmune encephalomyelitis in the rat spinal cord: lesion detection with high-resolution MR microscopy at 17.6 T. , 2005, AJNR. American journal of neuroradiology.

[70]  F Barkhof,et al.  Axonal damage in the spinal cord of MS patients occurs largely independent of T2 MRI lesions , 2002, Neurology.

[71]  Joseph A. Helpern,et al.  Kurtosis analysis of neural diffusion organization , 2015, NeuroImage.

[72]  Qinhao Lin,et al.  Response to comments , 2004 .

[73]  Masaaki Hori,et al.  Diffusional kurtosis imaging of normal-appearing white matter in multiple sclerosis: preliminary clinical experience , 2012, Japanese Journal of Radiology.

[74]  Yaniv Assaf,et al.  Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain , 2005, NeuroImage.

[75]  M. Filippi,et al.  In vivo assessment of cervical cord damage in MS patients: a longitudinal diffusion tensor MRI study. , 2007, Brain : a journal of neurology.

[76]  J. Helpern,et al.  Stroke Assessment With Diffusional Kurtosis Imaging , 2012, Stroke.

[77]  C. Westin,et al.  Can the neurite density be estimated with diffusion MRI? A multidimensional MRI study using b-tensor encoding and multiple echo times , 2018 .

[78]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[79]  J. Veraart,et al.  Degeneracy in model parameter estimation for multi‐compartmental diffusion in neuronal tissue , 2016, NMR in biomedicine.

[80]  Markus Nilsson,et al.  Alterations of Diffusion Kurtosis and Neurite Density Measures in Deep Grey Matter and White Matter in Parkinson’s Disease , 2016, PloS one.

[81]  F. Ståhlberg,et al.  The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter , 2013, Magnetic Resonance Materials in Physics, Biology and Medicine.

[82]  A. Compston,et al.  For Personal Use. Only Reproduce with Permission from the Lancet Publishing Group. Pathological Physiology and Anatomy Multiple Sclerosis , 2022 .

[83]  Rohit Bakshi,et al.  Gray matter involvement in multiple sclerosis , 2007, Neurology.

[84]  Masaaki Hori,et al.  Cervical spondylosis: Evaluation of microstructural changes in spinal cord white matter and gray matter by diffusional kurtosis imaging. , 2014, Magnetic resonance imaging.

[85]  V. Kiselev,et al.  Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation , 2016, NMR in biomedicine.

[86]  Nicolas Kunz,et al.  Intra- and extra-axonal axial diffusivities in the white matter: Which one is faster? , 2018, NeuroImage.

[87]  Sheng-Kwei Song,et al.  Axial Diffusivity Is the Primary Correlate of Axonal Injury in the Experimental Autoimmune Encephalomyelitis Spinal Cord: A Quantitative Pixelwise Analysis , 2009, The Journal of Neuroscience.

[88]  M. S. Dresselhaus,et al.  Magnetic Resonance Studies , 1996 .

[89]  Leif Østergaard,et al.  Modeling dendrite density from magnetic resonance diffusion measurements , 2007, NeuroImage.

[90]  Bailey A. Box,et al.  Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients , 2017, NeuroImage: Clinical.

[91]  Daniel C. Alexander,et al.  Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo , 2015, NeuroImage.

[92]  Jelle Veraart,et al.  Diffusion MRI noise mapping using random matrix theory , 2016, Magnetic resonance in medicine.

[93]  M. Ptito,et al.  Contrast and stability of the axon diameter index from microstructure imaging with diffusion MRI , 2012, Magnetic resonance in medicine.

[94]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996. , 1996, Journal of magnetic resonance.

[95]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[96]  Eliza M. Gordon-Lipkin,et al.  Sensorimotor dysfunction in multiple sclerosis and column-specific magnetization transfer-imaging abnormalities in the spinal cord. , 2009, Brain : a journal of neurology.

[97]  Kathryn L. West,et al.  Evaluation of diffusion kurtosis imaging in ex vivo hypomyelinated mouse brains , 2016, NeuroImage.

[98]  Brian Hansen,et al.  White matter biomarkers from fast protocols using axially symmetric diffusion kurtosis imaging , 2016, NMR in biomedicine.

[99]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[100]  G. Paxinos,et al.  The Spinal Cord: A Christopher and Dana Reeve Foundation Text and Atlas , 2009 .

[101]  Joseph A. Helpern,et al.  White matter characterization with diffusional kurtosis imaging , 2011, NeuroImage.

[102]  K. Trinkaus,et al.  Increased diffusivity in acute multiple sclerosis lesions predicts risk of black hole , 2010, Neurology.

[103]  Andre D. A. Souza,et al.  Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies , 2008, NeuroImage.

[104]  Massimo Filippi,et al.  Association between pathological and MRI findings in multiple sclerosis , 2012, The Lancet Neurology.

[105]  Phillip Zhe Sun,et al.  Stratification of Heterogeneous Diffusion MRI Ischemic Lesion With Kurtosis Imaging: Evaluation of Mean Diffusion and Kurtosis MRI Mismatch in an Animal Model of Transient Focal Ischemia , 2012, Stroke.

[106]  Timothy Q. Duong,et al.  Spatiotemporal dynamics of diffusional kurtosis, mean diffusivity and perfusion changes in experimental stroke , 2012, Brain Research.

[107]  B. Weinstock-Guttman,et al.  Impact of diagnosis and early treatment on the course of multiple sclerosis. , 2013, The American journal of managed care.

[109]  Jan Sijbers,et al.  Gliomas: diffusion kurtosis MR imaging in grading. , 2012, Radiology.

[110]  Benjamin V. Tucker,et al.  The effects of N-gram probabilistic measures on the recognition and production of four-word sequences , 2011 .

[111]  Brian Hansen,et al.  Recent Developments in Fast Kurtosis Imaging , 2017, Front. Phys..

[112]  R. Rudick,et al.  Neurological disability correlates with spinal cord axonal loss and reduced N‐acetyl aspartate in chronic multiple sclerosis patients , 2000, Annals of neurology.

[113]  Mara Cercignani,et al.  Exploring the relationship between white matter and gray matter damage in early primary progressive multiple sclerosis: An in vivo study with TBSS and VBM , 2009, Human brain mapping.

[114]  J. Sijbers,et al.  More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging , 2011, Magnetic resonance in medicine.

[115]  H. Akaike INFORMATION THEORY AS AN EXTENSION OF THE MAXIMUM LIKELIHOOD , 1973 .

[116]  G. Giovannoni,et al.  Practical guide to the induction of relapsing progressive experimental autoimmune encephalomyelitis in the Biozzi ABH mouse. , 2012, Multiple sclerosis and related disorders.

[117]  M. Battaglini,et al.  Brain damage as detected by magnetization transfer imaging is less pronounced in benign than in early relapsing multiple sclerosis. , 2006, Brain : a journal of neurology.

[118]  Jelle Veraart,et al.  In vivo quantification of demyelination and recovery using compartment-specific diffusion MRI metrics validated by electron microscopy , 2016, NeuroImage.

[119]  Kathryn L. West,et al.  Experimental studies of g-ratio MRI in ex vivo mouse brain , 2018, NeuroImage.

[120]  T E Lund,et al.  Mean Diffusional Kurtosis in Patients with Glioma: Initial Results with a Fast Imaging Method in a Clinical Setting , 2015, American Journal of Neuroradiology.

[121]  R I Grossman,et al.  Non-Gaussian diffusion MRI of gray matter is associated with cognitive impairment in multiple sclerosis , 2015, Multiple sclerosis.

[122]  Hsiao-Fang Liang,et al.  Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords , 2011, NeuroImage.

[123]  Jelle Veraart,et al.  One diffusion acquisition and different white matter models: How does microstructure change in human early development based on WMTI and NODDI? , 2015, NeuroImage.

[124]  Marco Rovaris,et al.  Corpus callosum damage and cognitive dysfunction in benign MS , 2009, Human brain mapping.

[125]  G A Johnson,et al.  Diffusion‐weighted MR microscopy with fast spin‐echo , 1993, Magnetic resonance in medicine.

[126]  S. Jespersen,et al.  Kurtosis fractional anisotropy, its contrast and estimation by proxy , 2016, Scientific Reports.

[127]  C. Westin,et al.  Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling , 2018, Human brain mapping.

[128]  Y. Cohen,et al.  Diffusion MRI of the spinal cord: from structural studies to pathology , 2017, NMR in biomedicine.

[129]  Stephen J Blackband,et al.  Aldehyde fixative solutions alter the water relaxation and diffusion properties of nervous tissue , 2009, Magnetic resonance in medicine.

[130]  Shinichi Nakagawa,et al.  A general and simple method for obtaining R2 from generalized linear mixed‐effects models , 2013 .

[131]  J. Veraart,et al.  Mapping orientational and microstructural metrics of neuronal integrity with in vivo diffusion MRI , 2016, 1609.09144.

[132]  P. W. Stroman,et al.  The current state-of-the-art of spinal cord imaging: Applications , 2014, NeuroImage.

[133]  Jan Sijbers,et al.  Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls , 2013, NeuroImage.

[134]  Sune Nørhøj Jespersen,et al.  Erratum: Hansen, Lund, Sangill, and Jespersen. Experimentally and Computationally Fast Method for Estimation of a Mean Kurtosis. Magnetic Resonance in Medicine 69:1754–1760 (2013) , 2014 .

[135]  J. Nyengaard,et al.  Differential microstructural alterations in rat cerebral cortex in a model of chronic mild stress depression , 2018, PloS one.

[136]  Brian Hansen,et al.  Diffusion time dependence of microstructural parameters in fixed spinal cord , 2017, NeuroImage.

[137]  F. Barkhof,et al.  Unraveling the relationship between regional gray matter atrophy and pathology in connected white matter tracts in long‐standing multiple sclerosis , 2015, Human brain mapping.

[138]  Moses Rodriguez,et al.  Quantitative ultrastructural analysis of a single spinal cord demyelinated lesion predicts total lesion load, axonal loss, and neurological dysfunction in a murine model of multiple sclerosis. , 2000, The American journal of pathology.

[139]  Brian Hansen,et al.  Fast imaging of mean, axial and radial diffusion kurtosis , 2016, NeuroImage.

[140]  Paul C. Johnson Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models , 2014, Methods in ecology and evolution.

[141]  J. Sijbers,et al.  Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination , 2016, NeuroImage.