Detecting microstructural properties of white matter based on compartmentalization of magnetic susceptibility

The microscopic structure of neuronal tissue is crucial to brain function, with axon diameter, axonal density and myelination directly influencing signal conduction in the white matter. There is increasing evidence that these microstructural properties alter signal in magnetic resonance imaging (MRI) driven by magnetic susceptibility of different compartments (e.g., myelin sheaths and iron-laden cells). To explain these observations, we have developed a multi-compartmental geometric model of whitematter microstructure. Using a single set of literature parameters, this forward model predicts experimentally observed orientation dependence and temporal evolution of the MRI signal. Where previous models have aimed to explain only the orientation dependence of signal phase, the proposed approach encapsulates the full repertoire of signal behavior. The frequency distribution underlying signal behavior is predicted to be a rich source of microstructural information with relevance to neuronal pathology.

[1]  C. Nicholson,et al.  Extracellular space structure revealed by diffusion analysis , 1998, Trends in Neurosciences.

[2]  C. Moonen,et al.  A fast calculation method for magnetic field inhomogeneity due to an arbitrary distribution of bulk susceptibility , 2003 .

[3]  E. Syková,et al.  Glia and volume transmission during physiological and pathological states , 2004, Journal of Neural Transmission.

[4]  Johannes Reichold,et al.  The microvascular system of the striate and extrastriate visual cortex of the macaque. , 2008, Cerebral cortex.

[5]  Carlo Caltagirone,et al.  Characterization of white matter fiber bundles with T  2* relaxometry and diffusion tensor imaging , 2009, Magnetic resonance in medicine.

[6]  Christine Stadelmann,et al.  Extensive Cortical Remyelination in Patients with Chronic Multiple Sclerosis , 2007, Brain pathology.

[7]  P. Jezzard,et al.  Asymmetries of the balanced SSFP profile. Part II: White matter , 2010, Magnetic resonance in medicine.

[8]  Jeff H. Duyn,et al.  The contribution of myelin to magnetic susceptibility-weighted contrasts in high-field MRI of the brain , 2012, NeuroImage.

[9]  R. Bowtell,et al.  Application of a Fourier‐based method for rapid calculation of field inhomogeneity due to spatial variation of magnetic susceptibility , 2005 .

[10]  Yu-Chung N. Cheng,et al.  Magnetic Resonance Imaging: Physical Principles and Sequence Design , 1999 .

[11]  J. Schenck.  The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. , 1996, Medical physics.

[12]  Yiping P. Du,et al.  Fast multislice mapping of the myelin water fraction using multicompartment analysis of T  2* decay at 3T: A preliminary postmortem study , 2007, Magnetic resonance in medicine.

[13]  W. Helfrich,et al.  Magnetic anisotropy of egg lecithin membranes. , 1978, Biophysical journal.

[14]  A. MacKay,et al.  The influence of white matter fibre orientation on MR signal phase and decay , 2011, NMR in biomedicine.

[15]  H. Schipper,et al.  Neurodegeneration with brain iron accumulation - clinical syndromes and neuroimaging. , 2012, Biochimica et biophysica acta.

[16]  U. Klose,et al.  The in vivo influence of white matter fiber orientation towards B0 on T2* in the human brain , 2010, NMR in biomedicine.

[17]  D. Yablonskiy,et al.  Biophysical mechanisms of phase contrast in gradient echo MRI , 2009, Proceedings of the National Academy of Sciences.

[18]  Rolf Gruetter,et al.  In vivo assessment of myelination by phase imaging at high magnetic field , 2012, NeuroImage.

[19]  Jeff H. Duyn,et al.  T2*-based fiber orientation mapping , 2011, NeuroImage.

[20]  Mark D Does,et al.  Diffusion of myelin water , 2006, Magnetic resonance in medicine.

[21]  M. Bronskill,et al.  Anisotropy of NMR properties of tissues , 1994, Magnetic resonance in medicine.

[22]  S. Majumdar.  Quantitative study of the susceptibility difference between trabecular bone and bone marrow: Computer simulations , 1991, Magnetic resonance in medicine.

[23]  Jeff H. Duyn,et al.  In vivo quantification of T2 ⁎ anisotropy in white matter fibers in marmoset monkeys , 2012, NeuroImage.

[24]  R A Knight,et al.  MR imaging of human brain at 3.0 T: preliminary report on transverse relaxation rates and relation to estimated iron content. , 1999, Radiology.

[25]  M. Fukunaga,et al.  Sensitivity of MRI resonance frequency to the orientation of brain tissue microstructure , 2010, Proceedings of the National Academy of Sciences.

[26]  E. Haacke,et al.  Imaging iron stores in the brain using magnetic resonance imaging. , 2005, Magnetic resonance imaging.

[27]  J. Duyn,et al.  Nonexponential T2* decay in white matter , 2012, Magnetic resonance in medicine.

[28]  R. Bowtell,et al.  Fiber orientation-dependent white matter contrast in gradient echo MRI , 2012, Proceedings of the National Academy of Sciences.

[29]  Bing Wu,et al.  High-field (9.4T) MRI of brain dysmyelination by quantitative mapping of magnetic susceptibility , 2011, NeuroImage.

[30]  S. Love,et al.  Qualitative and quantitative morphology of human sural nerve at different ages. , 1985, Brain : a journal of neurology.

[31]  J. R. Reichenbach,et al.  Non-linear evolution of GRE phase as a means to investigate tissue microstructure , 2010 .

[32]  Jeff H. Duyn,et al.  Susceptibility contrast in high field MRI of human brain as a function of tissue iron content , 2009, NeuroImage.

[33]  A. Scheibel,et al.  Fiber composition of the human corpus callosum , 1992, Brain Research.

[34]  A. MacKay,et al.  In vivo visualization of myelin water in brain by magnetic resonance , 1994, Magnetic resonance in medicine.

[35]  Chunlei Liu.  Susceptibility tensor imaging , 2010, Magnetic resonance in medicine.

[36]  D. Rusakov,et al.  Asymmetry of glia near central synapses favors presynaptically directed glutamate escape. , 2002, Biophysical journal.

[37]  Jeff H. Duyn,et al.  High-field MRI of brain cortical substructure based on signal phase , 2007, Proceedings of the National Academy of Sciences.

[38]  Michael G. Fehlings,et al.  Visualization of cytoplasmic diffusion within living myelin sheaths of CNS white matter axons using microinjection of the fluorescent dye Lucifer Yellow , 2011, NeuroImage.

[39]  Rolf Gruetter,et al.  On the origin of the MR image phase contrast: An in vivo MR microscopy study of the rat brain at 14.1 T , 2009, NeuroImage.

[40]  B R Rosen,et al.  Mr contrast due to intravascular magnetic susceptibility perturbations , 1995, Magnetic resonance in medicine.

[41]  W. Rushton.  A theory of the effects of fibre size in medullated nerve , 1951, The Journal of physiology.

[42]  Ravi S. Menon,et al.  Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. , 1993, Biophysical journal.

[43]  R. H. M. King,et al.  Neurocytology: Fine Structure of Neurons, Nerve Processes, and Neuroglial Cells. , 1996 .

[44]  J W Langston,et al.  The correlation between phase shifts in gradient-echo MR images and regional brain iron concentration. , 1999, Magnetic resonance imaging.

[45]  M. Fukunaga,et al.  Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast , 2010, Proceedings of the National Academy of Sciences.

[46]  E. Haacke,et al.  Theory of NMR signal behavior in magnetically inhomogeneous tissues: The static dephasing regime , 1994, Magnetic resonance in medicine.

[47]  Jeff H. Duyn,et al.  On the contribution of deoxy-hemoglobin to MRI gray–white matter phase contrast at high field , 2010, NeuroImage.

[48]  S. Ogawa,et al.  Magnetic resonance imaging of blood vessels at high fields: In vivo and in vitro measurements and image simulation , 1990, Magnetic resonance in medicine.

[49]  C. Laule,et al.  Water content and myelin water fraction in multiple sclerosis , 2004, Journal of Neurology.

[50]  Kenneth Stephenson,et al.  A circle packing algorithm , 2003, Comput. Geom..