The sensitivity of diffusion MRI to microstructural properties and experimental factors

Afzali, Maryam; Pieciak, Tomasz; Newman, Sharlene D.; Garyfallidis, Eleftherios; Özarslan, Evren; Cheng, Hu; Jones, Derek K.

doi:10.1016/j.jneumeth.2020.108951

Cited by 70 publications

(56 citation statements)

References 377 publications

(334 reference statements)

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“…Various diffusion indices are used across the literature, including fractional anisotropy (FA), mean diffusivity (MD), number of streamlines, and voxels intersected by streamlines as a proxy of volume. These indices have been associated with microstructural properties and have been used to indicate axonal damage or degeneration (Beaulieu 2002;Ciccarelli et al, 2008;Afzali et al, 2021). Each index was extracted from the 326 studies and the results highlight that some measures are more commonly reported than others (Figure 5).…”

Section: Resultsmentioning

confidence: 99%

White matter variability, cognition, and disorders: a systematic review.

Forkel

Friedrich

Schotten

et al. 2021

Preprint

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Inter-individual differences can inform treatment procedures and - if accounted for - have the potential to significantly improve patient outcomes. However, when studying brain anatomy, these inter-individual variations are commonly unaccounted for, despite reports of differences in gross anatomical features, cross-sectional and connectional anatomy. Brain connections are essential to facilitate functional organisation and, when severed, cause impairments or complete loss of function. Hence the study of cerebral white matter may be an ideal compromise to capture inter-individual variability in structure and function. We reviewed the wealth of studies that associate functions and clinical symptoms with individual tracts using diffusion tractography. Our systematic review indicates that tractography has proven to be a sensitive method in neurology, psychiatry and healthy populations to identify variability and its functional correlates. However, the literature may be biased, as we determined that the most commonly studied tracts are not necessarily those with the highest sensitivity to cognitive functions and pathologies. Additionally, the side of the studied tract is often unreported, thus neglecting functional laterality and hemispheric asymmetries. Finally, we demonstrate that tracts, as we define them, are not usually correlated with only one, but rather multiple cognitive domains or pathologies. While our systematic review identified some methodological caveats, it also suggests that tract-function correlations might be a promising biomarker for precision medicine. It characterises variations in brain anatomy, differences in functional organisation, and predicting resilience or recovery in patients.

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Section: Resultsmentioning

confidence: 99%

White matter variability, cognition, and disorders: a systematic review.

Forkel

Friedrich

Schotten

et al. 2021

Preprint

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“…A key application of QDI is in imaging of the human body where diffusion of free water at body temperature is D FW = 3 × 10 −3 mm 2 s −1 and 0.5 < α < 1 in typical healthy brain tissue [1]. Figure 1 illustrates the family of signal decay curves described by (10). Figure 1a shows the quasi-diffusion signal attenuation parameterised by b for an arbitrary diffusion coefficient, D 1,2 = 1.5 × 10 −3 mm 2 s −1 for 0.1 ≤ α ≤ 0.99 with Figure 1b showing the quasi-diffusion signal attenuation parameterised by q. with ≪ ∆.…”

Section: Quasi-diffusion Imagingmentioning

confidence: 99%

“…A key application of QDI is in imaging of the human body where diffusion of free water at body temperature is 3 10 mm 2 s −1 and 0.5 1 in typical healthy brain tissue [1]. Figure 1 illustrates the family of signal decay curves described by (10).…”

Section: Quasi-diffusion Imagingmentioning

confidence: 99%

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The Mathematics of Quasi-Diffusion Magnetic Resonance Imaging

et al. 2021

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Quasi-diffusion imaging (QDI) is a novel quantitative diffusion magnetic resonance imaging (dMRI) technique that enables high quality tissue microstructural imaging in a clinically feasible acquisition time. QDI is derived from a special case of the continuous time random walk (CTRW) model of diffusion dynamics and assumes water diffusion is locally Gaussian within tissue microstructure. By assuming a Gaussian scaling relationship between temporal () and spatial () fractional exponents, the dMRI signal attenuation is expressed according to a diffusion coefficient, (in mm2 s−1), and a fractional exponent, . Here we investigate the mathematical properties of the QDI signal and its interpretation within the quasi-diffusion model. Firstly, the QDI equation is derived and its power law behaviour described. Secondly, we derive a probability distribution of underlying Fickian diffusion coefficients via the inverse Laplace transform. We then describe the functional form of the quasi-diffusion propagator, and apply this to dMRI of the human brain to perform mean apparent propagator imaging. QDI is currently unique in tissue microstructural imaging as it provides a simple form for the inverse Laplace transform and diffusion propagator directly from its representation of the dMRI signal. This study shows the potential of QDI as a promising new model-based dMRI technique with significant scope for further development.

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“…The resulting models are called MC models and they require the acquisition of multi-shell dMRI data in order to accurately disentangle the contribution of each compartment (Scherrer and Warfield, 2010). Thorough reviews have been dedicated to the design and validation of such models (Jelescu and Budde, 2017), to the sensitivity of MC models to experimental factors and microstructural properties of the described tissues (Afzali et al, 2020), and to the abstraction of these models that allows to obtain a unified theory (Fick et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multi-Tissue Multi-Compartment models of diffusion MRI

Frigo¹,

Fick²,

Zucchelli³

et al. 2021

Preprint

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State-of-the-art multi-compartment microstructural models of diffusion MRI (dMRI) in the human brain have limited capability to model multiple tissues at the same time. In particular, the available techniques that allow this multi-tissue modelling are based on multi-TE acquisitions. In this work we propose a novel multi-tissue formulation of classical multi-compartment models that relies on more common single-TE acquisitions and can be employed in the analysis of previously acquired datasets. We show how modelling multiple tissues provides a new interpretation of the concepts of signal fraction and volume fraction in the context of multi-compartment modelling. The software that allows to inspect single-TE diffusion MRI data with multi-tissue multi-compartment models is included in the publicly available Dmipy Python package.

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The sensitivity of diffusion MRI to microstructural properties and experimental factors

Abstract: Highlights This work reviews different methods for studying brain microstructure using dMRI. Sensitivity to microstructural differences and experimental factors is investigated. Signal representation-based methods and multi-compartment models are explained.

Cited by 70 publications

References 377 publications

White matter variability, cognition, and disorders: a systematic review.

White matter variability, cognition, and disorders: a systematic review.

The Mathematics of Quasi-Diffusion Magnetic Resonance Imaging

Multi-Tissue Multi-Compartment models of diffusion MRI

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