Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter

Axonal density and diameter are two fundamental properties of brain white matter. Recently, advanced diffusion MRI techniques have made these two parameters accessible in vivo. However, the techniques available to estimate such parameters are still under development. For example, current methods to map axonal diameters capture relative trends over different structures, but consistently over-estimate absolute diameters. Axonal density estimates are more accessible experimentally, but different modeling approaches exist and the impact of the experimental parameters has not been thoroughly quantified, potentially leading to incompatibility of results obtained in different studies using different techniques. Here, we characterise the impact of diffusion time on axonal density and diameter estimates using Monte Carlo simulations and STEAM diffusion MRI at 7 T on 9 healthy volunteers. We show that axonal density and diameter estimates strongly depend on diffusion time, with diameters almost invariably overestimated and density both over and underestimated for some commonly used models. Crucially, we also demonstrate that these biases are reduced when the model accounts for diffusion time dependency in the extra-axonal space. For axonal density estimates, both upward and downward bias in different situations are removed by modeling extra-axonal time-dependence, showing increased accuracy in these estimates. For axonal diameter estimates, we report increased accuracy in ground truth simulations and axonal diameter estimates decreased away from high values given by earlier models and towards known values in the human corpus callosum when modeling extra-axonal time-dependence. Axonal diameter feasibility under both advanced and clinical settings is discussed in the light of the proposed advances.

[1]  P. V. van Zijl,et al.  Evaluation of restricted diffusion in cylinders. Phosphocreatine in rabbit leg muscle. , 1994, Journal of magnetic resonance. Series B.

[2]  D. Alexander A general framework for experiment design in diffusion MRI and its application in measuring direct tissue‐microstructure features , 2008, Magnetic resonance in medicine.

[3]  Chunshui Yu,et al.  Performances of diffusion kurtosis imaging and diffusion tensor imaging in detecting white matter abnormality in schizophrenia , 2014, NeuroImage: Clinical.

[4]  Cheng Guan Koay,et al.  Parsimonious Model Selection for Tissue Segmentation and Classification Applications: A Study Using Simulated and Experimental DTI Data , 2007, IEEE Transactions on Medical Imaging.

[5]  H. Harris,et al.  The Rat , 1958, Nature.

[6]  C. H. Neuman Spin echo of spins diffusing in a bounded medium , 1974 .

[7]  P. Basser,et al.  New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter , 2004, Magnetic resonance in medicine.

[8]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[9]  Mariana Lazar,et al.  Axonal deficits in young adults with High Functioning Autism and their impact on processing speed , 2014, NeuroImage: Clinical.

[10]  Response to the comments on the paper by Horowitz et al. (2014) , 2015, Brain Structure and Function.

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

[12]  A. Ludolph,et al.  Amyotrophic lateral sclerosis. , 2012, Current opinion in neurology.

[13]  C. Njiokiktjien,et al.  Callosal size in children with learning disabilities , 1994, Behavioural Brain Research.

[14]  Julien Cohen-Adad,et al.  The Human Connectome Project and beyond: Initial applications of 300mT/m gradients , 2013, NeuroImage.

[15]  D. Livy,et al.  Alcohol exposure during the first two trimesters-equivalent alters the development of corpus callosum projection neurons in the rat. , 2008, Alcohol.

[16]  Jan Sijbers,et al.  ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data , 2009 .

[17]  A. Ilienko,et al.  CONTINUOUS COUNTERPARTS OF POISSON AND BINOMIAL DISTRIBUTIONS AND THEIR PROPERTIES , 2013, 1303.5990.

[18]  Derek K. Jones,et al.  Gleaning multicomponent T1 and T2 information from steady‐state imaging data , 2008, Magnetic resonance in medicine.

[19]  P. L. Randall Schizophrenia, abnormal connection, and brain evolution. , 1983, Medical hypotheses.

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

[21]  B. Murphy,et al.  Signal-to-noise measures for magnetic resonance imagers. , 1993, Magnetic Resonance Imaging.

[22]  R. Caminiti,et al.  Comments on the paper by Horowitz et al. (2014) , 2014, Brain Structure and Function.

[23]  Hui Zhang,et al.  PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study , 2015, Magnetic resonance in medicine.

[24]  P. Basser,et al.  Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.

[25]  Joseph A. Helpern,et al.  White matter characterization with diffusional kurtosis imaging , 2011, NeuroImage.

[26]  S. Maruyama,et al.  Increase in diameter of the axonal initial segment is an early change in amyotrophic lateral sclerosis , 1992, Journal of the Neurological Sciences.

[27]  R. Adalbert,et al.  Age-related axonal swellings precede other neuropathological hallmarks in a knock-in mouse model of Huntington's disease , 2014, Neurobiology of Aging.

[28]  John C Gore,et al.  Fast and robust measurement of microstructural dimensions using temporal diffusion spectroscopy. , 2014, Journal of magnetic resonance.

[29]  J. Helpern,et al.  Monte Carlo study of a two‐compartment exchange model of diffusion , 2010, NMR in biomedicine.

[30]  B. Dardzinski,et al.  Temperature Dependent Change of Apparent Diffusion Coefficient of Water in Normal and Ischemic Brain of Rats , 1994, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[31]  Y. Assaf,et al.  Improved precision in CHARMED assessment of white matter through sampling scheme optimization and model parsimony testing , 2014, Magnetic resonance in medicine.

[32]  Derek K. Jones,et al.  Why diffusion tensor MRI does well only some of the time: Variance and covariance of white matter tissue microstructure attributes in the living human brain☆ , 2014, NeuroImage.

[33]  Daniel C. Alexander,et al.  Camino: Open-Source Diffusion-MRI Reconstruction and Processing , 2006 .

[34]  Yaniv Assaf,et al.  Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain , 2005, NeuroImage.

[35]  Els Fieremans,et al.  Revealing mesoscopic structural universality with diffusion , 2014, Proceedings of the National Academy of Sciences.

[36]  P. Basser,et al.  Axcaliber: A method for measuring axon diameter distribution from diffusion MRI , 2008, Magnetic resonance in medicine.

[37]  Tim B. Dyrby,et al.  Orientationally invariant indices of axon diameter and density from diffusion MRI , 2010, NeuroImage.

[38]  G. Yovel,et al.  In vivo correlation between axon diameter and conduction velocity in the human brain , 2014, Brain Structure and Function.

[39]  E. Fieremans,et al.  Novel White Matter Tract Integrity Metrics Sensitive to Alzheimer Disease Progression , 2013, American Journal of Neuroradiology.

[40]  B. Pakkenberg,et al.  Age-Induced White Matter Changes in the Human Brain: A Stereological Investigation , 1997, Neurobiology of Aging.

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

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

[43]  F. Ståhlberg,et al.  Diffusion‐weighted MRI measurements on stroke patients reveal water‐exchange mechanisms in sub‐acute ischaemic lesions , 2009, NMR in biomedicine.

[44]  P. Basser,et al.  In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. , 2009, Brain : a journal of neurology.

[45]  F. Ståhlberg,et al.  The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter , 2013, Magnetic Resonance Materials in Physics, Biology and Medicine.

[46]  F. Ståhlberg,et al.  The importance of axonal undulation in diffusion MR measurements: a Monte Carlo simulation study , 2012, NMR in biomedicine.

[47]  Alan Connelly,et al.  Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.

[48]  Qinhao Lin,et al.  Response to comments , 2004 .

[49]  J. Veraart,et al.  Degeneracy in model parameter estimation for multi‐compartmental diffusion in neuronal tissue , 2016, NMR in biomedicine.

[50]  Yaniv Assaf,et al.  Micro-structural assessment of short term plasticity dynamics , 2013, NeuroImage.

[51]  M. Horsfield,et al.  Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[52]  S Arndt,et al.  An MRI study of the corpus callosum in autism. , 1997, The American journal of psychiatry.

[53]  Charles S Springer,et al.  Equilibrium water exchange between the intra‐ and extracellular spaces of mammalian brain , 2003, Magnetic resonance in medicine.

[54]  A. Scheibel,et al.  Fiber composition of the human corpus callosum , 1992, Brain Research.

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

[56]  C. Beaulieu,et al.  The basis of anisotropic water diffusion in the nervous system – a technical review , 2002, NMR in biomedicine.

[57]  F Barkhof,et al.  Axonal damage in the spinal cord of MS patients occurs largely independent of T2 MRI lesions , 2002, Neurology.

[58]  Dmitry S. Novikov,et al.  Mesoscopic structure of neuronal tracts from time-dependent diffusion , 2015, NeuroImage.

[59]  Fernando Zelaya,et al.  Diffusion in porous systems and the influence of pore morphology in pulsed gradient spin-echo nuclear magnetic resonance studies , 1992 .

[60]  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.

[61]  D. Rice,et al.  Critical periods of vulnerability for the developing nervous system: evidence from humans and animal models. , 2000, Environmental health perspectives.

[62]  Daniel C. Alexander,et al.  Compensation for bias from unwanted gradient contributions in STEAM diffusion MRI , 2012 .

[63]  J. R. Hughes Autism: The first firm finding = underconnectivity? , 2007, Epilepsy & Behavior.

[64]  P. Basser Inferring microstructural features and the physiological state of tissues from diffusion‐weighted images , 1995, NMR in biomedicine.