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A two-compartment model of diffusion in white matter, which accounts for intra- and extra-axonal spaces, is associated with two plausible mathematical scenarios: either the intra-axonal axial diffusivity D is higher than the extra-axonal D (Branch 1), or the opposite, i.e. D < D (Branch 2). This duality calls for an independent validation of compartment axial diffusivities, to determine which of the two cases holds. The aim of the present study was to use an intracerebroventricular injection of a gadolinium-based contrast agent to selectively reduce the extracellular water signal in the rat brain, and compare diffusion metrics in the genu of the corpus callosum before and after gadolinium infusion. The diffusion metrics considered were diffusion and kurtosis tensor metrics, as well as compartment-specific estimates of the WMTI-Watson two-compartment model. A strong decrease in genu T and T relaxation times post-Gd was observed (p < 0.001), as well as an increase of 48% in radial kurtosis (p < 0.05), which implies that the relative fraction of extracellular water signal was selectively decreased. This was further supported by a significant increase in intra-axonal water fraction as estimated from the two-compartment model, for both branches (p < 0.01 for Branch 1, p < 0.05 for Branch 2). However, pre-Gd estimates of axon dispersion in Branch 1 agreed better with literature than those of Branch 2. Furthermore, comparison of post-Gd changes in diffusivity and dispersion between data and simulations further supported Branch 1 as the biologically plausible solution, i.e. D > D. This result is fully consistent with other recent measurements of compartment axial diffusivities that used entirely different approaches, such as diffusion tensor encoding.
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http://dx.doi.org/10.1016/j.neuroimage.2018.07.020 | DOI Listing |
Front Hum Neurosci
August 2025
Signal Processing Laboratory (LTS5), École Polytechnique Féderale de Lausanne (EPFL), Lausanne, Switzerland.
Introduction: Absence of language development is a condition encountered across a large range of neurodevelopmental disorders, including a significant proportion of children with autism spectrum disorder. The neurobiological underpinnings of non-verbal ASD (nvASD) remain poorly understood.
Methods: This study employed multimodal MRI to investigate white matter (WM) microstructural abnormalities in nvASD, focusing on language-related pathways.
Comput Biol Med
September 2025
Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain. Electronic address:
Modelling the diffusion-relaxation magnetic resonance (MR) signal obtained from multi-parametric sequences has recently gained immense interest in the community due to new techniques significantly reducing data acquisition time. A preferred approach for examining the diffusion-relaxation MR data is to follow the continuum modelling principle that employs kernels to represent the tissue features, such as the relaxations or diffusion properties. However, constructing reasonable dictionaries with predefined signal components depends on the sampling density of model parameter space, thus leading to a geometrical increase in the number of atoms per extra tissue parameter considered in the model.
View Article and Find Full Text PDFImaging Neurosci (Camb)
July 2025
GE HealthCare Technology & Innovation Center, Niskayuna, NY, United States.
The MAGNUS high-performance MRI gradient platform delivers G = 200-300 mT/m, and SR = 500-750 T/m/s using standard clinical 3.0T system power electronics. This enables the exploration of an expanded diffusion parameter space (b~7-≥30 ms/μm) with reasonable SNR, along with substantially shorter diffusion encoding pulse-widths, echo times, reduced distortion, and blurring from shorter echo spacing.
View Article and Find Full Text PDFImaging Neurosci (Camb)
August 2025
Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
Diffusion MRI (dMRI) plays a crucial role in studying tissue microstructure and fibre orientation. Due to the intricate nature of the dMRI signal, end users require representations that provide a straightforward interpretation. Currently, these representations rely on tissue-average estimations or simplified tissue models and are hence limited in their applicability to pathology.
View Article and Find Full Text PDFNeurooncol Adv
May 2025
IMM-PACT Clinician Scientist Program, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Background: Microstructural tumor characteristics discriminate metastases, glioblastoma, meningioma, and primary CNS lymphoma. We aimed to assess these intracranial neoplasms utilizing multiparametric diffusion imaging as a translational measure of morphology.
Methods: We investigated 101 newly diagnosed intracranial tumors (35 metastases, 34 glioblastomas [GB], 21 meningiomas, 11 primary CNS lymphomas [PCNSL]) with advanced diffusion MRI including Diffusion Tensor Imaging (DTI), Neurite Orientation and Dispersion Density Imaging (NODDI), and Diffusion Microstructure Imaging (DMI).