Multi-compartment Microscopic Diffusion Imaging

Authors: Enrico Kadena, Nathaniel D. Kelmb, Robert P. Carsonc, Mark D. Doesb, Daniel C. Alexandera

Abstract:

This paper introduces a multi-compartment model for microscopic diffusion anisotropy imaging. The aim is to estimate microscopic features specific to the intra- and extra-neurite compartments in nervous tissue unconfounded by the effects of fibre crossings and orientation dispersion, which are ubiquitous in the brain. The proposed MRI method is based on the Spherical Mean Technique (SMT), which factors out the neurite orientation distribution and thus provides direct estimates of the microscopic tissue structure. This technique can be immediately used in the clinic for the assessment of various neurological conditions, as it requires only a widely available off-the-shelf sequence with two b-shells and high-angular gradient resolution achievable within clinically feasible scan times. To demonstrate the developed method, we use high-quality diffusion data acquired with a bespoke scanner system from the Human Connectome Project. This study establishes the normative values of the new biomarkers for a large cohort of healthy young adults, which may then support clinical diagnostics in patients. Moreover, we show that the microscopic diffusion indices offer direct sensitivity to pathological tissue alterations, exemplified in a preclinical animal model of Tuberous Sclerosis Complex (TSC), a genetic multi-organ disorder which impacts brain microstructure and hence may lead to neurological manifestations such as autism, epilepsy and developmental delay.

Keywords

Spherical Mean Technique (SMT); Microscopic diffusion anisotropy; Neurite density; Fibre crossings; Orientation dispersion; Tuberous Sclerosis Complex (TSC).

Citation: Enrico Kadena, Nathaniel D. Kelmb, Robert P. Carsonc, Mark D. Doesb, Daniel C. Alexandera Multi-compartment Microscopic Diffusion Imaging doi:10.1016/j.neuroimage.2016.06.002.

Received: 8 April 2016, Revised: 30 May 2016, Accepted: 2 June 2016, Available online: 6 June 2016

Copyright: © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Acknowledgements

This work was supported by grants EPSRCEP/G007748/1, EPSRC EP/M020533/1, EPSRC EP/N018702/1, H2020 634541-2, NIHR01 EB001744, NIH 5K08 NS050484, NIH T32 EB014841 and NIH S10 RR029523. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.