Progress in magnetic resonance imaging (MRI) now makes it possible to identify the major white matter tracts in the living human brain. These tracts are important because they carry many of the signals communicated between different brain regions. MRI methods coupled with biophysical modeling can measure the tissue properties and structural features of the tracts that impact our ability to think, feel, and perceive. This review describes the fundamental ideas of the MRI methods used to identify the major white matter tracts in the living human brain.


Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. Accolla EA, Dukart J, Helms G, Weiskopf N, Kherif F. et al. 2014. Brain tissue properties differentiate between motor and limbic basal ganglia circuits. Hum. Brain Mapp. 35:5083–92 [Google Scholar]
  2. Aganj I, Lenglet C, Jahanshad N, Yacoub E, Harel N. et al. 2011. A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography. Med. Image Anal. 15:414–25 [Google Scholar]
  3. Alexander DC, Barker GJ, Arridge SR. 2002. Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med. 48:331–40 [Google Scholar]
  4. Allen JS, Damasio H, Grabowski TJ, Bruss J, Zhang W. 2003. Sexual dimorphism and asymmetries in the gray–white composition of the human cerebrum. NeuroImage 18:880–94 [Google Scholar]
  5. Ameis SH, Catani M. 2015. Altered white matter connectivity as a neural substrate for social impairment in Autism Spectrum Disorder. Cortex 62:158–81 [Google Scholar]
  6. Amunts K, Lepage C, Borgeat L, Mohlberg H, Dickscheid T. et al. 2013. BigBrain: an ultrahigh-resolution 3D human brain model. Science 340:1472–75 [Google Scholar]
  7. Anderson PW. 1972. More is different. Science 177:393–96 [Google Scholar]
  8. Assaf Y, Basser PJ. 2005. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. NeuroImage 27:48–58 [Google Scholar]
  9. Assaf Y, Blumenfeld-Katzir T, Yovel Y, Basser PJ. 2008. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn. Reson. Med. 59:1347–54 [Google Scholar]
  10. Axer M, Grässel D, Kleiner M, Dammers J, Dickscheid T. et al. 2011. High-resolution fiber tract reconstruction in the human brain by means of three-dimensional polarized light imaging. Front. Neuroinformatics 5:34 [Google Scholar]
  11. Azadbakht H, Parkes LM, Haroon HA, Augath M, Logothetis NK. et al. 2015. Validation of high-resolution tractography against in vivo tracing in the macaque visual cortex. Cereb. Cortex 254299–309
  12. Barazany D, Basser PJ, Assaf Y. 2009. In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. Brain 132:1210–20 [Google Scholar]
  13. Bartzokis G, Lu PH, Heydari P, Couvrette A, Lee GJ. et al. 2012. Multimodal magnetic resonance imaging assessment of white matter aging trajectories over the lifespan of healthy individuals. Biol. Psychiatry 72:1026–34 [Google Scholar]
  14. Basser PJ, Jones DK. 2002. Diffusion-tensor MRI: theory, experimental design and data analysis – a technical review. NMR Biomed. 15:456–67 [Google Scholar]
  15. Bassett DS, Brown JA, Deshpande V, Carlson JM, Grafton ST. 2011. Conserved and variable architecture of human white matter connectivity. NeuroImage 54:1262–79 [Google Scholar]
  16. Bastiani M, Shah NJ, Goebel R, Roebroeck A. 2012. Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm. NeuroImage 62:1732–49 [Google Scholar]
  17. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW. 2007. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?. NeuroImage 34:144–55 [Google Scholar]
  18. Behrens TE, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG. et al. 2003. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50:1077–88 [Google Scholar]
  19. Ben-Shachar M, Dougherty RF, Wandell BA. 2007. White matter pathways in reading. Curr. Opin. Neurobiol. 17:258–70 [Google Scholar]
  20. Buckheit JB, Donoho DL. 1995. WaveLab and reproducible research. Wavelets and Statistics A Antoniadis, G Oppenheim 55–81 New York: Springer-Verlag [Google Scholar]
  21. Bullock TH, Bennett MV, Johnston D, Josephson R, Marder E, Fields RD. 2005. The neuron doctrine, redux. Science 310:791–93 [Google Scholar]
  22. Calabresea E, Badea A, Coe CL, Lubach GR, Stynerd MA. et al. 2015. A diffusion tensor MRI atlas of the postmortem rhesus macaque brain. NeuroImage 117:408–16 [Google Scholar]
  23. Calford MB, Chino YM, Das A, Eysel UT, Gilbert CD. et al. 2005. Neuroscience: rewiring the adult brain. Nature 438:E3 [Google Scholar]
  24. Campbell JS, Pike GB. 2014. Potential and limitations of diffusion MRI tractography for the study of language. Brain Lang. 131:65–73 [Google Scholar]
  25. Caspers S, Axer M, Caspers J, Jockwitz C, Jütten K. et al. 2015. Target sites for transcallosal fibers in human visual cortex—a combined diffusion and polarized light imaging study. Cortex 72:40–53 [Google Scholar]
  26. Catani M, Thiebaut de Schotten M. 2008. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 44:1105–32 [Google Scholar]
  27. Catani M, Thiebaut de Schotten M, Slater D, Dell'Acqua F. 2013. Connectomic approaches before the connectome. NeuroImage 80:2–13 [Google Scholar]
  28. Chan AW, Song F, Vickers A, Jefferson T, Dickersin K. et al. 2014. Increasing value and reducing waste: addressing inaccessible research. Lancet 383:257–66 [Google Scholar]
  29. Civier O, Kronfeld-Duenias V, Amir O, Ezrati-Vinacour R, Ben-Shachar M. 2015. Reduced fractional anisotropy in the anterior corpus callosum is associated with reduced speech fluency in persistent developmental stuttering. Brain Lang. 143:20–31 [Google Scholar]
  30. Conturo TE, Lori NF, Cull TS, Akbudak E, Snyder AZ. et al. 1999. Tracking neuronal fiber pathways in the living human brain. PNAS 96:10422–27 [Google Scholar]
  31. Cook PA, Bai Y, Nedjati-Gilani S, Seunarine KK, Hall MG. et al. 2006. Camino: open-source diffusion–MRI reconstruction and processing. Presented at Sci. Meet. Int. Soc. Magn. Reson. Med., 14th, Seattle, WA
  32. Daducci A, Dal Palu A, Lemkaddem A, Thiran JP. 2015. COMMIT: convex optimization modeling for microstructure informed tractography. IEEE Trans. Med. Imaging 34:246–57 [Google Scholar]
  33. Damasio A, Carvalho GB. 2013. The nature of feelings: evolutionary and neurobiological origins. Nat. Rev. Neurosci. 14:143–52 [Google Scholar]
  34. Dauguet J, Delzescaux T, Conde F, Mangin JF, Ayache N. et al. 2007a. Three-dimensional reconstruction of stained histological slices and 3D non-linear registration with in-vivo MRI for whole baboon brain. J. Neurosci. Methods 164:191–204 [Google Scholar]
  35. Dauguet J, Peled S, Berezovskii V, Delzescaux T, Warfield SK. et al. 2007b. Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain. NeuroImage 37:530–38 [Google Scholar]
  36. De Santis S, Assaf Y, Evans CJ, Jones DK. 2014. Improved precision in CHARMED assessment of white matter through sampling scheme optimization and model parsimony testing. Magn. Reson. Med. 71:661–71 [Google Scholar]
  37. De Santis S, Assaf Y, Jones DK. 2012. Using the biophysical CHARMED model to elucidate the underpinnings of contrast in diffusional kurtosis analysis of diffusion-weighted MRI. MAGMA 25:267–76 [Google Scholar]
  38. De Santis S, Barazany D, Jones DK, Assaf Y. 2016. Resolving relaxometry and diffusion properties within the same voxel in the presence of crossing fibres by combining inversion recovery and diffusion-weighted acquisitions. Magn. Reson. Med. 75:372–80 [Google Scholar]
  39. Dell'Acqua F, Simmons A, Williams SCR, Catani M. 2013. Can spherical deconvolution provide more information than fiber orientations? Hindrance modulated orientational anisotropy, a true-tract specific index to characterize white matter diffusion. Hum. Brain Mapp. 34:2464–83 [Google Scholar]
  40. Donoho DL. 2010. An invitation to reproducible computational research. Biostatistics 11:385–88 [Google Scholar]
  41. Feinberg DA, Setsompop K. 2013. Ultra-fast MRI of the human brain with simultaneous multi-slice imaging. J. Magn. Reson. 229:90–100 [Google Scholar]
  42. Ferizi U, Schneider T, Witzel T, Wald LL, Zhang H. et al. 2015. White matter compartment models for in vivo diffusion MRI at 300 mT/m. NeuroImage 118:468–83 [Google Scholar]
  43. Fields RD. 2010. Change in the brain's white matter: the role of the brain's white matter in active learning and memory may be underestimated. Science 330:768–69 [Google Scholar]
  44. Fields RD, Araque A, Johansen-Berg H, Lim SS, Lynch G. et al. 2014. Glial biology in learning and cognition. Neuroscientist 20:426–31 [Google Scholar]
  45. Fillard P, Descoteaux M, Goh A, Gouttard S, Jeurissen B. et al. 2011. Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. NeuroImage 56:220–34 [Google Scholar]
  46. Fillard P, Poupon C, Mangin JF. 2009. A novel global tractography algorithm based on an adaptive spin glass model. Med. Image Comput. Comput. Assist. Interv. 12:927–34 [Google Scholar]
  47. Filler A. 2009. Magnetic resonance neurography and diffusion tensor imaging: origins, history, and clinical impact of the first 50,000 cases with an assessment of efficacy and utility in a prospective 5000-patient study group. Neurosurgery 65:Suppl. 4A29–43 [Google Scholar]
  48. Fitzsimmons J, Kubicki M, Shenton ME. 2013. Review of functional and anatomical brain connectivity findings in schizophrenia. Curr. Opin. Psychiatry 26:172–87 [Google Scholar]
  49. FMRIB Softw. Libr. 2015. FDT UserGuide. Oxford, UK: Univ. Oxford. http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/UserGuide
  50. Foxley S, Jbabdi S, Clare S, Lam W, Ansorge O. et al. 2014. Improving diffusion-weighted imaging of post-mortem human brains: SSFP at 7 T. NeuroImage 102:579–89 [Google Scholar]
  51. Frank LR. 2002. Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magn. Reson. Med. 47:1083–99 [Google Scholar]
  52. Friman O, Westin CF. 2005. Uncertainty in white matter fiber tractography. Med. Image Comput. Comput. Assist. Interv. 8:107–14 [Google Scholar]
  53. Fritzsche KH, Neher PF, Reicht I, van Bruggen T, Goch C. et al. 2012. MITK diffusion imaging. Methods Inf. Med. 51:441–48 [Google Scholar]
  54. Gabrieli JD. 2009. Dyslexia: a new synergy between education and cognitive neuroscience. Science 325:280–83 [Google Scholar]
  55. Garyfallidis E, Brett M, Amirbekian B, Rokem A, Van Der Walt S. et al. 2014. Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinformatics 8:8 [Google Scholar]
  56. Geschwind N. 1965a. Disconnexion syndromes in animals and man. I. Brain 88:237–94 [Google Scholar]
  57. Geschwind N. 1965b. Disconnexion syndromes in animals and man. II. Brain 88:585–644 [Google Scholar]
  58. Gibson EM, Purger D, Mount CW, Goldstein AK, Lin GL. et al. 2014. Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain. Science 344:1252304 [Google Scholar]
  59. Gould E. 2007. How widespread is adult neurogenesis in mammals?. Nat. Rev. Neurosci. 8:481–88 [Google Scholar]
  60. Gould E, Reeves AJ, Graziano MS, Gross CG. 1999. Neurogenesis in the neocortex of adult primates. Science 286:548–52 [Google Scholar]
  61. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ. et al. 2008. Mapping the structural core of human cerebral cortex. PLOS Biol. 6:e159 [Google Scholar]
  62. Hanisch UK. 2002. Microglia as a source and target of cytokines. Glia 40:140–55 [Google Scholar]
  63. Hermoye L, Wakana S, Laurent J-P, Jiang H, Cosnard G. et al. 2004. White matter atlas UCL–JHU. http://www.dtiatlas.org/
  64. Hoeft F, McCandliss BD, Black JM, Gantman A, Zakerani N. et al. 2011. Neural systems predicting long-term outcome in dyslexia. PNAS 108:361–66 [Google Scholar]
  65. Huang SY, Nummenmaa A, Witzel T, Duval T, Cohen-Adad J. et al. 2015. The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter. NeuroImage 106:464–72 [Google Scholar]
  66. Hughes EG, Kang SH, Fukaya M, Bergles DE. 2013. Oligodendrocyte progenitors balance growth with self-repulsion to achieve homeostasis in the adult brain. Nat. Neurosci. 16:668–76 [Google Scholar]
  67. Hum. Connect. Proj. 2014. 500 Subjects + MEG2 Reference Manual – Appendix I – Protocol Guidance and HCP Session Protocols. WU-Minn Consort. NIH Hum. Connect. Proj., Nov. 25. http://www.humanconnectome.org/documentation/S500/HCP_S500+MEG2_Release_Appendix_I.pdf
  68. Iturria-Medina Y, Canales-Rodriguez EJ, Melie-Garcia L, Valdes-Hernandez PA, Martinez-Montes E. et al. 2007. Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. NeuroImage 36:645–60 [Google Scholar]
  69. Jbabdi S, Johansen-Berg H. 2011. Tractography: Where do we go from here?. Brain Connect. 1:169–83 [Google Scholar]
  70. Jbabdi S, Sotiropoulos SN, Haber SN, Van Essen DC, Behrens TE. 2015. Measuring macroscopic brain connections in vivo. Nat. Neurosci. 18:1546–55 [Google Scholar]
  71. Jbabdi S, Woolrich MW, Andersson JL, Behrens TE. 2007. A Bayesian framework for global tractography. NeuroImage 37:116–29 [Google Scholar]
  72. Jeurissen B, Leemans A, Tournier JD, Jones DK, Sijbers J. 2013. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum. Brain Mapp. 34:2747–66 [Google Scholar]
  73. Johansen-Berg H, Baptista CS, Thomas AG. 2012. Human structural plasticity at record speed. Neuron 73:1058–60 [Google Scholar]
  74. Jones DK, Cercignani M. 2010. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed. 23:803–20 [Google Scholar]
  75. Jones DK, Knosche TR, Turner R. 2013. White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. NeuroImage 73:239–54 [Google Scholar]
  76. Koch C, Reid RC. 2012. Neuroscience: observatories of the mind. Nature 483:397–98 [Google Scholar]
  77. Kreher BW, Mader I, Kiselev VG. 2008. Gibbs tracking: a novel approach for the reconstruction of neuronal pathways. Magn. Reson. Med. 60:953–63 [Google Scholar]
  78. Kubicki M, Shenton ME. 2014. Diffusion tensor imaging findings and their implications in schizophrenia. Curr. Opin. Psychiatry 27:179–84 [Google Scholar]
  79. Lawes IN, Barrick TR, Murugam V, Spierings N, Evans DR. et al. 2008. Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection. NeuroImage 39:62–79 [Google Scholar]
  80. Lazar M, Weinstein DM, Tsuruda JS, Hasan KM, Arfanakis K. et al. 2003. White matter tractography using diffusion tensor deflection. Hum. Brain Mapp. 18:306–21 [Google Scholar]
  81. Le Bihan D, Johansen-Berg H. 2012. Diffusion MRI at 25: exploring brain tissue structure and function. NeuroImage 61:324–41 [Google Scholar]
  82. Lebel C, Benner T, Beaulieu C. 2012a. Six is enough? Comparison of diffusion parameters measured using six or more diffusion-encoding gradient directions with deterministic tractography. Magn. Reson. Med. 68:474–83 [Google Scholar]
  83. Lebel C, Gee M, Camicioli R, Wieler M, Martin W, Beaulieu C. 2012b. Diffusion tensor imaging of white matter tract evolution over the lifespan. NeuroImage 60:340–52 [Google Scholar]
  84. Lebel C, Walker L, Leemans A, Phillips L, Beaulieu C. 2008. Microstructural maturation of the human brain from childhood to adulthood. NeuroImage 40:1044–55 [Google Scholar]
  85. Lemkaddem A, Skioldebrand D, Dal Palu A, Thiran JP, Daducci A. 2014. Global tractography with embedded anatomical priors for quantitative connectivity analysis. Front. Neurol. 5:232 [Google Scholar]
  86. Leong JK, Pestilli F, Wu CC, Samanez-Larkin GR, Knutson B. 2016. White-matter tract connecting anterior insula to nucleus accumbens correlates with reduced preference for positively skewed gambles. Neuron 89:63–69 [Google Scholar]
  87. Leuze CW, Anwander A, Bazin PL, Dhital B, Stuber C. et al. 2014. Layer-specific intracortical connectivity revealed with diffusion MRI. Cereb. Cortex 24:328–39 [Google Scholar]
  88. Logothetis NK. 2008. What we can do and what we cannot do with fMRI. Nature 453:869–78 [Google Scholar]
  89. Logothetis NK, Wandell BA. 2004. Interpreting the BOLD signal. Annu. Rev. Physiol. 66:735–69 [Google Scholar]
  90. Long P, Corfas G. 2014. Dynamic regulation of myelination in health and disease. JAMA Psychiatry 71:1296–97 [Google Scholar]
  91. Lutti A, Dick F, Sereno MI, Weiskopf N. 2014. Using high-resolution quantitative mapping of R1 as an index of cortical myelination. NeuroImage 93:176–88 [Google Scholar]
  92. Magnain C, Augustinack JC, Konukoglu E, Boas D, Fischl B. 2015. Visualization of the cytoarchitecture of ex vivo human brain by optical coherence tomography. Opt. Life Sci., Optics and the Brain 2015, Vancouver, Can. pap. BrT4B.5. Washington, DC: Opt. Soc. Am. [Google Scholar]
  93. Mangin JF, Fillard P, Cointepas Y, Le Bihan D, Frouin V, Poupon C. 2013. Toward global tractography. NeuroImage 80:290–96 [Google Scholar]
  94. Mangin JF, Poupon C, Cointepas Y, Riviere D, Papadopoulos-Orfanos D. et al. 2002. A framework based on spin glass models for the inference of anatomical connectivity from diffusion-weighted MR data—a technical review. NMR Biomed. 15:481–92 [Google Scholar]
  95. Marcus DS, Harms MP, Snyder AZ, Jenkinson M, Wilson JA. et al. 2013. Human Connectome Project informatics: quality control, database services, and data visualization. NeuroImage 80:202–19 [Google Scholar]
  96. Marcus DS, Olsen TR, Ramaratnam M, Buckner RL. 2007. The extensible neuroimaging archive toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics 5:11–34 [Google Scholar]
  97. McKenzie IA, Ohayon D, Li H, de Faria JP, Emery B. et al. 2014. Motor skill learning requires active central myelination. Science 346:318–22 [Google Scholar]
  98. Mezer AA, Rokem A, Hastie T, Wandell B. 2016. Proton density mapping: ways to remove the receive inhomogeneity. Hum. Brain Mapp. In press
  99. Mezer AA, Stikov NA, Kay KN, Dougherty RF, Wandell BA. 2010. A new quantitative MRI contrast for measuring white matter myelin. Presented at Soc. Neurosci., 40th, San Diego
  100. Mezer AA, Yeatman JD, Stikov N, Kay KN, Cho NJ. et al. 2013. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat. Med. 19:1667–72 [Google Scholar]
  101. Miller KL, Stagg CJ, Douaud G, Jbabdi S, Smith SM. et al. 2011. Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner. NeuroImage 57:167–81 [Google Scholar]
  102. Mori S, Crain BJ, Chacko VP, van Zijl PC. 1999. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45:265–69 [Google Scholar]
  103. Mori S, van Zijl PC. 2002. Fiber tracking: principles and strategies—a technical review. NMR Biomed. 15:468–80 [Google Scholar]
  104. Neher PF, Laun FB, Stieltjes B, Maier-Hein KH. 2014. Fiberfox: facilitating the creation of realistic white matter software phantoms. Magn. Reson. Med. 72:1460–70 [Google Scholar]
  105. Nestler EJ, Hyman SE. 2010. Animal models of neuropsychiatric disorders. Nat. Neurosci. 13:1161–69 [Google Scholar]
  106. Nordahl CW, Iosif AM, Young GS, Perry LM, Dougherty R. et al. 2015. Sex differences in the corpus callosum in preschool-aged children with autism spectrum disorder. Mol. Autism 6:26 [Google Scholar]
  107. Ogawa S, Takemura H, Horiguchi H, Terao M, Haji T. et al. 2014. White matter consequences of retinal receptor and ganglion cell damage. Investig. Ophthalmol. Vis. Sci. 55:6976–86 [Google Scholar]
  108. Oishi K, Zilles K, Amunts K, Faria A, Jiang H. et al. 2008. Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. NeuroImage 43:447–57 [Google Scholar]
  109. Parizel PM, Van Rompaey V, Van Loock R, Van Hecke W, Van Goethem JW. et al. 2007. Influence of user-defined parameters on diffusion tensor tractography of the corticospinal tract. Neuroradiol. J 20:139–47 [Google Scholar]
  110. Parker GD, Marshall D, Rosin PL, Drage N, Richmond S, Jones DK. 2013. A pitfall in the reconstruction of fibre ODFs using spherical deconvolution of diffusion MRI data. NeuroImage 65:433–48 [Google Scholar]
  111. Pestilli F, Yeatman JD, Rokem A, Kay KN, Wandell BA. 2014. Evaluation and statistical inference for human connectomes. Nat. Methods 11:1058–63 [Google Scholar]
  112. Purger D, Gibson EM, Monje M. 2015. Myelin plasticity in the central nervous system. Neuropharmacology In press. doi: 10.1016/j.neuropharm.2015.08.001
  113. Rakic P. 2002. Neurogenesis in adult primate neocortex: an evaluation of the evidence. Nat. Rev. Neurosci. 3:65–71 [Google Scholar]
  114. Reisert M, Mader I, Anastasopoulos C, Weigel M, Schnell S, Kiselev V. 2011. Global fiber reconstruction becomes practical. NeuroImage 54:955–62 [Google Scholar]
  115. Reutskiy S, Rossoni E, Tirozzi B. 2003. Conduction in bundles of demyelinated nerve fibers: computer simulation. Biol. Cybern. 89:439–48 [Google Scholar]
  116. Rokem A, Yeatman JD, Pestilli F, Kay KN, Mezer A. et al. 2015. Evaluating the accuracy of diffusion MRI models in white matter. PLOS ONE 10:e0123272 [Google Scholar]
  117. Rushton WA. 1951. A theory of the effects of fibre size in medullated nerve. J. Physiol. 115:101–22 [Google Scholar]
  118. Sagi Y, Tavor I, Hofstetter S, Tzur-Moryosef S, Blumenfeld-Katzir T, Assaf Y. 2012. Learning in the fast lane: new insights into neuroplasticity. Neuron 73:1195–203 [Google Scholar]
  119. Samanez-Larkin GR, Levens SM, Perry LM, Dougherty RF, Knutson B. 2012. Frontostriatal white matter integrity mediates adult age differences in probabilistic reward learning. J. Neurosci. 32:5333–37 [Google Scholar]
  120. Sasson E, Doniger GM, Pasternak O, Tarrasch R, Assaf Y. 2013. White matter correlates of cognitive domains in normal aging with diffusion tensor imaging. Front. Neurosci. 7:32 [Google Scholar]
  121. Schnieder TP, Trencevska I, Rosoklija G, Stankov A, Mann JJ. et al. 2014. Microglia of prefrontal white matter in suicide. J. Neuropathol. Exp. Neurol. 73:880–90 [Google Scholar]
  122. Schreiber J, Riffert T, Anwander A, Knosche TR. 2014. Plausibility tracking: a method to evaluate anatomical connectivity and microstructural properties along fiber pathways. NeuroImage 90:163–78 [Google Scholar]
  123. Schüz A, Braitenberg V. 2002. The human cortical white matter: quantitative aspects of cortico-cortical long-range connectivity. Cortical Areas: Unity and Diversity A Schüz, R Miller 377–85 New York: Taylor and Francis [Google Scholar]
  124. Scott A, Courtney W, Wood D, de la Garza R, Lane S. et al. 2011. COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Front. Neuroinformatics 5:33 [Google Scholar]
  125. Seehaus A, Roebroeck A, Bastiani M, Fonseca L, Bratzke H. et al. 2015. Histological validation of high-resolution DTI in human post mortem tissue. Front. Neuroanat. 9:98 [Google Scholar]
  126. Seok J, Warren HS, Cuenca AG, Mindrinos MN, Baker HV. et al. 2013. Genomic responses in mouse models poorly mimic human inflammatory diseases. PNAS 110:3507–12 [Google Scholar]
  127. Sherbondy AJ, Dougherty RF, Ananthanarayanan R, Modha DS, Wandell BA. 2009. Think global, act local; projectome estimation with BlueMatter. Med. Image Comput. Comput. Assist. Interv. 12:861–68 [Google Scholar]
  128. Sherbondy AJ, Dougherty RF, Ben-Shachar M, Napel S, Wandell BA. 2008a. ConTrack: finding the most likely pathways between brain regions using diffusion tractography. J. Vis. 8:151–16 [Google Scholar]
  129. Sherbondy AJ, Dougherty RF, Napel S, Wandell BA. 2008b. Identifying the human optic radiation using diffusion imaging and fiber tractography. J. Vis. 8:121–11 [Google Scholar]
  130. Sherbondy AJ, Rowe MC, Alexander DC. 2010. MicroTrack: an algorithm for concurrent projectome and microstructure estimation. Med. Image Comput. Comput. Assist. Interv. 13:183–90 [Google Scholar]
  131. Smith AM, Dragunow M. 2014. The human side of microglia. Trends Neurosci. 37:125–35 [Google Scholar]
  132. Smith RE, Tournier JD, Calamante F, Connelly A. 2013. SIFT: spherical-deconvolution informed filtering of tractograms. NeuroImage 67:298–312 [Google Scholar]
  133. Smith RE, Tournier JD, Calamante F, Connelly A. 2015a. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. NeuroImage 104:253–65 [Google Scholar]
  134. Smith RE, Tournier JD, Calamante F, Connelly A. 2015b. SIFT2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage 119:338–51 [Google Scholar]
  135. Sotiropoulos SN, Jbabdi S, Xu J, Andersson JL, Moeller S. et al. 2013. Advances in diffusion MRI acquisition and processing in the Human Connectome Project. NeuroImage 80:125–43 [Google Scholar]
  136. Sporns O. 2011. Networks of the Brain Cambridge, MA: MIT Press
  137. Sporns O, Tononi G, Kotter R. 2005. The human connectome: a structural description of the human brain. PLOS Comput. Biol. 1:e42 [Google Scholar]
  138. Stanisz GJ, Szafer A, Wright GA, Henkelman RM. 1997. An analytical model of restricted diffusion in bovine optic nerve. Magn. Reson. Med. 37:103–11 [Google Scholar]
  139. Stejskal EO, Tanner JE. 1965. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J. Chem. Phys. 42:288–92 [Google Scholar]
  140. Stikov N, Perry LM, Mezer A, Rykhlevskaia E, Wandell BA. et al. 2011. Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. NeuroImage 54:1112–21 [Google Scholar]
  141. Stone J. 1973. Sampling properties of microelectrodes assessed in the cat's retina. J. Neurophysiol. 36:1071–79 [Google Scholar]
  142. Stuber C, Morawski M, Schafer A, Labadie C, Wahnert M. et al. 2014. Myelin and iron concentration in the human brain: a quantitative study of MRI contrast. NeuroImage 93:95–106 [Google Scholar]
  143. Takao K, Miyakawa T. 2015. Genomic responses in mouse models greatly mimic human inflammatory diseases. PNAS 112:1167–72 [Google Scholar]
  144. Takemura H, Caiafa CF, Wandell B, Pestilli F. 2016a. Ensemble tractography. PLOS Comput. Biol. 12:e1004692 [Google Scholar]
  145. Takemura H, Rokem A, Winawer J, Yeatman JD, Wandell BA, Pestilli F. 2016b. A major human white matter pathway between dorsal and ventral visual cortex. Cereb. Cortex 262205–14
  146. Tavor I, Hofstetter S, Assaf Y. 2013. Micro-structural assessment of short term plasticity dynamics. NeuroImage 81:1–7 [Google Scholar]
  147. Thomas C, Ye FQ, Irfanoglu MO, Modi P, Saleem KS. et al. 2014. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. PNAS 111:16574–79 [Google Scholar]
  148. Tournier JD, Calamante F, Connelly A. 2007. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35:1459–72 [Google Scholar]
  149. Tournier JD, Calamante F, Connelly A. 2012. MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22:53–66 [Google Scholar]
  150. Tournier JD, Calamante F, Connelly A. 2013. Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed. 26:1775–86 [Google Scholar]
  151. Tournier JD, Calamante F, Gadian DG, Connelly A. 2004. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23:1176–85 [Google Scholar]
  152. Tournier JD, Yeh CH, Calamante F, Cho KH, Connelly A, Lin CP. 2008. Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data. NeuroImage 42:617–25 [Google Scholar]
  153. Tsang JM, Dougherty RF, Deutsch GK, Wandell BA, Ben-Shachar M. 2009. Frontoparietal white matter diffusion properties predict mental arithmetic skills in children. PNAS 106:22546–51 [Google Scholar]
  154. Tuch DS. 2004. Q-ball imaging. Magn. Reson. Med. 52:1358–72 [Google Scholar]
  155. Tuch DS, Reese TG, Wiegell MR, Wedeen VJ. 2003. Diffusion MRI of complex neural architecture. Neuron 40:885–95 [Google Scholar]
  156. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TE. et al. 2012. The Human Connectome Project: a data acquisition perspective. NeuroImage 62:2222–31 [Google Scholar]
  157. Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PC, Mori S. 2004. Fiber tract-based atlas of human white matter anatomy. Radiology 230:77–87 [Google Scholar]
  158. Wandell BA, Rauschecker AM, Yeatman JD. 2012. Learning to see words. Annu. Rev. Psychol. 63:31–53 [Google Scholar]
  159. Wandell BA, Rokem A, Perry LM, Schaefer G, Dougherty RF. 2015. Data management to support reproducible research. arXiv:1502.06900 [q-bio.QM]
  160. Wandell BA, Winawer J. 2015. Computational neuroimaging and population receptive fields. Trends Cogn. Sci. 19:349–57 [Google Scholar]
  161. Wandell BA, Yeatman JD. 2013. Biological development of reading circuits. Curr. Opin. Neurobiol. 23:261–68 [Google Scholar]
  162. Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM. 2005. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 54:1377–86 [Google Scholar]
  163. Wedeen VJ, Reese T, Tuch D, Weigel M, Dou J. et al. 2000. Mapping fiber orientation spectra in cerebral white matter with Fourier-transform diffusion MR. Presented at Proc. Int. Soc. Mag. Res. Med., Denver, CO
  164. Weiskopf N, Mohammadi S, Lutti A, Callaghan MF. 2015. Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology. Curr. Opin. Neurol. 28:313–22 [Google Scholar]
  165. Wernicke C. 1874 (1977). Der aphasischer Symptomenkomplex: Eine psychologische Studie auf anatomischer Basis. Wernicke's Works on Aphasia: A Sourcebook and Review transl. GH Eggert 91–145 The Hague: Mouton
  166. Yeatman JD, Dougherty RF, Ben-Shachar M, Wandell BA. 2012a. Development of white matter and reading skills. PNAS 109:E3045–53 [Google Scholar]
  167. Yeatman JD, Dougherty RF, Myall NJ, Wandell BA, Feldman HM. 2012b. Tract profiles of white matter properties: automating fiber-tract quantification. PLOS ONE 7:e49790 [Google Scholar]
  168. Yeatman JD, Dougherty RF, Rykhlevskaia E, Sherbondy AJ, Deutsch GK. et al. 2011. Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children. J. Cogn. Neurosci. 23:3304–17 [Google Scholar]
  169. Yeatman JD, Wandell BA, Mezer AA. 2014a. Lifespan maturation and degeneration of human brain white matter. Nat. Commun. 5:4932 [Google Scholar]
  170. Yeatman JD, Weiner KS, Pestilli F, Rokem A, Mezer A, Wandell BA. 2014b. The vertical occipital fasciculus: a century of controversy resolved by in vivo measurements. PNAS 111:E5214–23 [Google Scholar]
  171. Yeh FC, Wedeen VJ, Tseng WY. 2011. Estimation of fiber orientation and spin density distribution by diffusion deconvolution. NeuroImage 55:1054–62 [Google Scholar]
  172. Yendiki A, Panneck P, Srinivasan P, Stevens A, Zollei L. et al. 2011. Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front. Neuroinformatics 5:23 [Google Scholar]
  173. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. 2012. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61:1000–16 [Google Scholar]
  174. Zhang K, Sejnowski TJ. 2000. A universal scaling law between gray matter and white matter of cerebral cortex. PNAS 97:5621–26 [Google Scholar]
  175. Zhang S, Laidlaw DH. 2006. Sampling DTI fibers in the human brain based on DWI forward modeling. Proc. 28th IEEE EMBS Annu. Int. Conf., New York,4885–88 New York: IEEE [Google Scholar]

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error