Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Tutorial: a guide to diffusion MRI and structural connectomics

Nature Protocols volume 20, pages 317–335 (2025)Cite this article

Subjects

Abstract

Diffusion magnetic resonance imaging (dMRI) is a versatile imaging technique that has gained popularity thanks to its sensitive ability to measure displacement of water molecules within a living tissue on a micrometer scale. Although dMRI has been around since the early 1990s, its applications are constantly evolving, primarily regarding the inference of structural connectomics from nerve fiber trajectories. However, these applications require expertise in image processing and statistics, and it can be difficult for a newcomer to choose an appropriate pipeline to fit their research needs, not least because dMRI is such a flexible methodology that dozens of acquisition and analysis pipelines have been developed over the years. This introductory guide is designed for graduate students and researchers in the neuroscience community who are interested in integrating this new methodology regardless of their background in neuroimaging and computational tools. The guide provides a brief overview of the basic dMRI methodologies but focuses on its applications in neuroplasticity and connectomics. The guide starts with dMRI experimental designs and a complete step-by-step pipeline for structural connectomics. The following section covers the basics of dMRI, including parameters and clinical applications (apparent diffusion coefficient, mean diffusivity, fractional anisotropy and microscopic fractional anisotropy), as well as different approaches and models. The final section focuses on structural connectomics, covering subjects from fiber tracking (techniques, evaluation and limitations) to structural networks (constructing, analyzing and visualizing a network).

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The processing pipeline for performing structural connectomics.
Fig. 2: DWI and MD.
Fig. 3: ADC and FA.
Fig. 4: Axon diameter and tractography.
Fig. 5: Tractography.
Fig. 6: Networks visualizations and measures.

Similar content being viewed by others

References

  1. Park, H. J. & Friston, K. Structural and functional brain networks: from connections to cognition. Science 342, 1238411 (2013).

    PubMed  Google Scholar 

  2. Van Essen, D. C. et al. The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013).

    PubMed  Google Scholar 

  3. Jbabdi, S. & Johansen-Berg, H. Tractography: where do we go from here? Brain Connect. 1, 169–183 (2011).

    PubMed  PubMed Central  Google Scholar 

  4. Van Essen, D. C. & Glasser, M. F. The human connectome project: progress and prospects. Cerebrum 2016, cer-10-16 (2016).

    PubMed Central  Google Scholar 

  5. Jones, D. K. Diffusion MRI: Theory, Methods, and Applications (Oxford University Press, 2012).

  6. Dale, B. M., Brown, M. A. & Semelka, R. C. MRI: Basic Principles and Applications 5th edn. (Wiley-Blackwell, 2015).

  7. Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).

    PubMed  Google Scholar 

  8. Leemans, A., Jeurissen, B., Sijbers, J. & Jones, D. K. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In 17th Annual Meeting of the International Society of Magnetic Resonance in Medicine 3537 (ISMRM, 2009).

  9. Tournier, J. D. et al. Mrtrix3: A fast, flexible and open software framework for medical image processing and visualization. Neuroimage 202, 116–137 (2019).

    Google Scholar 

  10. Theaud, G., Houde, J. C., Rheault, A. B. F., Morency, F. & Descoteaux, M. TractoFlow: a robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity. Neuroimage 218, 116889 (2020).

    PubMed  Google Scholar 

  11. Cieslak, M. et al. QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nat. Methods 18, 775–778 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Cruces, R. R. et al. Micapipe: a pipeline for multimodal neuroimaging and connectome analysis. Neuroimage 263, 119612 (2022).

    PubMed  Google Scholar 

  13. Assaf, Y. & Barazany, D. in Advances in Magnetic Resonance Technology and Applications Vol. 4 (eds. Cohi, Y. & Jezzard, P.) 157–173 (Academic Press, 2021).

  14. Johansen-Berg, H. & Behrens, T. E. J. Diffusion MRI: From Quantitative Measurement to In vivo Neuroanatomy 2nd edn. (Elsevier, 2014).

  15. Tavor, I., Hofstetter, S. & Assaf, Y. Micro-structural assessment of short term plasticity dynamics. Neuroimage 81, 1–7 (2013).

    PubMed  Google Scholar 

  16. Assaf, Y. Imaging laminar structures in the gray matter with diffusion MRI. Neuroimage 197, 677–688 (2019).

    PubMed  Google Scholar 

  17. Sotiropoulos, S. N. & Zalesky, A. Building connectomes using diffusion MRI: why, how and but. NMR Biomed. 32, e3752 (2019).

    PubMed  Google Scholar 

  18. Rheault, F., Poulin, P., Valcourt Caron, A., St-Onge, E. & Descoteaux, M. Common misconceptions, hidden biases and modern challenges of dMRI tractography. J. Neural Eng. 17, 011001 (2020).

    PubMed  Google Scholar 

  19. Yeh, C. H., Jones, D. K., Liang, X., Descoteaux, M. & Connelly, A. Mapping structural connectivity using diffusion MRI: challenges and opportunities. J. Magn. Reson. Imaging 53, 1666–1682 (2021).

    PubMed  Google Scholar 

  20. Helenius, J. et al. Diffusion-weighted MR imaging in normal human brains in various age groups. Am. J. Neuroradiol. 23, 194–199 (2002).

    PubMed  PubMed Central  Google Scholar 

  21. Schlaug, G., Siewert, B., Benfield, A., Edelman, R. R. & Warach, S. Time course of the apparent diffusion coefficient (ADC) abnormality in human stroke. Neurology 49, 113–119 (1997).

    CAS  PubMed  Google Scholar 

  22. Kuroiwa, T. et al. Different apparent diffusion coefficient: water content correlations of gray and white matter during early ischemia. Stroke 29, 859–865 (1998).

    CAS  PubMed  Google Scholar 

  23. van Everdingen, K. J., van der Grond, J., Kappelle, L. J., Ramos, L. M. P. & Mali, W. P. T. M. Diffusion-weighted magnetic resonance imaging in acute stroke. Stroke 29, 1783–1790 (1998).

    PubMed  Google Scholar 

  24. Harkins, K. D., Galons, J. P., Secomb, T. W. & Trouard, T. P. Assessment of the effects of cellular tissue properties on ADC measurements by numerical simulation of water diffusion. Magn. Reson. Med. 62, 1414–1422 (2009).

    PubMed  PubMed Central  Google Scholar 

  25. Jose, J. M., Marques, P., Alves, V. & Nuno, S. A hitchhiker’s guide to diffusion tensor imaging. Front. Neurosci. 7, 1–14 (2013).

    Google Scholar 

  26. Sener, R. N. Diffusion MRI: apparent diffusion coefficient (ADC) values in the normal brain and a classification of brain disorders based on ADC values. Comput. Med. Imaging Graph. 25, 299–326 (2001).

    CAS  PubMed  Google Scholar 

  27. Barrio-Arranz, G., de Luis-García, R., Tristán-Vega, A., Martín-Fernández, M. & Aja-Fernández, S. Impact of MR acquisition parameters on DTI scalar indexes: a tractography based approach. PloS ONE 10, e0137905 (2015).

    PubMed  PubMed Central  Google Scholar 

  28. Assaf, Y., Johansen-Berg, H. & Thiebaut de Schotten, M. The role of diffusion MRI in neuroscience. NMR Biomed. 32, e3762 (2019).

    PubMed  Google Scholar 

  29. Blumenfeld-Katzir, T., Pasternak, O., Dagan, M. & Assaf, Y. Diffusion MRI of structural brain plasticity induced by a learning and memory task. PloS ONE 6, e20678 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Tavor, I., Botvinik-Nezer, R., Bernstein-Eliav, M., Tsarfaty, G. & Assaf, Y. Short-term plasticity following motor sequence learning revealed by diffusion magnetic resonance imaging. Hum. Brain Mapp. 41, 442–452 (2020).

    PubMed  Google Scholar 

  31. Assaf, Y. New dimensions for brain mapping. Science 362, 994–995 (2018).

    CAS  PubMed  Google Scholar 

  32. Sagi, Y. et al. Learning in the fast lane: new insights into neuroplasticity. Neuron 73, 1195–1203 (2012).

    CAS  PubMed  Google Scholar 

  33. Hofstetter, S., Tavor, I., Tzur-Moryosef, S. & Assaf, Y. Short-term learning induces white matter plasticity in the fornix. J. Neurosci. 33, 12844–12850 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Hofstetter, S., Friedmann, N. & Assaf, Y. Rapid language-related plasticity: microstructural changes in the cortex after a short session of new word learning. Brain Struct. Funct. 222, 1231–1241 (2017).

    PubMed  Google Scholar 

  35. Henf, J., Grothe, M. J., Brueggen, K., Teipel, S. & Dyrba, M. Mean diffusivity in cortical gray matter in Alzheimer’s disease: The importance of partial volume correction. Neuroimage Clin. 17, 579–586 (2018).

    PubMed  Google Scholar 

  36. Duncan, J. S. Imaging the brain’s highways-diffusion tensor imaging in epilepsy. Epilepsy Curr. 8, 85–89 (2008).

    PubMed  PubMed Central  Google Scholar 

  37. Szczepankiewicz, F. et al. Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors. Neuroimage 104, 241–252 (2015).

    PubMed  Google Scholar 

  38. Kaden, E., Kelm, N. D., Carson, R. P., Does, M. D. & Alexander, D. C. Multi-compartment microscopic diffusion imaging. Neuroimage 139, 346–359 (2016).

    PubMed  Google Scholar 

  39. Henriques, R. N., Jespersen, S. N. & Shemesh, N. Microscopic anisotropy misestimation in spherical-mean single diffusion encoding MRI. Magn. Reson. Med. 81, 3245–3261 (2019).

    PubMed  PubMed Central  Google Scholar 

  40. Magdoom, K. N., Avram, A. V., Sarlls, J. E., Dario, G. & Basser, P. J. A novel framework for in-vivo diffusion tensor distribution MRI of the human brain. Neuroimage 271, 120003 (2023).

    PubMed  Google Scholar 

  41. Stejskal, E. O. & Tanner, J. E. Spin diffusion measurements: spin echoes in the presence of time-dependent field gradient. J. Chem. Phys. 42, 288–292 (1965).

    CAS  Google Scholar 

  42. Assaf, Y., Mayk, A. & Cohen, Y. Displacement imaging of spinal cord using q-space diffusion-weighted MRI. Magn. Reson. Med. 44, 713–722 (2000).

    CAS  PubMed  Google Scholar 

  43. Cohen, Y. & Assaf, Y. High b-value q-space analyzed diffusion-weighted MRS and MRI in neuronal tissues—a technical review. NMR Biomed. 15, 516–542 (2002).

    PubMed  Google Scholar 

  44. Jensen, H. J. & Helpern, J. A. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed. 23, 698–710 (2010).

    PubMed  PubMed Central  Google Scholar 

  45. Steven, A. J., Zhuo, J. & Melhem, E. R. Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain. Am. J. Roentgenol. 202, W26–W33 (2014).

    Google Scholar 

  46. Henriques, R. N. et al. Diffusional kurtosis imaging in the diffusion imaging in Python project. Front. Hum. Neurosci. 15, 675433 (2021).

    PubMed  PubMed Central  Google Scholar 

  47. Assaf, Y. & Basser, P. J. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage 27, 48–58 (2005).

    PubMed  Google Scholar 

  48. Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61, 1000–1016 (2012).

    PubMed  Google Scholar 

  49. Scherrer, B. et al. Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND). Magn. Reson. Med. 76, 963–977 (2016).

    PubMed  Google Scholar 

  50. Assaf, Y., Blumenfeld-Katzir, T., Yovel, Y. & Basser, P. J. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Mag. Reson. Med. 59, 1347–1354 (2008).

    Google Scholar 

  51. 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 

  52. Assaf, Y. et al. The CONNECT project: combining macro- and micro-structure. Neuroimage 80, 273–282 (2013).

    CAS  PubMed  Google Scholar 

  53. Daducci, A., Dal Palù, A., Lemkaddem, A. & Thiran, J. P. COMMIT: convex optimization modeling for microstructure informed tractography. IEEE Trans. Med. Imaging 34, 246–257 (2015).

    PubMed  Google Scholar 

  54. Barakovic, M. et al. Bundle-specific axon diameter index as a new contrast to differentiate white matter tracts. Front. Neurosci. 15, 646034 (2021).

    PubMed  PubMed Central  Google Scholar 

  55. Drakesmith, M. et al. Estimating axon conduction velocity in vivo from microstructural MRI. Neuroimage 203, 116186 (2019).

    PubMed  Google Scholar 

  56. Dyrby, T. B., Søgaard, L. V., Hall, M. G., Ptito, M. & Alexander, D. C. Contrast and stability of the axon diameter index from microstructure imaging with diffusion MRI. Magn. Reson. Med. 70, 711–721 (2013).

    PubMed  Google Scholar 

  57. Harkins, K. D., Beaulieu, C., Xu, J., Gore, J. C. & Does, M. D. A simple estimate of axon size with diffusion MRI. Neuroimage 227, 117619 (2021).

    PubMed  Google Scholar 

  58. Daducci, A. et al. Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data. Neuroimage 15, 32–44 (2015).

    Google Scholar 

  59. Romascano, D. et al. ActiveAxADD: toward non-parametric and orientationally invariant axon diameter distribution mapping using PGSE. Magn. Reson. Med. 83, 2322–2330 (2020).

    PubMed  Google Scholar 

  60. Veraart, J. et al. Noninvasive quantification of axon radii using diffusion MRI. eLife 9, e49855 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Horowitz, A. et al. In vivo correlation between axon diameter and conduction velocity in the human brain. Brain Struct. Funct. 220, 1777–1788 (2015).

    PubMed  Google Scholar 

  62. Gast, H. et al. A method for in-vivo mapping of axonal diameter distributions in the human brain using diffusion-based axonal spectrum imaging (AxSI). Neuroinformatics 21, 469–482 (2023).

    PubMed  PubMed Central  Google Scholar 

  63. Sepehrband, F., Alexander, D. C., Kurniawan, N. D., Reutens, D. C. & Yang, Z. Towards higher sensitivity and stability of axon diameter estimation with diffusion-weighted MRI. NMR Biomed. 29, 293–308 (2016).

    PubMed  PubMed Central  Google Scholar 

  64. Heidemann, R. M., Anwander, A., Feiweier, T., Knösche, T. R. & Turner, R. k-space and q-space: combining ultra-high spatial and angular resolution in diffusion imaging using ZOOPPA at 7 T. Neuroimage 60, 967–978 (2012).

    PubMed  Google Scholar 

  65. Tournier, J. D., Calamante, F. & Connelly, A. Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed. 26, 1775–1786 (2013).

    PubMed  Google Scholar 

  66. 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 

  67. Fillard, P. et al. Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. Neuroimage 56, 220–234 (2011).

    PubMed  Google Scholar 

  68. Zhan, L. et al. For the Alzheimer’s Disease Neuroimaging Initiative. Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease. Front. Aging Neurosci. 7, 48 (2015).

    PubMed  PubMed Central  Google Scholar 

  69. Poulin, P., Jörgens, D., Jodoin, P. M. & Descoteaux, M. Tractography and machine learning: Current state and open challenges. Magn. Reson. Imaging 64, 37–48 (2019).

    PubMed  Google Scholar 

  70. Poulin, P. et al. TractoInferno—a large-scale, open-source, multi-site database for machine learning dMRI tractography. Sci. Data 9, 725 (2022).

    PubMed  PubMed Central  Google Scholar 

  71. Maier-Hein, K. H. et al. The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8, 1349 (2017).

    PubMed  PubMed Central  Google Scholar 

  72. Colon-Perez, L. M. et al. A majority rule approach for region-of-interest-guided streamline fiber tractography. Brain Imaging Behav. 10, 1137–1147 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. David, S. et al. The superoanterior fasciculus (SAF): a novel white matter pathway in the human brain? Front. Neuroanat. 13, 24 (2019).

    PubMed  PubMed Central  Google Scholar 

  74. Sotiropoulos, S. N., Behrens, T. E. & Jbabdi, S. Ball and rackets: inferring fiber fanning from diffusion-weighted MRI. Neuroimage 60, 1412–1425 (2012).

    PubMed  Google Scholar 

  75. Jeurissen, B., Tournier, J. D., Dhollander, T., Connelly, A. & Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage 103, 411–426 (2014).

    PubMed  Google Scholar 

  76. St-Onge, E., Al-Sharif, N., Girard, G., Theaud, G. & Descoteaux, M. Cortical surfaces integration with tractography for structural connectivity analysis. Brain Connect. 11, 505–517 (2021).

    PubMed  Google Scholar 

  77. Shastin, D. et al. Surface-based tracking for short association fibre tractography. Neuroimage 260, 119423 (2022).

    PubMed  Google Scholar 

  78. Mu, J., Xu, Q., Tian, J. & Liu, J. The effect of feature image on sensitivity of the statistical analysis in the pipeline of a tractography atlas-based analysis. Sci. Rep. 7, 12669 (2017).

    PubMed  PubMed Central  Google Scholar 

  79. Hagmann, P. et al. Mapping human whole-brain structural networks with diffusion MRI. PLoS ONE 2, e597 (2007).

    PubMed  PubMed Central  Google Scholar 

  80. Côté, M. A., Boré, A., Girard, G., Houde, J. C. & Descoteaux, M. Tractometer: online evaluation system for tractography. Med. Image Comput. Comput. Assist. Interv. 15, 699–706 (2012).

    PubMed  Google Scholar 

  81. Côté, M. A. et al. Tractometer: towards validation of tractography pipelines. Med. Image Anal. 17, 844–857 (2013).

    PubMed  Google Scholar 

  82. Schilling, K. G. et al. Prevalence of white matter pathways coming into a single white matter voxel orientation: the bottleneck issue in tractography. Hum. Brain Mapp. 43, 1196–1213 (2022).

    PubMed  Google Scholar 

  83. Shamir, I. & Assaf, Y. Expanding connectomics to the laminar level: a perspective. Netw. Neurosci. 7, 377–388 (2023).

    PubMed  PubMed Central  Google Scholar 

  84. Bastiani, M., Shah, N. J., Goebel, R. & Roebroeck, A. Human cortical connectome reconstruction from diffusion weighted MRI: The effect of tractography algorithm. Neuroimage 62, 1732–1749 (2012).

    PubMed  Google Scholar 

  85. Jones, D. K., Knösche, T. R. & Turner, R. White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. Neuroimage 73, 239–254 (2013).

    PubMed  Google Scholar 

  86. De Santis, S., Drakesmith, M., Bells, S., Assaf, Y. & Jones, D. K. Why diffusion tensor MRI does well only some of the time: variance and covariance of white matter tissue microstructure attributes in the living human brain. Neuroimage 89, 35–44 (2014).

    PubMed  Google Scholar 

  87. Jeurissen, B., Descoteaux, M., Mori, S. & Leemans, A. Diffusion MRI fiber tractography of the brain. NMR Biomed. 32, e3785 (2019).

    PubMed  Google Scholar 

  88. Brodmann, K. Vergleichende Lokalisationslehre der Grosshirnrinde (Johann Ambrosius, 1909).

  89. 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 

  90. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    PubMed  Google Scholar 

  91. Eickhoff, S. B., Heim, S., Zilles, K. & Amunts, K. Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps. Neuroimage 32, 570–582 (2006).

    PubMed  Google Scholar 

  92. Fornito, A., Zalesky, A. & Bullmore, E. Network scaling effects in graph analytic studies of human resting-state fMRI data. Front. Syst. Neurosci. 4, 22 (2010).

    PubMed  PubMed Central  Google Scholar 

  93. Fornito, A., Harrison, B. J., Zalesky, A. & Simons, J. S. Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proc. Natl Acad. Sci. USA 109, 12788–12793 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).

    PubMed  Google Scholar 

  95. Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. van den Heuvel, M. P., Stam, C. J., Boersma, M. & Hulshoff Pol, H. E. Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage 43, 528–539 (2008).

    PubMed  Google Scholar 

  97. Glasser, M. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Eickhoff, S. B., Yeo, B. T. T. & Genon, S. Imaging-based parcellations of the human brain. Nat. Rev. Neurosci. 19, 672–686 (2018).

    CAS  PubMed  Google Scholar 

  99. Craddock, R. C. et al. Imaging human connectomes at the macroscale. Nat. Methods 10, 524–539 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Smith, R. E., Tournier, J. D., Calamante, F. & Connelly, A. SIFT: spherical‐deconvolution informed filtering of tractograms. Neuroimage 67, 298–312 (2013).

    PubMed  Google Scholar 

  101. Smith, R. E., Tournier, J. D., Calamante, F. & Connelly, A. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. Neuroimage 104, 253–265 (2015).

    PubMed  Google Scholar 

  102. Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N. & Wandell, B. A. Evaluation and statistical inference for human connectomes. Nat. Methods 11, 1058–1063 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775–15786 (2011).

    PubMed  PubMed Central  Google Scholar 

  104. Wig, G. S. Segregated systems of human brain networks. Trends Cogn. Sci. 21, 981–996 (2017).

    PubMed  Google Scholar 

  105. Bernhardt, B. C., Smallwood, J., Keilholz, S. & Margulies, D. S. Gradients in brain organization. Neuroimage 251, 1189872022 (2022).

    Google Scholar 

  106. Royer, J. et al. Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy. Brain 146, 3923–3937 (2023).

    PubMed  PubMed Central  Google Scholar 

  107. Jones, D. K. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn. Reson. Med. 51, 807–815 (2004).

    PubMed  Google Scholar 

  108. Le Bihan, D. Diffusion/perfusion MR imaging of the brain: from structure to function. Radiology 177, 328–329 (1990).

    PubMed  Google Scholar 

  109. Le Bihan, D., Urayama, S. I., Aso, T., Hanakawa, T. & Fukuyama, H. Direct and fast detection of neuronal activation in the human brain with diffusion MRI. Proc. Natl Acad. Sci. USA 103, 8263–8268 (2006).

    PubMed  PubMed Central  Google Scholar 

  110. Le Bihan, D. The ‘wet mind’: water and functional neuroimaging. Phys. Med. Biol. 52, R57–R90 (2007).

    PubMed  Google Scholar 

  111. Le Bihan, D. Diffusion, confusion and functional MRI. Neuroimage 62, 1131–1136 (2012).

    PubMed  Google Scholar 

  112. Garyfallidis, E. et al. DIPY, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8, 8 (2014).

    PubMed  PubMed Central  Google Scholar 

  113. Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using NetworkX. In Proc. of the 7th Python in Science Conference (SciPy 2008) (eds. Varoquaux, G., Vaught, T. & Millman, J.) 11–15 (2008).

  114. De Domenico, M., Porter, M. A. & Arenas, A. MuxViz: a tool for multilayer analysis and visualization of networks. J. Complex Netw. 3, 159–176 (2015).

    Google Scholar 

  115. Krzywinski, M. I. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Shamir, I. & Assaf, Y. An MRI-based, data-driven model of cortical laminar connectivity. Neuroinformatics 19, 205–218 (2021).

    PubMed  Google Scholar 

  117. Shamir, I., Tomer, O., Krupnik, R. & Assaf, Y. Modelling the laminar connectome of the human brain. Brain Struct. Funct. 227, 2153–2165 (2022).

    PubMed  Google Scholar 

  118. Callaghan, P. T. Principles of Nuclear Magnetic Resonance Microscopy (Oxford Univ. Press, 1993).

  119. Le Bihan, D. Diffusion and Perfusion Magnetic Resonance Imaging: Applications to Functional MRI (Raven Press, 1995).

  120. Pierpaoli, C. & Basser, P. J. Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36, 893–906 (1996).

    CAS  PubMed  Google Scholar 

  121. Basser, P. J., Mattiello, J. & LeBihan, D. MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Pierpaoli, C., Jezzard, P., Basser, P. J., Barnett, A. & Di Chiro, G. Diffusion tensor MR imaging of the human brain. Radiology 201, 637–648 (1996).

    CAS  PubMed  Google Scholar 

  123. Basser, P. J. & Pierpaoli, C. A simplified method to measure the diffusion tensor from seven MR images. Magn. Reson. Med. 39, 928–934 (1998).

    CAS  PubMed  Google Scholar 

  124. Tournier, J. D., Mori, S. & Leemans, A. Diffusion tensor imaging and beyond. Magn. Reson. Med. 65, 1532–1556 (2011).

    PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel

    Ittai Shamir & Yaniv Assaf

  2. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

    Yaniv Assaf

Authors
  1. Ittai Shamir
    View author publications

    Search author on:PubMed Google Scholar

  2. Yaniv Assaf
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Yaniv Assaf.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Note and Table 1.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shamir, I., Assaf, Y. Tutorial: a guide to diffusion MRI and structural connectomics. Nat Protoc 20, 317–335 (2025). https://doi.org/10.1038/s41596-024-01052-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41596-024-01052-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing