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NODDI-DTI: extracting neurite orientation and dispersion parameters from a diffusion tensor

View ORCID ProfileLuke J. Edwards, Kerrin J. Pine, Nikolaus Weiskopf, Siawoosh Mohammadi
doi: https://doi.org/10.1101/077099
Luke J. Edwards
aDepartment of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraβe 1a, 04103 Leipzig, Germany
bWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, 12 Queen Square, London, WC1N 3BG, UK.
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  • For correspondence: ledwards@cbs.mpg.de
Kerrin J. Pine
aDepartment of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraβe 1a, 04103 Leipzig, Germany
bWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, 12 Queen Square, London, WC1N 3BG, UK.
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Nikolaus Weiskopf
aDepartment of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraβe 1a, 04103 Leipzig, Germany
bWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, 12 Queen Square, London, WC1N 3BG, UK.
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Siawoosh Mohammadi
bWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, 12 Queen Square, London, WC1N 3BG, UK.
cInstitut für Systemische Neurowissenschaften, Universitätsklinikum Hamburg-Eppendorf, Martinistraβe 52, 20246 Hamburg, UK
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Abstract

NODDI-DTI is a simplification of the NODDI model that, when its underlying assumptions are met, allows extraction of biophysical parameters from standard single-shell DTI data, permitting biophysical analysis of DTI datasets.

In contrast to the NODDI signal model, the NODDI-DTI signal model contains no CSF compartment, restricting its application to voxels with very low CSF partial-volume contamination. This simplification allowed derivation of simple analytical relations between parameters representing axon density, v and dispersion, τ, and DTI invariants (MD and FA) through moment expansion of the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. It should be emphasised that the NODDI-DTI model inherits the strong assumptions of the NODDI model, and so is nominally restricted to analysis of healthy brains.

NODDI-DTI parameter estimates were computed by applying the proposed analytical relations to DTI parameters extracted from the first shell of data, and compared to parameters extracted by fitting the NODDI-DTI model to (i) both shells (recommended) and (ii) the first shell (as for DTI) of data in the white matter of three different in vivo diffusion datasets. NODDI-DTI parameters estimated from DTI and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model it was found that diffusional kurtosis strongly biased DTI-based MD values, and so a novel heuristic correction requiring only DTI data was derived and used to account for this bias.

Our results demonstrate that NODDI-DTI is a promising model and technique to interpret restricted datasets acquired for DTI analysis with greater biophysical specificity, though its limitations must be borne in mind.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted January 21, 2017.
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NODDI-DTI: extracting neurite orientation and dispersion parameters from a diffusion tensor
Luke J. Edwards, Kerrin J. Pine, Nikolaus Weiskopf, Siawoosh Mohammadi
bioRxiv 077099; doi: https://doi.org/10.1101/077099
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NODDI-DTI: extracting neurite orientation and dispersion parameters from a diffusion tensor
Luke J. Edwards, Kerrin J. Pine, Nikolaus Weiskopf, Siawoosh Mohammadi
bioRxiv 077099; doi: https://doi.org/10.1101/077099

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