White Matter Tractography and Diffusion‐Weighted Imaging


Human cognition requires coordinated communication across macroscopic brain networks. This coordination is fundamentally constrained by how populations of neurons are connected together. Understanding how structural connectivity between brain regions constrains or predicts variability within and between individuals is a pervasive topic of cutting edge research in neuroscience and the focus of multimillion dollar investments in brain research (e.g. Human Connectome Project, the White House's B.R.A.I.N initiative). Currently, diffusion‐weighted imaging is the only noninvasive tool for studying the anatomical connectivity of macroscopic networks in the living human brain. Recent innovations in the acquisition and analysis of diffusion‐weighted imaging provide an unprecedented opportunity to examine how an individual's unique structural wiring constrains brain function and cognition and how this unique wiring is sculpted by both genetics and experience across the lifespan.

Key Concepts

  • The brain consists of 86 billion neurons that have macroscopic gray matter that represents and processes information (brain regions) and white matter that communicates information between disparate brain regions (axonal connections).
  • To study structural connectivity, diffusion‐weighted imaging (DWI) measures water diffusion using magnetic resonance imaging (MRI) technology, relying on the clever insight that the presence of an axon will restrict the movement of water molecules to align with the direction of the axon's trajectory.
  • The most popular DWI sampling schemes (DTI, HARDI, and DSI) differ in parameters that trade off between the total time of the scanning session and the resolution of water diffusion direction, which elucidates the direction and size of structural connections.
  • After diffusion images have been collected using a DWI sequence, reconstruction algorithms convert the raw MR diffusion signal to an estimate of the pattern of directional water movement in each voxel.
  • Once the fibre directions are reconstructed within a voxel, fibre tractography approaches can be applied to map the trajectories of axon bundles and delineate the path of major structural pathways between the brain regions.
  • Structural connectivity estimates from tractography have been productively employed to study macroscopic anatomical connections known as the structural human connectome.
  • To study brain networks, methods from network science represent neuroimaging data as graphs: gray matter brain regions serve as the nodes of the graph, and white matter tractography defines the edges that connect the nodes.
  • DWI provides an informative lens to investigate how genetics and learning interact and influence our unique structural wiring, including research that can identify an individual from their wiring alone at near perfect accuracy yet still capture plasticity at both short‐term (6 weeks) and long‐term (decades) timescales.

Keywords: neuroimaging; brain anatomy; axonal integrity; structural connectivity; neural communication; human connectome; brain structure–function relationships; individual differences

Figure 1. Principles of diffusion imaging. The axon of a neuron (represented as a cylinder) constrains the movement of water in small patch of imaged brain tissue, known as a voxel, using an MRI (magnetic resonance imaging) scanner, and this causes anisotropic water diffusion (top). When no axons are present, water moves equally in all directions in isotropic water diffusion (bottom).
Figure 2. Model‐based and model‐free estimations of water diffusion. Most imaged voxels in DWI (diffusion‐weighted imaging) belong to one of the three categories: no axons present (left column), axons aligned in one primary direction (middle column), or crossing axons oriented in different directions (right column). The first row depicts possible patterns of water diffusion, whereas the second and third rows illustrate two types of reconstruction methods and how they estimate the corresponding diffusion pattern. In row 2, a model‐based method known as the ball‐and‐stick method estimates the overall magnitude of diffusion (represented as a ball) and the fibre direction (represented as oriented sticks). In row 3, a model‐free method estimates an orientation distribution function (ODF), and the model captures multiple peaks in the empirical distribution of the water diffusion (represented by the ellipsoids) when crossing fibres are present.
Figure 3. Estimating brain networks. To examine brain network properties, gray matter is parcellated into distinct brain regions using a brain atlas (left). These regions then serve as the nodes of a graph (middle top) and the DWI structural connections, or fibre tractography, as edges (middle bottom) in a brain graph (right) that represents a brain network. The resultant brain graph can be used to investigate how structural connectivity relates to individual differences in function and performance.
Figure 4. Voxel‐based and network‐based metrics of structural integrity. On the left, two voxel‐based metrics are depicted: voxels with high fractional anisotropy (FA) reflect greater density in large, primary fibres, whereas a local connectome fingerprint also captures variability in crossing fibres, creating a neural signature of an individual's unique structural connectivity. On the right, two network‐based metrics are depicted: a measure known as walk length defines the number of nodes that are traversed on the path from an origin to a destination node, whereas a measure known as modularity identifies communities based on the density of structural connections.


Alexander DC, Barker GJ and Arridge SR (2002) Detection and modeling of non‐Gaussian apparent diffusion coefficient profiles in human brain data. Magnetic Resonance in Medicine 48: 331–340.

Azevedo FAC, Carvalho LRB, Grinberg LT, et al. (2009) Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled‐up primate brain. The Journal of Comparative Neurology 513: 532–541.

Bassett DS and Sporns O (2017) Network neuroscience. Nature Neuroscience 20: 353–364.

Behrens TEJ, Woolrich MW, Jenkinson M, et al. (2003) Characterization and propagation of uncertainty in diffusion‐weighted MR imaging. Magnetic Resonance in Medicine 50: 1077–1088.

Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MFS and Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34: 144–155.

Bortoletto M, Veniero D, Thut G and Miniussi C (2015) The contribution of TMS–EEG coregistration in the exploration of the human cortical connectome. Neuroscience & Biobehavioral Reviews 49: 114–124.

Budde MD, Janes L, Gold E, Turtzo LC and Frank JA (2011) The contribution of gliosis to diffusion tensor anisotropy and tractography following traumatic brain injury: validation in the rat using Fourier analysis of stained tissue sections. Brain 134: 2248–2260.

Bullmore E and Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience 10: 186–198.

Chiang M‐C, Barysheva M, Lee AD, et al. (2008) Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study. Medical Image Computing and Computer‐Assisted Intervention – MICCAI 11: 1060–1067.

Chiang M‐C, Barysheva M, Shattuck DW, et al. (2009) Genetics of brain fiber architecture and intellectual performance. Journal of Neuroscience 29: 2212–2224.

Daducci A et al. (2014) Quantitative comparison of reconstruction methods for intra‐voxel fiber recovery from diffusion MRI. IEEE Transactions on Medical Imaging 33: 384–399.

Daducci A, Dal Palú A, Descoteaux M and Thiran J‐P (2016) Microstructure informed tractography: pitfalls and open challenges. Frontiers in Neuroscience 10: 247.

Ellison‐Wright I and Bullmore E (2009) Meta‐analysis of diffusion tensor imaging studies in schizophrenia. Schizophrenia Research 108: 3–10.

Feinberg DA and Setsompop K (2013) Ultra‐fast MRI of the human brain with simultaneous multi‐slice imaging. Journal of Magnetic Resonance 229: 90–100.

Fernandez‐Miranda JC, Pathak S, Engh J, et al. (2012) High‐definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applications. Neurosurgery 71: 430–453.

Fillard P, Descoteaux M, Goh A, et al. (2011) Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. NeuroImage 56: 220–234.

Hagmann P, Jonasson L, Maeder P, et al. (2006) Understanding diffusion MR imaging techniques: from scalar diffusion‐weighted imaging to diffusion tensor imaging and beyond. Radiographics 26: S205–S223.

Jeurissen B, Leemans A, Tournier J‐D, Jones DK and Sijbers J (2013) Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Human Brain Mapping 34: 2747–2766.

Johansen‐Berg H (2010) Behavioural relevance of variation in white matter microstructure. Current Opinion in Neurology 23: 351–358.

Kahn AE, Mattar MG, Vettel JM, et al. (2016) Structural pathways supporting swift acquisition of new visuomotor skills. Cerebral Cortex 27 (1): 173–184.

Le Bihan D and Johansen‐Berg H (2012) Diffusion MRI at 25: exploring brain tissue structure and function. NeuroImage 61: 324–341.

Lichenstein SD, Bishop JH, Verstynen TD and Yeh F‐C (2016) Diffusion capillary phantom vs. human data: outcomes for reconstruction methods depend on evaluation medium. Frontiers in Neuroscience 10: 407.

Mori S and van Zijl PCM (2002) Fiber tracking: principles and strategies – a technical review. NMR in Biomedicine 15: 468–480.

Muldoon SF, Pasqualetti F, Gu S, et al. (2016) Stimulation‐based control of dynamic brain networks. PLoS Computational Biology 12: e1005076.

Muraskin J, Sherwin J, Lieberman G, et al. (2017) Fusing multiple neuroimaging modalities to assess group differences in perception–action coupling. Proceedings of the IEEE 105: 83–100.

Passingham RE, Stephan KE and Kotter R (2002) The anatomical basis of functional localization in the cortex. Nature Reviews. Neuroscience 3: 606–616.

Pfefferbaum A, Sullivan EV and Carmelli D (2001) Genetic regulation of regional microstructure of the corpus callosum in late life. Neuroreport 12: 1677–1681.

Reveley C, Seth AK, Pierpaoli C, et al. (2015) Superficial white matter fiber systems impede detection of long‐range cortical connections in diffusion MR tractography. Proceedings of the National Academy of Sciences 112: E2820–E2828.

Schmitt JE, Wallace GL, Rosenthal MA, et al. (2007) A multivariate analysis of neuroanatomic relationships in a genetically informative pediatric sample. NeuroImage 35: 70–82.

Scholz J, Klein MC, Behrens TEJ and Johansen‐Berg H (2009) Training induces changes in white‐matter architecture. Nature Neuroscience 12: 1370–1371.

Sexton CE, Kalu UG, Filippini N, Mackay CE and Ebmeier KP (2011) A meta‐analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease. Neurobiology of Aging 32: 2322.e5–2322.e18.

Taubert M, Draganski B, Anwander A, et al. (2010) Dynamic properties of human brain structure: learning‐related changes in cortical areas and associated fiber connections. Journal of Neuroscience 30: 11670–11677.

Tomassini V, Jbabdi S, Kincses ZT, et al. (2011) Structural and functional bases for individual differences in motor learning. Human Brain Mapping 32: 494–508.

Tournier JD, Calamante F, Gadian DG and Connelly A (2004) Direct estimation of the fiber orientation density function from diffusion‐weighted MRI data using spherical deconvolution. NeuroImage 23 (3): 1176–1185.

Tuch DS, Reese TG, Wiegell MR, et al. (2002) High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magnetic Resonance in Medicine 48: 577–582.

Van AT, Granziera C and Bammer R (2010) An introduction to model‐independent diffusion magnetic resonance imaging. Topics in Magnetic Resonance Imaging 21: 339–354.

Verstynen T (2015) How form constrains function in the human brain. In: Kosslyn S and Scott R (eds) Emerging Trends in Social and Behavioral Sciences. Chichester, United Kingdom: John Wiley & Sons, Inc.

Yeh F‐C, Badre D and Verstynen T (2015) Connectometry: a statistical approach harnessing the analytical potential of the local connectome. NeuroImage 125: 162–171.

Yeh F‐C, Vettel JM, Singh A, et al. (2016) Quantifying differences and similarities in whole‐brain white matter architecture using local connectome fingerprints. PLoS Computational Biology 12: e1005203.

Yoshida S, Oishi K, Faria AV and Mori S (2013) Diffusion tensor imaging of normal brain development. Pediatric Radiology 43: 15–27.

Further Reading

Griffa A, Baumann PS, Thiran J‐P and Hagmann P (2013) Structural connectomics in brain diseases. NeuroImage 80: 515–526.

Jones DK, Knösche TR and Turner R (2013) White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. NeuroImage 73: 239–254.

Kanai R and Rees G (2011) The structural basis of inter‐individual differences in human behaviour and cognition. Nature Reviews. Neuroscience 12: 231–242.

Soares JM, Marques P, Alves V and Sousa N (2013) A hitchhiker's guide to diffusion tensor imaging. Frontiers in Neuroscience 7: 31.

Thomason ME and Thompson PM (2011) Diffusion imaging, white matter, and psychopathology. Annual Review of Clinical Psychology 7: 63–85.

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Vettel, Jean M, Cooper, Nicole, Garcia, Javier O, Yeh, Fang‐Cheng, and Verstynen, Timothy D(Oct 2017) White Matter Tractography and Diffusion‐Weighted Imaging. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0027162]