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  Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

Riffert, T., Schreiber, J., Anwander, A., & Knösche, T. R. (2014). Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models. NeuroImage, 100, 176-191. doi:10.1016/j.neuroimage.2014.06.015.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0019-B600-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-1509-C
Genre: Journal Article

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 Creators:
Riffert, Till1, Author              
Schreiber, Jan1, Author              
Anwander, Alfred2, Author              
Knösche, Thomas R.1, Author              
Affiliations:
1Methods and Development Group MEG and EEG - Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2205650              
2Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              

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Free keywords: Diffusion MRI; Spherical deconvolution; Microstructural metrics; Bingham distribution; Fiber orientation density function
 Abstract: Diffusion MRI (dMRI) measurements are used for inferring the microstructural properties of white matter and to reconstruct fiber pathways. Very often voxels contain complex fiber configurations comprising multiple bundles, rendering the simple diffusion tensor model unsuitable. Multi-compartment models deliver a convenient parameterization of the underlying complex fiber architecture, but pose challenges for fitting and model selection. Spherical deconvolution, in contrast, very economically produces a fiber orientation density function (fODF) without any explicit model assumptions. Since, however, the fODF is represented by spherical harmonics, a direct interpretation of the model parameters is impossible. Based on the fact that the fODF can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle), we offer a solution that seeks to combine the advantages of both approaches: first the fiber configuration is modeled as fODF represented by spherical harmonics and then each of the peaks is parameterized separately in order to characterize the underlying bundle. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second-order statistics of the fiber orientations, from which we derive metrics for the parametric quantification of fiber bundles. We propose meaningful relationships between these measures and the underlying microstructural properties. We focus on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. We compare these metrics to the conventionally used fractional anisotropy (FA) and show how they may help to increase the specificity of the characterization of microstructural properties. While metric relying on the first moments of the Bingham distributions provide relatively robust results, second-order metrics representing the peak spread are only meaningful, if the SNR is very high and no fiber crossings are present in the voxel.

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Language(s): eng - English
 Dates: 2014-06-062014-06-142014-10-15
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2014.06.015
PMID: 24936681
Other: Epub 2014
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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Elsevier Inc.
Pages: - Volume / Issue: 100 Sequence Number: - Start / End Page: 176 - 191 Identifier: ISSN: 1053-8119
CoNE: /journals/resource/954922650166