Bingham–NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI

This paper presents Bingham-NODDI, a clinically-feasible technique for estimating the anisotropic orientation dispersion of neurites. Direct quantification of neurite morphology on clinical scanners was recently realised by a diffusion MRI technique known as neurite orientation dispersion and density imaging (NODDI). However in its current form NODDI cannot estimate anisotropic orientation dispersion, which is widespread in the brain due to common fanning and bending of neurites. This work proposes Bingham-NODDI that extends the NODDI formalism to address this limitation. Bingham-NODDI characterises anisotropic orientation dispersion by utilising the Bingham distribution to model neurite orientation distribution. The new model estimates the extent of dispersion about the dominant orientation, separately along the primary and secondary dispersion orientations. These estimates are subsequently used to estimate the overall dispersion about the dominant orientation and the dispersion anisotropy. We systematically evaluate the ability of the new model to recover these key parameters of anisotropic orientation dispersion with standard NODDI protocol, both in silico and in vivo. The results demonstrate that the parameters of the proposed model can be estimated without additional acquisition requirements over the standard NODDI protocol. Thus anisotropic dispersion can be determined and has the potential to be used as a marker for normal brain development and ageing or in pathology. We additionally find that the original NODDI model is robust to the effects of anisotropic orientation dispersion, when the quantification of anisotropic dispersion is not of interest.

[1]  Jean-Philippe Thiran,et al.  Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data , 2015, NeuroImage.

[2]  S. Nagarajan,et al.  White Matter Changes of Neurite Density and Fiber Orientation Dispersion during Human Brain Maturation , 2015, PloS one.

[3]  Thomas R. Knösche,et al.  Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models , 2014, NeuroImage.

[4]  Jean-Philippe Thiran,et al.  Connectivity and tissue microstructural alterations in right and left temporal lobe epilepsy revealed by diffusion spectrum imaging , 2014, NeuroImage: Clinical.

[5]  Stephen M. Smith,et al.  Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.

[6]  J. Gore,et al.  Theoretical Model for Water Diffusion in Tissues , 1995, Magnetic resonance in medicine.

[7]  Alan Connelly,et al.  Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.

[8]  Hui Zhang,et al.  Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models , 2014, NeuroImage.

[9]  Christopher D. Kroenke,et al.  Determination of Axonal and Dendritic Orientation Distributions Within the Developing Cerebral Cortex by Diffusion Tensor Imaging , 2012, IEEE Transactions on Medical Imaging.

[10]  Jelle Veraart,et al.  One diffusion acquisition and different white matter models: How does microstructure change in human early development based on WMTI and NODDI? , 2015, NeuroImage.

[11]  M. Chakravarty,et al.  Functional Consequences of Neurite Orientation Dispersion and Density in Humans across the Adult Lifespan , 2015, The Journal of Neuroscience.

[12]  P. Basser,et al.  Diffusion tensor MR imaging of the human brain. , 1996, Radiology.

[13]  Hui Zhang,et al.  Advanced diffusion imaging sequences could aid assessing patients with focal cortical dysplasia and epilepsy☆ , 2014, Epilepsy Research.

[14]  Ken Shoemake,et al.  Uniform Random Rotations , 1992, Graphics Gems III.

[15]  A. Friedman,et al.  Fiber dissection technique: lateral aspect of the brain. , 2000, Neurosurgery.

[16]  van Cappellen van Walsum A-M.,et al.  Validation of NODDI estimation of dispersion anisotropy in V1 of the human neocortex , 2015 .

[17]  Alan Edelman,et al.  The efficient evaluation of the hypergeometric function of a matrix argument , 2006, Math. Comput..

[18]  Christopher Bingham.  An Antipodally Symmetric Distribution on the Sphere , 1974 .

[19]  Jong Hoon Lee,et al.  Axial asymmetry of water diffusion in brain white matter , 2005, Magnetic resonance in medicine.

[20]  J. Jacobs,et al.  Regional dendritic and spine variation in human cerebral cortex: a quantitative golgi study. , 2001, Cerebral cortex.

[21]  Leif Østergaard,et al.  Modeling dendrite density from magnetic resonance diffusion measurements , 2007, NeuroImage.

[22]  Philip A. Cook,et al.  Modelling noise-induced fibre-orientation error in diffusion-tensor MRI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[23]  Hui Zhang,et al.  Axon diameter mapping in the presence of orientation dispersion with diffusion MRI , 2011, NeuroImage.

[24]  Timothy Edward John Behrens,et al.  Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI , 2012, NeuroImage.

[25]  B. Jacobs,et al.  Life‐span dendritic and spine changes in areas 10 and 18 of human cortex: A quantitative golgi study , 1997, The Journal of comparative neurology.

[26]  Daniel C. Alexander,et al.  In vivo Estimation of Dispersion Anisotropy of Neurites Using Diffusion MRI , 2014, MICCAI.

[27]  M. Mallar Chakravarty,et al.  Neurite density from magnetic resonance diffusion measurements at ultrahigh field: Comparison with light microscopy and electron microscopy , 2010, NeuroImage.

[28]  Sébastien Ourselin,et al.  Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI , 2015, NeuroImage.

[29]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[30]  Jean-Philippe Thiran,et al.  Accelerated Microstructure Imaging via Convex Optimization (AMICO) in crossing fibers , 2015 .

[31]  Kristen M Harris,et al.  Dendritic Spine Pathology: Cause or Consequence of Neurological Disorders? , 2002, Brain Research Reviews.

[32]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[33]  R. Peeters,et al.  Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI , 2015, Neurobiology of Aging.

[34]  Thomas R. Knösche,et al.  Parametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging , 2007, NeuroImage.

[35]  J. Olszewski,et al.  The postnatal development of the human cerebral cortex , 1955 .

[36]  Hui Zhang,et al.  Beyond Crossing Fibers: Tractography Exploiting Sub-voxel Fibre Dispersion and Neighbourhood Structure , 2013, IPMI.

[37]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[38]  Carl-Fredrik Westin,et al.  Processing and visualization for diffusion tensor MRI , 2002, Medical Image Anal..

[39]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[40]  M. Budde,et al.  Quantification of anisotropy and fiber orientation in human brain histological sections , 2012, Front. Integr. Neurosci..

[41]  David B. Kirk,et al.  Graphics Gems III , 1992 .

[42]  M. Hedehus,et al.  Diffusion time dependence of the apparent diffusion tensor in healthy human brain and white matter disease , 2001, Magnetic resonance in medicine.

[43]  Hui Zhang,et al.  White matter microstructure pathology in classic galactosemia revealed by neurite orientation dispersion and density imaging , 2014, Journal of Inherited Metabolic Disease.

[44]  Pratik Mukherjee,et al.  Aberrant White Matter Microstructure in Children with 16p11.2 Deletions , 2014, The Journal of Neuroscience.

[45]  Hui Zhang,et al.  Detailed laminar characteristics of the human neocortex revealed by NODDI , 2013 .