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NMR in Biomedicine
Article . 2017 . Peer-reviewed
License: CC BY NC
Data sources: Crossref
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NMR in Biomedicine
Article
License: CC BY NC
Data sources: UnpayWall
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Diffusion MRI fiber tractography of the brain

Authors: Ben Jeurissen; Maxime Descoteaux; Susumu Mori; Alexander Leemans;

Diffusion MRI fiber tractography of the brain

Abstract

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.

Country
Belgium
Keywords

Computer. Automation, Chemistry, Diffusion Tensor Imaging, Physics, Terminology as Topic, Connectome, Image Processing, Computer-Assisted, Brain, Humans, Biology, Algorithms

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    469
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
469
Top 0.1%
Top 1%
Top 0.1%
hybrid