Abstract
We compare two strategies for modeling the connections of the brain's white matter: fiber clustering and the parcellation-based connectome. Both methods analyze diffusion magnetic resonance imaging fiber tractography to produce a quantitative description of the brain's connections. Fiber clustering is designed to reconstruct anatomically-defined white matter tracts, while the parcellation-based white matter segmentation enables the study of the brain as a network. From the perspective of white matter segmentation, we compare and contrast the goals and methods of the parcellation-based and clustering approaches, with special focus on reviewing the field of fiber clustering. We also propose a third category of new hybrid methods that combine the aspects of parcellation and clustering, for joint analysis of connection structure and anatomy or function. We conclude that these different approaches for segmentation and modeling of the white matter can advance the neuroscientific study of the brain's connectivity in complementary ways.
Highlights
Computational methods that attempt to answer questions about the function and structure of the human brain are increasingly popular
Fiber clustering aims to reconstruct tracts corresponding to anatomical divisions of the white matter, while parcellation-based segmentation divides tracts according to the cortical regions, or nodes, that they connect
We review many of the methods that have been proposed for segmenting white matter tractography
Summary
Computational methods that attempt to answer questions about the function and structure of the human brain are increasingly popular. Many methods aim to describe the structural connectivity or wiring diagram of the brain, where processing streams in the brain’s functional regions are interconnected by white matter fiber tracts. Based on dMRI data, the fiber tracts can be virtually reconstructed or traced throughout the brain using computational methods called tractography The pairwise connectivities are encoded in a matrix that models networks in the brain [69] These two popular styles of analysis of dMRI tractography data both perform a segmentation of the white matter, but with different goals. Fiber clustering aims to reconstruct tracts corresponding to anatomical divisions of the white matter, while parcellation-based segmentation divides tracts according to the cortical regions, or nodes, that they connect. We compare the parcellation and clustering approaches by discussing how their outputs correspond to the brain’s anatomical structure and function. We conclude with an assessment of the impact of the parcellation and clustering methods, demonstrating that these different approaches can advance the study of the brain’s connectivity in complementary ways
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