Modeling diffusion of intracellular metabolites in the mouse brain up to very high diffusion‐weighting: Diffusion in long fibers (almost) accounts for non‐monoexponential attenuation

Palombo, Marco; Ligneul, Clémence; Valette, Julien

doi:10.1002/mrm.26548

Cited by 53 publications

(103 citation statements)

References 34 publications

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“…(Palombo et al, 2017) have also demonstrated diffusion of NAA and other metabolites can be modelled as occurring in long, cylindrical fibers, having a D a,‖ of 0.33 µm 2 /ms. The diffusion weighted signals of other metabolites were also fit well by assuming long cylindrical processes (Najac et al, 2016; Najac et al, 2014; Palombo et al, 2017). Since metabolites have differences in molecular size, affinity to charged surfaces, and potential ambiguity in compartment selectivity, the intra-axonal diffusivity of water cannot be unequivocally extrapolated from metabolite diffusivities.…”

Section: Validating Diffusion Modelsmentioning

confidence: 93%

“…(Ronen et al, 2014), accounting for both the macro- and microscopic curvature of the human corpus callosum, measured a slightly larger D a,‖ (0.51 µm 2 /ms) putting it in the range of 60–70% of aqueous NAA. (Palombo et al, 2017) have also demonstrated diffusion of NAA and other metabolites can be modelled as occurring in long, cylindrical fibers, having a D a,‖ of 0.33 µm 2 /ms. The diffusion weighted signals of other metabolites were also fit well by assuming long cylindrical processes (Najac et al, 2016; Najac et al, 2014; Palombo et al, 2017).…”

Section: Validating Diffusion Modelsmentioning

confidence: 97%

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Design and Validation of Diffusion MRI Models of White Matter

2017

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Diffusion MRI is arguably the method of choice for characterizing white matter microstructure in vivo. Over the typical duration of diffusion encoding, the displacement of water molecules is conveniently on a length scale similar to that of the underlying cellular structures. Moreover, water molecules in white matter are largely compartmentalized which enables biologically-inspired compartmental diffusion models to characterize and quantify the true biological microstructure. A plethora of white matter models have been proposed. However, overparameterization and mathematical fitting complications encourage the introduction of simplifying assumptions that vary between different approaches. These choices impact the quantitative estimation of model parameters with potential detriments to their biological accuracy and promised specificity. First, we review biophysical white matter models in use and recapitulate their underlying assumptions and realms of applicability. Second, we present up-to-date efforts to validate parameters estimated from biophysical models. Simulations and dedicated phantoms are useful in assessing the performance of models when the ground truth is known. However, the biggest challenge remains the validation of the “biological accuracy” of estimated parameters. Complementary techniques such as microscopy of fixed tissue specimens have facilitated direct comparisons of estimates of white matter fiber orientation and densities. However, validation of compartmental diffusivities remains challenging, and complementary MRI-based techniques such as alternative diffusion encodings, compartment-specific contrast agents and metabolites have been used to validate diffusion models. Finally, white matter injury and disease pose additional challenges to modeling, which are also discussed. This review aims to provide an overview of the current state of models and their validation and to stimulate further research in the field to solve the remaining open questions and converge towards consensus.

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Section: Validating Diffusion Modelsmentioning

confidence: 93%

Section: Validating Diffusion Modelsmentioning

confidence: 97%

Design and Validation of Diffusion MRI Models of White Matter

2017

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“…At the extreme end of the spectrum, for structures so short that

scriptl ≫ \sqrt{D δ}

, entering into regime C, no curvature is tolerated if the tail q −1 is to appear. These considerations will have to be revisited if one measures the diffusion of molecules other than water, due to differences in their diffusion characteristics [48, 49, 50]. …”

Section: Resultsmentioning

confidence: 99%

Influence of the Size and Curvedness of Neural Projections on the Orientationally Averaged Diffusion MR Signal

et al. 2018

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Neuronal and glial projections can be envisioned to be tubes of infinitesimal diameter as far as diffusion magnetic resonance (MR) measurements via clinical scanners are concerned. Recent experimental studies indicate that the decay of the orientationally-averaged signal in white-matter may be characterized by the power-law, Ē(q) ∝ q−1, where q is the wavenumber determined by the parameters of the pulsed field gradient measurements. One particular study by McKinnon et al. [1] reports a distinctively faster decay in gray-matter. Here, we assess the role of the size and curvature of the neurites and glial arborizations in these experimental findings. To this end, we studied the signal decay for diffusion along general curves at all three temporal regimes of the traditional pulsed field gradient measurements. We show that for curvy projections, employment of longer pulse durations leads to a disappearance of the q−1 decay, while such decay is robust when narrow gradient pulses are used. Thus, in clinical acquisitions, the lack of such a decay for a fibrous specimen can be seen as indicative of fibers that are curved. We note that the above discussion is valid for an intermediate range of q-values as the true asymptotic behavior of the signal decay is Ē(q) ∝ q−4 for narrow pulses (through Debye-Porod law) or steeper for longer pulses. This study is expected to provide insights for interpreting the diffusion-weighted images of the central nervous system and aid in the design of acquisition strategies.

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“…In recent works performed in healthy animals, we have proposed that high diffusion-weighting 85 DW-MRS data primarily reflect the radius of brain cell fibers (dendrites, axons, astrocytic processes…) (Palombo et al, 2017b), while long diffusion times data primarily reflect fiber long-range structure, in particular complexity and length (Palombo et al, 2016). Here we measured signal attenuation of reliable metabolites (CRLB<5 %) up to a diffusion-weighting b=50 ms/µm 2 , at fixed diffusion time t d =53.2 ms, which is well suited to probe restriction in 90 fibers' transverse plane.…”

Section: Dw-mrs Reveals Myo-inositol As a Specific Diffusion Marker Omentioning

confidence: 99%

“…4). High diffusion-weighting data were analyzed using analytical models of diffusion in randomly oriented cylinders (Palombo et al, 2017b), slightly modified to also account for diffusion in spherical soma representing 20% 120 of the total cell volume (Palombo et al, 2018a, b). We extracted fiber radii of 0.58±0.02 µm for the control group versus 0.85±0.02 µm for the CNTF group (+47%), and soma radii of 3.80±0.06 µm for the control group versus 4.66±0.07 µm for the CNTF group (+23%) ( Supplementary Table S2).…”

Section: Diffusion Modeling Allows Quantifying Structural Alterationsmentioning

confidence: 99%

Diffusion-weighted magnetic resonance spectroscopy enables cell-specific monitoring of astrocyte reactivity in vivo

Ligneul

Hernández-Garzón

Palombo

et al. 2018

Preprint

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The diffusion of brain intracellular metabolites, as measured using diffusion-weighted magnetic resonance spectroscopy in vivo, is thought to specifically depend on the cellular structure constraining them. However, it has never been established that variations of metabolite diffusion, e.g. as observed in some diseases, could indeed be linked to alterations of cellular morphology. Here we demonstrate, in a mouse model of reactive astrocytes, that advanced diffusion-weighted magnetic resonance spectroscopy acquisition and modeling techniques enable non-invasive detection of reactive astrocyte hypertrophy (increased soma radius, increased fiber radius and length), as inferred from variations of myo-inositol diffusion, and as confirmed by confocal microscopy ex vivo. This establishes that specific alterations of intracellular metabolite diffusion can be measured and related to cell-specific morphological alterations. Furthermore, as reactive astrocytes are a hallmark of many brain pathologies, this work opens exciting perspectives for neuroscience and clinical research.

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Modeling diffusion of intracellular metabolites in the mouse brain up to very high diffusion‐weighting: Diffusion in long fibers (almost) accounts for non‐monoexponential attenuation

Cited by 53 publications

References 34 publications

Design and Validation of Diffusion MRI Models of White Matter

Design and Validation of Diffusion MRI Models of White Matter

Influence of the Size and Curvedness of Neural Projections on the Orientationally Averaged Diffusion MR Signal

Diffusion-weighted magnetic resonance spectroscopy enables cell-specific monitoring of astrocyte reactivity in vivo

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