In Vivo Brain Water Determination by T1 Measurements: Effect of Total Water Content, Hydration Fraction, and Field Strength

Fatouros, Panos P.; Marmarou, Anthony; Kraft, Kenneth A.; Inao, Suguru; Schwarz, Frederick P.

doi:10.1002/mrm.1910170212

Cited by 130 publications

(117 citation statements)

References 17 publications

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“…Further, progressive increases in diffusion anisotropy (the degree to which water diffusion is oriented along a single dominant orientation), as measured by diffusion tensor (DT)-MRI, offers additional insight into white matter development, reflecting changes in axonal fiber size, density, tract coherence, as well as membrane structure and permeability (Zhang et al, 2005;Provenzale et al, 2007). Unfortunately, changes in these measures may not be specific to myelin content alteration (Fatouros et al, 1991;Beaulieu, 2002).…”

Section: Introductionmentioning

confidence: 99%

Mapping Infant Brain Myelination with Magnetic Resonance Imaging

Deoni¹,

Mercure²,

Blasi³

et al. 2011

J. Neurosci.

427

344

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Myelination, the elaboration of myelin surrounding neuronal axons, is essential for normal brain function. The development of the myelin sheath enables rapid synchronized communication across the neural systems responsible for higher order cognitive functioning. Despite this critical role, quantitative visualization of myelination in vivo is not possible with current neuroimaging techniques including diffusion tensor and structural magnetic resonance imaging (MRI). Although these techniques offer insight into structural maturation, they reflect several different facets of development, e.g., changes in axonal size, density, coherence, and membrane structure; lipid, protein, and macromolecule content; and water compartmentalization. Consequently, observed signal changes are ambiguous, hindering meaningful inferences between imaging findings and metrics of learning, behavior or cognition. Here we present the first quantitative study of myelination in healthy human infants, from 3 to 11 months of age. Using a new myelin-specific MRI technique, we report a spatiotemporal pattern beginning in the cerebellum, pons, and internal capsule; proceeding caudocranially from the splenium of the corpus callosum and optic radiations (at 3-4 months); to the occipital and parietal lobes (at 4 -6 months); and then to the genu of the corpus callosum and frontal and temporal lobes (at 6 -8 months). Our results also offer preliminary evidence of hemispheric myelination rate differences. This work represents a significant step forward in our ability to appreciate the fundamental process of myelination, and provides the first ever in vivo visualization of myelin maturation in healthy human infancy.

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Section: Introductionmentioning

confidence: 99%

Mapping Infant Brain Myelination with Magnetic Resonance Imaging

Deoni¹,

Mercure²,

Blasi³

et al. 2011

J. Neurosci.

427

344

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show abstract

“…In addition, DSC measurements have been employed in determining the rate constants and activation energy of irreversible transitions for proteins from the dependence of the transition properties on scan rate [26]. On a more applied level, DSC measurements have been employed in the determination of the amount of free water in brain tissue [27], of the stability of proteins in rabbit brain membrane fractions [28], of the effects of manufacturing operations on proteins in foods [29,30], and of new methods to improve freeze-drying of labile biological material [31].…”

Section: Introductionmentioning

confidence: 99%

Measurement and analysis of results obtained on biological substances with differential scanning calorimetry (IUPAC Technical Report)

Hinz

Schwarz

2001

Pure and Applied Chemistry

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Republication or reproduction of this report or its storage and/or dissemination by electronic means is permitted without the need for formal IUPAC permission on condition that an acknowledgmentAbstract: Differential scanning calorimeters (DSCs) have been widely used to determine the thermodynamics of phase transitions and conformational changes in biological systems including proteins, nucleic acid sequences, and lipid assemblies. DSCs monitor the temperature difference between two vessels, one containing the biological solution and the other containing a reference solution, as a function of temperature at a given scan rate. Recommendations for DSC measurement procedures, calibration procedures, and procedures for testing the performance of the DSC are described. Analysis of the measurements should include a correction for the time response of the instrument and conversion of the power vs. time curve to a heat capacity vs. temperature plot. Thermodynamic transition models should only be applied to the analysis of the heat capacity curves if the model-derived transition temperatures and enthalpies are independent of the DSC scan rate. Otherwise, kinetic models should be applied to the analysis of the data. Application of thermodynamic transition models involving two states, two states and dissociation, and three states to the heat capacity vs. temperature data are described. To check the operating performance with standard DSCs, samples of 1 to 10 mg mL -1 solutions of hen egg white lysozyme in 0.1 M HCl-glycine buffer at pH = 2.4 ± 0.1 were sent to six different DSC laboratories worldwide. The values obtained from proper measurements and application of a two-state transition model yielded an average unfolding transition temperature for lysozyme of 331.2 K with values ranging from 329.4 to 331.9 K, and an average transition enthalpy of 405 kJ mol -1 with values ranging from 377 to 439 kJ mol -1 . It is recommended that the reporting of DSC results be specific with regard to the composition of the solution, the operating conditions and calibrations of the DSC, determination of base lines that may be model-dependent, and the model used in the analysis of the data.

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“…This bias can arise because of components that have not been explicitly included in the model. However, it has previously been shown that macromolecular and iron components, which are rather well captured by MT and R 2 * respectively,31, 32 are the dominant contributors to R 1 11, 15, 16, 33, 34, 35. Any orientation‐dependent or higher‐order relationships that exist between the qMRI maps are not captured by the model either, and may also be a source of bias 36…”

Section: Discussionmentioning

confidence: 99%

Synthetic quantitative MRI through relaxometry modelling

2016

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Quantitative MRI (qMRI) provides standardized measures of specific physical parameters that are sensitive to the underlying tissue microstructure and are a first step towards achieving maps of biologically relevant metrics through in vivo histology using MRI. Recently proposed models have described the interdependence of qMRI parameters. Combining such models with the concept of image synthesis points towards a novel approach to synthetic qMRI, in which maps of fundamentally different physical properties are constructed through the use of biophysical models. In this study, the utility of synthetic qMRI is investigated within the context of a recently proposed linear relaxometry model. Two neuroimaging applications are considered. In the first, artefact‐free quantitative maps are synthesized from motion‐corrupted data by exploiting the over‐determined nature of the relaxometry model and the fact that the artefact is inconsistent across the data. In the second application, a map of magnetization transfer (MT) saturation is synthesized without the need to acquire an MT‐weighted volume, which directly leads to a reduction in the specific absorption rate of the acquisition. This feature would be particularly important for ultra‐high field applications. The synthetic MT map is shown to provide improved segmentation of deep grey matter structures, relative to segmentation using T 1‐weighted images or R 1 maps. The proposed approach of synthetic qMRI shows promise for maximizing the extraction of high quality information related to tissue microstructure from qMRI protocols and furthering our understanding of the interrelation of these qMRI parameters.

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In Vivo Brain Water Determination by T₁ Measurements: Effect of Total Water Content, Hydration Fraction, and Field Strength

Cited by 130 publications

References 17 publications

Mapping Infant Brain Myelination with Magnetic Resonance Imaging

Mapping Infant Brain Myelination with Magnetic Resonance Imaging

Measurement and analysis of results obtained on biological substances with differential scanning calorimetry (IUPAC Technical Report)

Synthetic quantitative MRI through relaxometry modelling

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