White Matter Tractography and Diffusion‐Weighted Imaging

Abstract

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.
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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]