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Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging

Nature Medicine volume 19, pages 1667–1672 (2013)Cite this article

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Abstract

Here, we describe a quantitative neuroimaging method to estimate the macromolecular tissue volume (MTV), a fundamental measure of brain anatomy. By making measurements over a range of field strengths and scan parameters, we tested the key assumptions and the robustness of the method. The measurements confirm that a consistent quantitative estimate of MTV can be obtained across a range of scanners. MTV estimates are sufficiently precise to enable a comparison between data obtained from an individual subject with control population data. We describe two applications. First, we show that MTV estimates can be combined with T1 and diffusion measurements to augment our understanding of the tissue properties. Second, we show that MTV provides a sensitive measure of disease status in individual patients with multiple sclerosis. The MTV maps are obtained using short clinically appropriate scans that can reveal how tissue changes influence behavior and cognition.

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Figure 1: Assessment of MTV fraction accuracy and reliability.
Figure 2: The MTVF and dissimilarity index (DI) in different brain regions.
Figure 3: The mean MTVF values near the midline of three callosal tracts.
Figure 4: Corticospinal tract (CST) measurements in controls and two individuals with multiple sclerosis.
Figure 5: The relationship between T1 and WVF in white matter.

References

  1. Tofts, P. Quantitative MRI of the Brain Measuring Changes Caused by Disease (John Wiley & Sons, Chichester, West Sussex; Hoboken, NJ, 2003).

  2. Laule, C. et al. Magnetic resonance imaging of myelin. Neurotherapeutics 4, 460–484 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Deoni, S.C. Magnetic resonance relaxation and quantitative measurement in the brain. Methods Mol. Biol. 711, 65–108 (2011).

    CAS  PubMed  Google Scholar 

  4. Alexander, A.L. et al. Characterization of cerebral white matter properties using quantitative magnetic resonance imaging atains. Brain Connect. 1, 423–426 (2011).

    PubMed  PubMed Central  Google Scholar 

  5. Fatouros, P.P. & Marmarou, A. Use of magnetic resonance imaging for in vivo measurements of water content in human brain: method and normal values. J. Neurosurg. 90, 109–115 (1999).

    CAS  PubMed  Google Scholar 

  6. Laule, C. et al. Water content and myelin water fraction in multiple sclerosis. A T2 relaxation study. J. Neurol. 251, 284–293 (2004).

    CAS  PubMed  Google Scholar 

  7. Neeb, H., Zilles, K. & Shah, N.J. A new method for fast quantitative mapping of absolute water content in vivo. Neuroimage 31, 1156–1168 (2006).

    CAS  PubMed  Google Scholar 

  8. Ashburner, J. & Friston, K.J. Voxel-based morphometry–the methods. Neuroimage 11, 805–821 (2000).

    CAS  PubMed  Google Scholar 

  9. Fischl, B. & Dale, A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. USA 97, 11050–11055 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Gogtay, N. et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl. Acad. Sci. USA 101, 8174–8179 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Kakeda, S. & Korogi, Y. The efficacy of a voxel-based morphometry on the analysis of imaging in schizophrenia, temporal lobe epilepsy, and Alzheimer's disease/mild cognitive impairment: a review. Neuroradiology 52, 711–721 (2010).

    PubMed  Google Scholar 

  12. Kanai, R. & Rees, G. The structural basis of inter-individual differences in human behaviour and cognition. Nat. Rev. Neurosci. 12, 231–242 (2011).

    CAS  PubMed  Google Scholar 

  13. May, A. Experience-dependent structural plasticity in the adult human brain. Trends Cogn. Sci. 15, 475–482 (2011).

    PubMed  Google Scholar 

  14. Thomas, C. & Baker, C.I. Remodeling human cortex through training: comment on May. Trends Cogn. Sci. 16, 96–97 (2012).

    PubMed  Google Scholar 

  15. Norton, W.T. & Autilio, L.A. The lipid composition of purified bovine brain myelin. J. Neurochem. 13, 213–222 (1966).

    CAS  PubMed  Google Scholar 

  16. Bottomley, P.A., Foster, T.H., Argersinger, R.E. & Pfeifer, L.M. A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1–100 MHz: dependence on tissue type, NMR frequency, temperature, species, excision, and age. Med. Phys. 11, 425–448 (1984).

    CAS  PubMed  Google Scholar 

  17. Mansfield, P. & Morris, P.G. NMR Imaging in Biomedicine (Academic Press, London, 1982).

  18. Rooney, W.D. et al. Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo. Magn. Reson. Med. 57, 308–318 (2007).

    CAS  PubMed  Google Scholar 

  19. Fram, E.K. et al. Rapid calculation of T1 using variable flip angle gradient refocused imaging. Magn. Reson. Imaging 5, 201–208 (1987).

    CAS  PubMed  Google Scholar 

  20. Koenig, S.H. Cholesterol of myelin is the determinant of gray-white contrast in MRI of brain. Magn. Reson. Med. 20, 285–291 (1991).

    CAS  PubMed  Google Scholar 

  21. Kucharczyk, W., Macdonald, P.M., Stanisz, G.J. & Henkelman, R.M. Relaxivity and magnetization transfer of white matter lipids at MR imaging: importance of cerebrosides and pH. Radiology 192, 521–529 (1994).

    CAS  PubMed  Google Scholar 

  22. Aboitiz, F., Scheibel, A.B., Fisher, R.S. & Zaidel, E. Fiber composition of the human corpus callosum. Brain Res. 598, 143–153 (1992).

    CAS  PubMed  Google Scholar 

  23. Barazany, D., Basser, P.J. & Assaf, Y. In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. Brain 132, 1210–1220 (2009).

    PubMed  PubMed Central  Google Scholar 

  24. Stikov, N. et al. Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. Neuroimage 54, 1112–1121 (2011).

    PubMed  Google Scholar 

  25. Alexander, A.L., Lee, J.E., Lazar, M. & Field, A.S. Diffusion tensor imaging of the brain. Neurotherapeutics 4, 316–329 (2007).

    PubMed  PubMed Central  Google Scholar 

  26. Beaulieu, C. The basis of anisotropic water diffusion in the nervous system–a technical review. NMR Biomed. 15, 435–455 (2002).

    PubMed  Google Scholar 

  27. Paus, T. Growth of white matter in the adolescent brain: myelin or axon? Brain Cogn. 72, 26–35 (2010).

    PubMed  Google Scholar 

  28. Yeatman, J.D., Dougherty, R.F., Myall, N.J., Wandell, B.A. & Feldman, H.M. Tract profiles of white matter properties: automating fiber-tract quantification. PLoS ONE 7, e49790 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Wedeen, V.J. et al. Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage 41, 1267–1277 (2008).

    CAS  PubMed  Google Scholar 

  30. Gelman, N., Ewing, J.R., Gorell, J.M., Spickler, E.M. & Solomon, E.G. Interregional variation of longitudinal relaxation rates in human brain at 3.0 T: relation to estimated iron and water contents. Magn. Reson. Med. 45, 71–79 (2001).

    CAS  PubMed  Google Scholar 

  31. Does, M.D. & Gore, J.C. Compartmental study of T(1) and T(2) in rat brain and trigeminal nerve in vivo. Magn. Reson. Med. 47, 274–283 (2002).

    PubMed  Google Scholar 

  32. Filippi, M. & Rocca, M.A. MR imaging of multiple sclerosis. Radiology 259, 659–681 (2011).

    PubMed  Google Scholar 

  33. Lövblad, K.O. et al. MR imaging in multiple sclerosis: review and recommendations for current practice. AJNR Am. J. Neuroradiol. 31, 983–989 (2010).

    PubMed  PubMed Central  Google Scholar 

  34. Poloni, G., Minagar, A., Haacke, E.M. & Zivadinov, R. Recent developments in imaging of multiple sclerosis. Neurologist 17, 185–204 (2011).

    PubMed  Google Scholar 

  35. MacKay, A.L. et al. MR relaxation in multiple sclerosis. Neuroimaging Clin. N. Am. 19, 1–26 (2009).

    CAS  PubMed  Google Scholar 

  36. Popescu, B.F. & Lucchinetti, C.F. Pathology of demyelinating diseases. Annu. Rev. Pathol. 7, 185–217 (2012).

    CAS  PubMed  Google Scholar 

  37. Le Bihan, D. et al. Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13, 534–546 (2001).

    CAS  PubMed  Google Scholar 

  38. Glasser, M.F. & Van Essen, D.C. Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neurosci. 31, 11597–11616 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Noterdaeme, O., Anderson, M., Gleeson, F. & Brady, S.M. Intensity correction with a pair of spoiled gradient recalled echo images. Phys. Med. Biol. 54, 3473–3489 (2009).

    PubMed  Google Scholar 

  40. Volz, S., Noth, U. & Deichmann, R. Correction of systematic errors in quantitative proton density mapping. Magn. Reson. Med. 68, 74–85 (2012).

    PubMed  Google Scholar 

  41. Koenig, B.W. & Gawrisch, K. Specific volumes of unsaturated phosphatidylcholines in the liquid crystalline lamellar phase. Biochim. Biophys. Acta 1715, 65–70 (2005).

    CAS  PubMed  Google Scholar 

  42. Loosley-Millman, M.E., Rand, R.P. & Parsegian, V.A. Effects of monovalent ion binding and screening on measured electrostatic forces between charged phospholipid bilayers. Biophys. J. 40, 221–232 (1982).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Ulrich, A.S. & Watts, A. Molecular response of the lipid headgroup to bilayer hydration monitored by 2H-NMR. Biophys. J. 66, 1441–1449 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Nagle, J.F. Theory of the main lipid bilayer phase transition. Annu. Rev. Phys. Chem. 31, 157–196 (1980).

    CAS  Google Scholar 

  45. Polman, C.H. et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann. Neurol. 58, 840–846 (2005).

    PubMed  Google Scholar 

  46. Kurtzke, J.F. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33, 1444–1452 (1983).

    CAS  PubMed  Google Scholar 

  47. Barral, J.K. et al. A robust methodology for in vivo T1 mapping. Magn. Reson. Med. 64, 1057–1067 (2010).

    PubMed  PubMed Central  Google Scholar 

  48. Chang, L.C., Koay, C.G., Basser, P.J. & Pierpaoli, C. Linear least-squares method for unbiased estimation of T1 from SPGR signals. Magn. Reson. Med. 60, 496–501 (2008).

    PubMed  PubMed Central  Google Scholar 

  49. Dale, A.M., Fischl, B. & Sereno, M.I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).

    CAS  PubMed  Google Scholar 

  50. Hopkins, A.L., Yeung, H.N. & Bratton, C.B. Multiple field strength in vivo T1 and T2 for cerebrospinal fluid protons. Magn. Reson. Med. 3, 303–311 (1986).

    CAS  PubMed  Google Scholar 

  51. Sigalovsky, I.S., Fischl, B. & Melcher, J.R. Mapping an intrinsic MR property of gray matter in auditory cortex of living humans: a possible marker for primary cortex and hemispheric differences. Neuroimage 32, 1524–1537 (2006).

    PubMed  Google Scholar 

  52. Yarnykh, V.L. & Yuan, C. Cross-relaxation imaging reveals detailed anatomy of white matter fiber tracts in the human brain. Neuroimage 23, 409–424 (2004).

    PubMed  Google Scholar 

  53. Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J. & Aldroubi, A. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632 (2000).

    CAS  PubMed  Google Scholar 

  54. Mori, S., Crain, B.J., Chacko, V.P. & van Zijl, P.C. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269 (1999).

    CAS  PubMed  Google Scholar 

  55. Dougherty, R.F., Ben-Shachar, M., Bammer, R., Brewer, A.A. & Wandell, B.A. Functional organization of human occipital-callosal fiber tracts. Proc. Natl. Acad. Sci. USA 102, 7350–7355 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Wakana, S., Jiang, H., Nagae-Poetscher, L.M., van Zijl, P.C. & Mori, S. Fiber tract-based atlas of human white matter anatomy. Radiology 230, 77–87 (2004).

    PubMed  Google Scholar 

  57. Corouge, I., Gouttard, S. & Gerig, G. A statistical shape model of individual fiber tracts extracted from diffusion tensor MRI. Lect. Notes Comput. Sci. 3217, 671–679 (2004).

    Google Scholar 

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Acknowledgements

We acknowledge J. Barral, M. Gutman, H. Horiguchi, I. Levesque, A. Sherbondy and A. Takahashi for helpful advice and feedback. We thank S. Phipps, I. Levesque and A. Kerr for help in data analysis and acquisition. This work was supported by the US National Institutes of Health research grants RO1-EY15000 and NSF grant BCS-1228397. A.M. is the recipient of support from the Human Frontier Science Program and a Jewish Community Federation Program Machiah Foundation Fellowship. N.-J.C. is the recipient of support from the Singapore National Research Foundation (NRF-NRFF2011-01).

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Authors and Affiliations

  1. Department of Psychology, Stanford University, Stanford, California, USA

    Aviv Mezer, Jason D Yeatman, Kendrick N Kay, Michael L Perry & Brian A Wandell

  2. Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

    Nikola Stikov

  3. Department of Chemical Engineering, Stanford University, Stanford, California, USA

    Nam-Joon Cho

  4. School of Materials Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Nam-Joon Cho

  5. Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, California, USA

    Robert F Dougherty & Brian A Wandell

  6. Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA

    Josef Parvizi & Le H Hua

  7. Department of Radiology, Stanford University, Stanford, California, USA

    Kim Butts-Pauly

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Contributions

A.M. and B.A.W. developed the method, wrote the manuscript and prepared the figures. J.D.Y., N.S., M.L.P., R.F.D., K.B.-P. and A.M. obtained the data. N.-J.C. provided the lipid phantoms. N.S., R.F.D., K.N.K. and A.M. developed analysis tools. J.P. and L.H.H. diagnosed the patient with multiple sclerosis and enabled those scans. A.M. and J.D.Y. developed the diffusion methods and applications. B.A.W. provided equipment and administered the experiment. All authors reviewed the manuscript.

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Correspondence to Aviv Mezer.

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Competing interests

Stanford University has filed a US patent application describing the technology used to measure PD, T1, MTV, VIP and SIR in this study (A.M., R.F.D. and B.A.W.).

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Supplementary Table 1, Supplementary Figures 1–12, Supplementary Discussion and Supplementary Data. (PDF 3092 kb)

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Mezer, A., Yeatman, J., Stikov, N. et al. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat Med 19, 1667–1672 (2013). https://doi.org/10.1038/nm.3390

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