Abstract
Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively. Today, model-based techniques are widely available and used for white matter characterisation where their development is relatively mature. Conversely, tissue modelling in grey matter is more challenging, and no generally accepted models exist. With advances in measurement technology and modelling efforts, a clinically viable technique that reveals salient features of grey matter microstructure, such as the density of quasi-spherical cell bodies and quasi-cylindrical cell projections, is an exciting prospect. As a step towards capturing the microscopic architecture of grey matter in clinically feasible settings, this work uses a biophysical model that is designed to disentangle the diffusion signatures of spherical and cylindrical structures in the presence of orientation heterogeneity, and takes advantage of B-tensor encoding measurements, which provide additional sensitivity compared to standard single diffusion encoding sequences. For the fast and robust estimation of microstructural parameters, we leverage recent advances in machine learning and replace conventional fitting techniques with an artificial neural network that fits complex biophysical models within seconds. Our results demonstrate apparent markers of spherical and cylindrical geometries in healthy human subjects, and in particular an increased volume fraction of spherical compartments in grey matter compared to white matter. We evaluate the extent to which spherical and cylindrical geometries may be interpreted as correlates of neural soma and neural projections, respectively, and quantify parameter estimation errors in the presence of various departures from the modelling assumptions. While further work is necessary to translate the ideas presented in this work to the clinic, we suggest that biomarkers focussing on quasi-spherical cellular geometries may be valuable for the enhanced assessment of neurodevelopmental disorders and neurodegenerative diseases.
Highlights
Diffusion MRI enables the non-invasive investigation of tissue microstructure in the human brain (Johansen-Berg and Behrens, 2009; Jones, 2011)
The higher b-values obtained for linear tensor encoding (LTE) are beneficial for reliably estimating the four independent parameters of the model used in this work
Errors in the compartment diffusivities increase as the corresponding volume fraction decreases. These results suggest that estimates of λsph may be unreliable in white matter and estimates of λcyl may be unreliable in grey matter
Summary
Diffusion MRI enables the non-invasive investigation of tissue microstructure in the human brain (Johansen-Berg and Behrens, 2009; Jones, 2011) This imaging technique has been successful in white matter, where it has improved the characterisation of a range of medical conditions including multiple sclerosis (Bagnato et al, 2019; Ciccarelli et al, 2001; Hygino da Cruz et al, 2011; Schmierer et al, 2007; Werring et al, 1999), Alzheimer’s disease (Acosta-Cabronero and Nestor, 2014; Kantarci et al, 2017; Schouten et al, 2017), Huntington’s disease (McColgan et al, 2017; Novak et al, 2014), ischemic stroke (Fung et al, 2011; Lutsep et al, 1997; Warach et al, 1992), Autism spectrum disorder (Ameis et al, 2016; Gibbard et al, 2013; 2017; Travers et al, 2012) and congenital hypothyroidism (Cooper et al, 2019). We concentrate on the second challenge and outline recent advances in biophysical modelling, diffusion MR acquisition and computational modelling, which may facilitate the development of microstructural imaging methodology with particular focus on grey matter
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