Neurite density from magnetic resonance diffusion measurements at ultrahigh field: Comparison with light microscopy and electron microscopy

By S. Jespersen, C. Bjarkam, J. Nyengaard, M. Chakravarty, B. Hansen, T. Vosegaard, L. Østergaard, D. Yablonskiy, N. C. Nielsen, P. Vestergaard-Poulsen. NeuroImage, 2009

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Due to its unique sensitivity to tissue microstructure, diffusion-weighted magnetic resonance imaging (MRI) has found many applications in clinical and fundamental science. With few exceptions, a more precise correspondence between physiological or biophysical properties and the obtained diffusion parameters remain uncertain due to lack of specificity. In this work, we address this problem by comparing diffusion parameters of a recently introduced model for water diffusion in brain matter to light microscopy and quantitative electron microscopy. Specifically, we compare diffusion model predictions of neurite density in rats to optical myelin staining intensity and stereological estimation of neurite volume fraction using electron microscopy. We find that the diffusion model describes data better and that its parameters show stronger correlation with optical and electron microscopy, and thus reflect myelinated neurite density better than the more frequently used diffusion tensor imaging (DTI) and cumulant expansion methods. Furthermore, the estimated neurite orientations capture dendritic architecture more faithfully than DTI diffusion ellipsoids.

Subfields: Nuclear and High Energy Physics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/abe2fd767876dc116d7d33e6a05740248c8b523a

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Citations in the corpus

Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison (2012, unknown): “…MRI field (Alexander, 2008; Alexander et al., 2010; Assaf and Basser, 2005; Assaf et al., 2008; Barazany et al., 2009; Behrens et al., 2003; Jespersen et al., 2007, 2010; Zhang et al., 2011a,b) is towards more direct microstructure imaging by fitting more descriptive models of tissue that…”
NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain (2012, background): “The map of νic shows the expected pattern of neurite density (Jespersen et al., 2010).”
Imaging brain microstructure with diffusion MRI: practicality and applications (2019, methodology): “OGSE‐frequency dependence in both the intra‐axonal and extracellular spaces,(109) and good agreement of myelinated neurite fractions from dMRI and histology.(4) Image analysis of fixed tissue has also been used to quantify levels of axonal orientation dispersion.”
SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI (2019, unknown): “Therefore, from the water diffusion standpoint, we can consider branching neurites as a collection of individual branches (or cylinders/sticks), randomly oriented in space (Jespersen et al., 2012; Hansen et al., 2013).”
Multi-compartment microscopic diffusion imaging (2016, background): “More recently, Jespersen et al. (2007, 2010) estimated the neurite density and per-dendrite/ axon diffusion coefficients in the presence of complex orientation distributions using loworder spherical harmonics, which, however, exclude mixtures of Dirac masses and are impractical for crossings of…”
White matter characterization with diffusional kurtosis imaging (2011, methodology): “7 for the axonal volume density of the commisura anterior of rat brain based on AMG staining (Jespersen et al., 2010).”