The effect of diffusion gradient direction number on corticospinal tractography in the human brain: an along-tract analysis

ObjectivesWe evaluated diffusion imaging measures of the corticospinal tract obtained with a probabilistic tractography algorithm applied to data of two acquisition protocols based on different numbers of diffusion gradient directions (NDGDs).Materials and methodsThe corticospinal tracts (CST) of 18 healthy subjects were delineated using 22 and 66-NDGD data. An along-tract analysis of diffusion metrics was performed to detect possible local differences due to NDGD.ResultsFA values at 22-NDGD showed an increase along the central portion of the CST. The mean of partial volume fraction of the orientation of the second fiber (f2) was higher at 66-NDGD bilaterally, because for 66-NDGD data the algorithm more readily detects dominant fiber directions beyond the first, thus the increase in FA at 22-NDGD is due to a substantially reduced detection of crossing fiber volume. However, the good spatial correlation between the tracts drawn at 22 and 66 NDGD shows that the extent of the tract can be successfully defined even at lower NDGD.ConclusionsGiven the spatial tract localization obtained even at 22-NDGD, local analysis of CST can be performed using a NDGD compatible with clinical protocols. The probabilistic approach was particularly powerful in evaluating crossing fibers when present.

[1]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[2]  Paul M. Thompson,et al.  Along-tract statistics allow for enhanced tractography analysis , 2012, NeuroImage.

[3]  Viola Priesemann,et al.  Local active information storage as a tool to understand distributed neural information processing , 2013, Front. Neuroinform..

[4]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[5]  Alain Pitiot,et al.  Handedness, motor skills and maturation of the corticospinal tract in the adolescent brain , 2009, Human brain mapping.

[6]  Sylvain Bouix,et al.  Thalamo‐frontal white matter alterations in chronic schizophrenia , 2009, Human brain mapping.

[7]  Alan Connelly,et al.  MRtrix: Diffusion tractography in crossing fiber regions , 2012, Int. J. Imaging Syst. Technol..

[8]  Osamu Abe,et al.  Tract-specific analysis of white matter pathways in healthy subjects: a pilot study using diffusion tensor MRI , 2009, Neuroradiology.

[9]  John S. Duncan,et al.  Combined functional MRI and tractography to demonstrate the connectivity of the human primary motor cortex in vivo , 2003, NeuroImage.

[10]  C. Lebel,et al.  Lateralization of the arcuate fasciculus from childhood to adulthood and its relation to cognitive abilities in children , 2009, Human brain mapping.

[11]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

[12]  René Westerhausen,et al.  Corticospinal tract asymmetries at the level of the internal capsule: Is there an association with handedness? , 2007, NeuroImage.

[13]  O. Masri,et al.  An Essay on the Human Corticospinal Tract: History, Development, Anatomy, and Connections , 2011 .

[14]  Maxime Descoteaux,et al.  Dipy, a library for the analysis of diffusion MRI data , 2014, Front. Neuroinform..

[15]  Fei Wang,et al.  Asymmetry analysis of cingulum based on scale‐invariant parameterization by diffusion tensor imaging , 2005, Human brain mapping.

[16]  R. C. Oldfield.  The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[17]  Matthew P. G. Allin,et al.  Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography , 2011, NeuroImage.

[18]  Joshua Ng,et al.  Microstructural correlations of white matter tracts in the human brain , 2010, NeuroImage.

[19]  D. Reich,et al.  Quantitative characterization of the corticospinal tract at 3T. , 2006, AJNR. American journal of neuroradiology.

[20]  P. Nathan,et al.  The corticospinal tracts in man. Course and location of fibres at different segmental levels. , 1990, Brain : a journal of neurology.

[21]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[22]  A. Alexander,et al.  Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. , 2004, AJNR. American journal of neuroradiology.

[23]  Susumu Mori,et al.  Fiber tracking: principles and strategies – a technical review , 2002, NMR in biomedicine.

[24]  In Chan Song,et al.  Diffusion tensor MRI as a diagnostic tool of upper motor neuron involvement in amyotrophic lateral sclerosis , 2004, Journal of the Neurological Sciences.

[25]  Hong Sun,et al.  Quantitative analysis along the pyramidal tract by length-normalized parameterization based on diffusion tensor tractography: Application to patients with relapsing neuromyelitis optica , 2006, NeuroImage.

[26]  J. Rademacher,et al.  Variability and asymmetry in the human precentral motor system. A cytoarchitectonic and myeloarchitectonic brain mapping study. , 2001, Brain : a journal of neurology.

[27]  Carl-Fredrik Westin,et al.  Tract-based morphometry for white matter group analysis , 2009, NeuroImage.

[28]  Jerry L. Prince,et al.  Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T , 2007, NeuroImage.

[29]  Ji Heon Hong,et al.  Somatotopic location of corticospinal tract at pons in human brain: A diffusion tensor tractography study , 2010, NeuroImage.

[30]  S Ekholm,et al.  Effects of number of diffusion gradient directions on derived diffusion tensor imaging indices in human brain. , 2006, AJNR. American journal of neuroradiology.

[31]  A. Leemans,et al.  Assessment of Global and Regional Diffusion Changes along White Matter Tracts in Parkinsonian Disorders by MR Tractography , 2013, PloS one.

[32]  M. I. Smith,et al.  A study of rotationally invariant and symmetric indices of diffusion anisotropy. , 1999, Magnetic resonance imaging.

[33]  Ali R. Khan,et al.  The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery , 2015, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[34]  S. Aja‐Fernández,et al.  Impact of MR Acquisition Parameters on DTI Scalar Indexes: A Tractography Based Approach , 2015, PloS one.

[35]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[36]  AJ Thompson,et al.  A comprehensive assessment of cerebellar damage in multiple sclerosis using diffusion tractography and volumetric analysis , 2011, Multiple sclerosis.

[37]  In Chan Song,et al.  Tractography-guided statistics (TGIS) in diffusion tensor imaging for the detection of gender difference of fiber integrity in the midsagittal and parasagittal corpora callosa , 2007, NeuroImage.

[38]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[39]  S Skare,et al.  Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI. , 2000, Journal of magnetic resonance.

[40]  N. Papadakis,et al.  Minimal gradient encoding for robust estimation of diffusion anisotropy. , 2000, Magnetic resonance imaging.

[41]  S. Zhuang,et al.  Effect of Increasing Diffusion Gradient Direction Number on Diffusion Tensor Imaging Fiber Tracking in the Human Brain , 2015, Korean journal of radiology.

[42]  R. Watts,et al.  Diffusion Tensor Tractography of the Motor White Matter Tracts in Man: Current Controversies and Future Directions , 2005, Annals of the New York Academy of Sciences.

[43]  Derek K. Jones,et al.  The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study † , 2004, Magnetic resonance in medicine.

[44]  David G. Gadian,et al.  A random effects modelling approach to the crossing-fibre problem in tractography , 2009, NeuroImage.

[45]  Thomas H Gillingwater,et al.  Quantitative tractography and tract shape modeling in amyotrophic lateral sclerosis , 2013, Journal of magnetic resonance imaging : JMRI.

[46]  Catherine Lebel,et al.  Six is enough? Comparison of diffusion parameters measured using six or more diffusion‐encoding gradient directions with deterministic tractography , 2012, Magnetic resonance in medicine.

[47]  Mara Cercignani,et al.  Twenty‐five pitfalls in the analysis of diffusion MRI data , 2010, NMR in biomedicine.

[48]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[49]  Osamu Abe,et al.  Amyotrophic lateral sclerosis: diffusion tensor tractography and voxel‐based analysis , 2004, NMR in biomedicine.

[50]  G J Barker,et al.  Diffusion tensor imaging detects corticospinal tract involvement at multiple levels in amyotrophic lateral sclerosis , 2003, Journal of neurology, neurosurgery, and psychiatry.

[51]  B. Ardekani,et al.  A Fully Automatic Multimodality Image Registration Algorithm , 1995, Journal of computer assisted tomography.

[52]  T Yuasa,et al.  Altered Microstructure in Corticospinal Tract in Idiopathic Normal Pressure Hydrocephalus: Comparison with Alzheimer Disease and Parkinson Disease with Dementia , 2011, American Journal of Neuroradiology.

[53]  Timothy Edward John Behrens,et al.  Probabilistic diffusion tractography: a potential tool to assess the rate of disease progression in amyotrophic lateral sclerosis. , 2006, Brain : a journal of neurology.