Diffusion MRI fiber tractography of the brain

By Ben Jeurissen, Maxime Descoteaux, Susumu Mori, Alexander Leemans. NMR in Biomedicine, 2019

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The ability of fiber tractography to delineate non‐invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of “connectomics,” a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography‐derived “connection strengths” between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber‐tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state‐of‐the‐art techniques, many issues that severely bias the resulting structural “connectomes” remain unresolved.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/dba9b3e437792c297753d84a35092ea06ac599ea

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