Grey matter | EPFL Graph Search

Grey matter is a major component of the central nervous system, consisting of neuronal cell bodies, neuropil (dendrites and unmyelinated axons), glial cells (astrocytes and oligodendrocytes), synapses, and capillaries. Grey matter is distinguished from white matter in that it contains numerous cell bodies and relatively few myelinated axons, while white matter contains relatively few cell bodies and is composed chiefly of long-range myelinated axons. The colour difference arises mainly from the whiteness of myelin. In living tissue, grey matter actually has a very light grey colour with yellowish or pinkish hues, which come from capillary blood vessels and neuronal cell bodies. Structure Grey matter refers to unmyelinated neurons and other cells of the central nervous system. It is present in the brain, brainstem and cerebellum, and present throughout the spinal cord. Grey matter is distributed at the surface of the cerebral hemispheres (cerebral cortex) and of the cerebel

Neurite Exchange Imaging ((NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

Alexandre Jean François Marie De Skowronski, Ileana Ozana Jelescu

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Karger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

Cerebral Gray and White Matter Involvement in Anorexia Nervosa Evaluated by T1, T2, and T2*Mapping

Nurten Ceren Askin, Tobias Kober, François Lazeyras

BACKGROUND AND PURPOSE Changes in the brain composition of anorexics could potentially be expected, opening the door to new imaging approaches where quantitative and qualitative MRI have a role. Our purpose was to investigate anorexia-related brain dehydration and myelin depletion by analyzing T1, T2, and T2* relaxation times of different brain structures in anorexics and controls. METHODS Thirty-eight anorexic female patients (mean age, 26.2 years; age range, 16.2-48.7 years; mean BMI, 14.5 kg/m(2); BMI range, 10.0-18.4 kg/m(2)) underwent brain MRI between August 2014 and August 2018. Controls were 16 healthy females (mean age, 28.0 years; age range, 22.3-34.7 years; mean BMI, 20.9 kg/m(2); BMI range, 18.4-26.6 kg/m(2)). T1, T2, and T2* relaxation times were obtained for different brain structures in anorexics and controls as part of this retrospective case-control study. RESULTS The T1 relaxation times of gray and white matter were significantly lower in anorexics (P = .009), whereas the T2 relaxation times of gray matter were higher (P < .001). There were no statistically significant differences in gray matter T2* relaxation times or in white matter T2 and T2* relaxation times between anorexics and controls. Occipital lobe gray matter showed the shortest T1, T2, and T2* relaxation times of all brain regions (P < .05). CONCLUSIONS T1 shortening in anorexics suggests both dehydration and myelin loss, whereas T2 prolongation points toward myelin loss (myelin water has lower T2), which seems to be less discernible in white matter. Shorter overall relaxation times in the most posterior regions of the brain suggest higher iron content.

WILEY2019

Global tractography with embedded anatomical priors for quantitative connectivity analysis

Alessandro Daducci, Alia Lemkaddem, Didrik Hans Malcolm Skiöldebrand, Jean-Philippe Thiran

Tractography algorithms provide us with the ability to non-invasively reconstruct fiber pathways in the white matter (WM) by exploiting the directional information described with diffusion magnetic resonance. These methods could be divided into two major classes, local and global. Local methods reconstruct each fiber tract iteratively by considering only directional information at the voxel level and its neighborhood. Global methods, on the other hand, reconstruct all the fiber tracts of the whole brain simultaneously by solving a global energy minimization problem. The latter have shown improvements compared to previous techniques but these algorithms still suffer from an important shortcoming that is crucial in the context of brain connectivity analyses. As no anatomical priors are usually considered during the reconstruction process, the recovered fiber tracts are not guaranteed to connect cortical regions and, as a matter of fact, most of them stop prematurely in the WM; this violates important properties of neural connections, which are known to originate in the gray matter (GM) and develop in the WM. Hence, this shortcoming poses serious limitations for the use of these techniques for the assessment of the structural connectivity between brain regions and, de facto, it can potentially bias any subsequent analysis. Moreover, the estimated tracts are not quantitative, every fiber contributes with the same weight toward the predicted diffusion signal. In this work, we propose a novel approach for global tractography that is specifically designed for connectivity analysis applications which: (i) explicitly enforces anatomical priors of the tracts in the optimization and (ii) considers the effective contribution of each of them, i.e., volume, to the acquired diffusion magnetic resonance imaging (MRI) image. We evaluated our approach on both a realistic diffusion MRI phantom and in vivo data, and also compared its performance to existing tractography algorithms.

2014