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Papazoglou S, Ashtarayeh M, Oeschger JM, Callaghan MF, Does MD, Mohammadi S. Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy. NMR IN BIOMEDICINE 2024; 37:e5070. [PMID: 38098204 DOI: 10.1002/nbm.5070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/25/2023] [Accepted: 10/19/2023] [Indexed: 02/17/2024]
Abstract
Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction (f AW ), which in turn is key for noninvasive estimation of the axonal volume fraction (f A ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation off AW . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics off A . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmentalT 2 and can substantially enhance the comparability between EM- and DWI-based metrics off A . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-basedf A . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.
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Affiliation(s)
- Sebastian Papazoglou
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany
| | - Mohammad Ashtarayeh
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Malte Oeschger
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Mark D Does
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Chau Loo Kung G, Knowles JK, Batra A, Ni L, Rosenberg J, McNab JA. Quantitative MRI reveals widespread, network-specific myelination change during generalized epilepsy progression. Neuroimage 2023; 280:120312. [PMID: 37574120 PMCID: PMC11095339 DOI: 10.1016/j.neuroimage.2023.120312] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/17/2023] [Accepted: 08/04/2023] [Indexed: 08/15/2023] Open
Abstract
Activity-dependent myelination is a fundamental mode of brain plasticity which significantly influences network function. We recently discovered that absence seizures, which occur in multiple forms of generalized epilepsy, can induce activity-dependent myelination, which in turn promotes further progression of epilepsy. Structural alterations of myelin are likely to be widespread, given that absence seizures arise from an extensive thalamocortical network involving frontoparietal regions of the bilateral hemispheres. However, the temporal course and spatial extent of myelin plasticity is unknown, due to limitations of gold-standard histological methods such as electron microscopy (EM). In this study, we leveraged magnetization transfer and diffusion MRI for estimation of g-ratios across major white matter tracts in a mouse model of generalized epilepsy with progressive absence seizures. EM was performed on the same brains after MRI. After seizure progression, we found increased myelination (decreased g-ratios) throughout the anterior portion (genu-to-body) of the corpus callosum but not in the posterior portion (body-splenium) nor in the fornix or the internal capsule. Curves obtained from averaging g-ratio values at every longitudinal point of the corpus callosum were statistically different with p<0.001. Seizure-associated myelin differences found in the corpus callosum body with MRI were statistically significant (p = 0.0027) and were concordant with EM in the same region (p = 0.01). Notably, these differences were not detected by diffusion tensor imaging. This study reveals widespread myelin structural change that is specific to the absence seizure network. Furthermore, our findings demonstrate the potential utility and importance of MRI-based g-ratio estimation to non-invasively detect myelin plasticity.
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Affiliation(s)
- Gustavo Chau Loo Kung
- Bioengineering Department, Stanford University, 443 Via Ortega, Stanford, CA 94305, United States; Radiology Department, Stanford University, 1201 Welch Rd, Stanford, CA 94305, United States.
| | - Juliet K Knowles
- Neurology Department, 1701 Page Mill Road, Palo Alto, CA 94304, United States.
| | - Ankita Batra
- Neurology Department, 1701 Page Mill Road, Palo Alto, CA 94304, United States.
| | - Lijun Ni
- Neurology Department, SIM1 G3035, Stanford, CA 94305, United States.
| | - Jarrett Rosenberg
- Radiology Department, Stanford University, 1201 Welch Rd, Stanford, CA 94305, United States.
| | - Jennifer A McNab
- Radiology Department, Stanford University, 1201 Welch Rd, Stanford, CA 94305, United States.
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3
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Drobyshevsky A, Synowiec S, Goussakov I, Lu J, Gascoigne D, Aksenov DP, Yarnykh V. Temporal trajectories of normal myelination and axonal development assessed by quantitative macromolecular and diffusion MRI: Ultrastructural and immunochemical validation in a rabbit model. Neuroimage 2023; 270:119974. [PMID: 36848973 PMCID: PMC10103444 DOI: 10.1016/j.neuroimage.2023.119974] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/15/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023] Open
Abstract
INTRODUCTION Quantitative and non-invasive measures of brain myelination and maturation during development are of great importance to both clinical and translational research communities. While the metrics derived from diffusion tensor imaging, are sensitive to developmental changes and some pathologies, they remain difficult to relate to the actual microstructure of the brain tissue. The advent of advanced model-based microstructural metrics requires histological validation. The purpose of the study was to validate novel, model-based MRI techniques, such as macromolecular proton fraction mapping (MPF) and neurite orientation and dispersion indexing (NODDI), against histologically derived indexes of myelination and microstructural maturation at various stages of development. METHODS New Zealand White rabbit kits underwent serial in-vivo MRI examination at postnatal days 1, 5, 11, 18, and 25, and as adults. Multi-shell, diffusion-weighted experiments were processed to fit NODDI model to obtain estimates, intracellular volume fraction (ICVF) and orientation dispersion index (ODI). Macromolecular proton fraction (MPF) maps were obtained from three source (MT-, PD-, and T1-weighted) images. After MRI sessions, a subset of animals was euthanized and regional samples of gray and white matter were taken for western blot analysis, to determine myelin basic protein (MBP), and electron microscopy, to estimate axonal, myelin fractions and g-ratio. RESULTS MPF of white matter regions showed a period of fast growth between P5 and P11 in the internal capsule, with a later onset in the corpus callosum. This MPF trajectory was in agreement with levels of myelination in the corresponding brain region, as assessed by western blot and electron microscopy. In the cortex, the greatest increase of MPF occurred between P18 and P26. In contrast, myelin, according to MBP western blot, saw the largest hike between P5 and P11 in the sensorimotor cortex and between P11 and P18 in the frontal cortex, which then seemingly plateaued after P11 and P18 respectively. G-ratio by MRI markers decreased with age in the white matter. However, electron microscopy suggest a relatively stable g-ratio throughout development. CONCLUSION Developmental trajectories of MPF accurately reflected regional differences of myelination rate in different cortical regions and white matter tracts. MRI-derived estimation of g-ratio was inaccurate during early development, likely due to the overestimation of axonal volume fraction by NODDI due to the presence of a large proportion of unmyelinated axons.
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Affiliation(s)
- Alexander Drobyshevsky
- Department of Pediatrics, NorthShore University HealthSystem Research Institute, Evanston, IL, USA.
| | - Sylvia Synowiec
- Department of Pediatrics, NorthShore University HealthSystem Research Institute, Evanston, IL, USA
| | - Ivan Goussakov
- Department of Pediatrics, NorthShore University HealthSystem Research Institute, Evanston, IL, USA
| | - Jing Lu
- Department of Pediatrics, University of Chicago, Chicago, IL, USA
| | - David Gascoigne
- Center for Basic MR Research, NorthShore University HealthSystem Research Institute, Evanston, IL, USA
| | - Daniil P Aksenov
- Center for Basic MR Research, NorthShore University HealthSystem Research Institute, Evanston, IL, USA
| | - Vasily Yarnykh
- Department of Radiology, University of Washington, Seattle, WA, USA
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Berg RC, Menegaux A, Amthor T, Gilbert G, Mora M, Schlaeger S, Pongratz V, Lauerer M, Sorg C, Doneva M, Vavasour I, Mühlau M, Preibisch C. Comparing myelin-sensitive magnetic resonance imaging measures and resulting g-ratios in healthy and multiple sclerosis brains. Neuroimage 2022; 264:119750. [PMID: 36379421 PMCID: PMC9931395 DOI: 10.1016/j.neuroimage.2022.119750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022] Open
Abstract
The myelin concentration and the degree of myelination of nerve fibers can provide valuable information on the integrity of human brain tissue. Magnetic resonance imaging (MRI) of myelin-sensitive parameters can help to non-invasively evaluate demyelinating diseases such as multiple sclerosis (MS). Several different myelin-sensitive MRI methods have been proposed to determine measures of the degree of myelination, in particular the g-ratio. However, variability in underlying physical principles and different biological models influence measured myelin concentrations, and consequently g-ratio values. We therefore investigated similarities and differences between five different myelin-sensitive MRI measures and their effects on g-ratio mapping in the brains of both MS patients and healthy volunteers. We compared two different estimates of the myelin water fraction (MWF) as well as the inhomogeneous magnetization transfer ratio (ihMTR), magnetization transfer saturation (MTsat), and macromolecular tissue volume (MTV) in 13 patients with MS and 14 healthy controls. In combination with diffusion-weighted imaging, we derived g-ratio parameter maps for each of the five different myelin measures. The g-ratio values calculated from different myelin measures varied strongly, especially in MS lesions. While, compared to normal-appearing white matter, MTsat and one estimate of the MWF resulted in higher g-ratio values within lesions, ihMTR, MTV, and the second MWF estimate resulted in lower lesion g-ratio values. As myelin-sensitive measures provide rough estimates of myelin content rather than absolute myelin concentrations, resulting g-ratio values strongly depend on the utilized myelin measure and model used for g-ratio mapping. When comparing g-ratio values, it is, thus, important to utilize the same MRI methods and models or to consider methodological differences. Particular caution is necessary in pathological tissue such as MS lesions.
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Affiliation(s)
- Ronja C. Berg
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Corresponding author at: Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Diagnostic and Interventional Neuroradiology, Ismaninger Str. 22, 81675, München, Germany. (R.C. Berg)
| | - Aurore Menegaux
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | | | | | - Maria Mora
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany
| | - Sarah Schlaeger
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany
| | - Viola Pongratz
- Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | - Markus Lauerer
- Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | - Christian Sorg
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany,Technical University of Munich, School of Medicine, Department of Psychiatry, Munich, Germany
| | | | - Irene Vavasour
- University of British Columbia, Department of Radiology, Vancouver, BC, Canada
| | - Mark Mühlau
- Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
| | - Christine Preibisch
- Technical University of Munich, School of Medicine, Department of Diagnostic and Interventional Neuroradiology, Munich, Germany,Technical University of Munich, School of Medicine, Department of Neurology, Munich, Germany,Technical University of Munich, School of Medicine, TUM Neuroimaging Center, Munich, Germany
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5
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Clark IA, Mohammadi S, Callaghan MF, Maguire EA. Conduction velocity along a key white matter tract is associated with autobiographical memory recall ability. eLife 2022; 11:79303. [PMID: 36166372 PMCID: PMC9514844 DOI: 10.7554/elife.79303] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 09/12/2022] [Indexed: 12/01/2022] Open
Abstract
Conduction velocity is the speed at which electrical signals travel along axons and is a crucial determinant of neural communication. Inferences about conduction velocity can now be made in vivo in humans using a measure called the magnetic resonance (MR) g-ratio. This is the ratio of the inner axon diameter relative to that of the axon plus the myelin sheath that encases it. Here, in the first application to cognition, we found that variations in MR g-ratio, and by inference conduction velocity, of the parahippocampal cingulum bundle were associated with autobiographical memory recall ability in 217 healthy adults. This tract connects the hippocampus with a range of other brain areas. We further observed that the association seemed to be with inner axon diameter rather than myelin content. The extent to which neurites were coherently organised within the parahippocampal cingulum bundle was also linked with autobiographical memory recall ability. Moreover, these findings were specific to autobiographical memory recall and were not apparent for laboratory-based memory tests. Our results offer a new perspective on individual differences in autobiographical memory recall ability, highlighting the possible influence of specific white matter microstructure features on conduction velocity when recalling detailed memories of real-life past experiences.
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Affiliation(s)
- Ian A Clark
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Siawoosh Mohammadi
- Institute of Systems Neuroscience, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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6
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Yendiki A, Aggarwal M, Axer M, Howard AF, van Cappellen van Walsum AM, Haber SN. Post mortem mapping of connectional anatomy for the validation of diffusion MRI. Neuroimage 2022; 256:119146. [PMID: 35346838 PMCID: PMC9832921 DOI: 10.1016/j.neuroimage.2022.119146] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 03/02/2022] [Accepted: 03/23/2022] [Indexed: 01/13/2023] Open
Abstract
Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.
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Affiliation(s)
- Anastasia Yendiki
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States,Corresponding author (A. Yendiki)
| | - Manisha Aggarwal
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Markus Axer
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine, Jülich, Germany,Department of Physics, University of Wuppertal Germany
| | - Amy F.D. Howard
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Anne-Marie van Cappellen van Walsum
- Department of Medical Imaging, Anatomy, Radboud University Medical Center, Nijmegen, the Netherland,Cognition and Behaviour, Donders Institute for Brain, Nijmegen, the Netherland
| | - Suzanne N. Haber
- Department of Pharmacology and Physiology, University of Rochester, Rochester, NY, United States,McLean Hospital, Belmont, MA, United States
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7
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Gyori NG, Clark CA, Alexander DC, Kaden E. On the potential for mapping apparent neural soma density via a clinically viable diffusion MRI protocol. Neuroimage 2021; 239:118303. [PMID: 34174390 PMCID: PMC8363942 DOI: 10.1016/j.neuroimage.2021.118303] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 06/16/2021] [Accepted: 06/22/2021] [Indexed: 12/14/2022] Open
Abstract
B-tensor encoding enables estimation of spherical cellular structures in the brain. Spherical compartments may provide markers for apparent neural soma density. Model parameters can be estimated in a fast and robust way using deep learning. Practical acquisition times are achievable on widely available clinical scanners.
Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively. Today, model-based techniques are widely available and used for white matter characterisation where their development is relatively mature. Conversely, tissue modelling in grey matter is more challenging, and no generally accepted models exist. With advances in measurement technology and modelling efforts, a clinically viable technique that reveals salient features of grey matter microstructure, such as the density of quasi-spherical cell bodies and quasi-cylindrical cell projections, is an exciting prospect. As a step towards capturing the microscopic architecture of grey matter in clinically feasible settings, this work uses a biophysical model that is designed to disentangle the diffusion signatures of spherical and cylindrical structures in the presence of orientation heterogeneity, and takes advantage of B-tensor encoding measurements, which provide additional sensitivity compared to standard single diffusion encoding sequences. For the fast and robust estimation of microstructural parameters, we leverage recent advances in machine learning and replace conventional fitting techniques with an artificial neural network that fits complex biophysical models within seconds. Our results demonstrate apparent markers of spherical and cylindrical geometries in healthy human subjects, and in particular an increased volume fraction of spherical compartments in grey matter compared to white matter. We evaluate the extent to which spherical and cylindrical geometries may be interpreted as correlates of neural soma and neural projections, respectively, and quantify parameter estimation errors in the presence of various departures from the modelling assumptions. While further work is necessary to translate the ideas presented in this work to the clinic, we suggest that biomarkers focussing on quasi-spherical cellular geometries may be valuable for the enhanced assessment of neurodevelopmental disorders and neurodegenerative diseases.
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Affiliation(s)
- Noemi G Gyori
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.
| | - Christopher A Clark
- Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Enrico Kaden
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
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8
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Bouhrara M, Kim RW, Khattar N, Qian W, Bergeron CM, Melvin D, Zukley LM, Ferrucci L, Resnick SM, Spencer RG. Age-related estimates of aggregate g-ratio of white matter structures assessed using quantitative magnetic resonance neuroimaging. Hum Brain Mapp 2021; 42:2362-2373. [PMID: 33595168 PMCID: PMC8090765 DOI: 10.1002/hbm.25372] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 01/21/2021] [Accepted: 02/04/2021] [Indexed: 12/19/2022] Open
Abstract
The g‐ratio, defined as the inner‐to‐outer diameter of a myelinated axon, is associated with the speed of nerve impulse conduction, and represents an index of axonal myelination and integrity. It has been shown to be a sensitive and specific biomarker of neurodevelopment and neurodegeneration. However, there have been very few magnetic resonance imaging studies of the g‐ratio in the context of normative aging; characterizing regional and time‐dependent cerebral changes in g‐ratio in cognitively normal subjects will be a crucial step in differentiating normal from abnormal microstructural alterations. In the current study, we investigated age‐related differences in aggregate g‐ratio, that is, g‐ratio averaged over all fibers within regions of interest, in several white matter regions in a cohort of 52 cognitively unimpaired participants ranging in age from 21 to 84 years. We found a quadratic, U‐shaped, relationship between aggregate g‐ratio and age in most cerebral regions investigated, suggesting myelin maturation until middle age followed by a decrease at older ages. As expected, we observed that these age‐related differences vary across different brain regions, with the frontal lobes and parietal lobes exhibiting slightly earlier ages of minimum aggregate g‐ratio as compared to more posterior structures such as the occipital lobes and temporal lobes; this agrees with the retrogenesis paradigm. Our results provide evidence for a nonlinear association between age and aggregate g‐ratio in a sample of adults from a highly controlled population. Finally, sex differences in aggregate g‐ratio were observed in several cerebral regions, with women exhibiting overall lower values as compared to men; this likely reflects the greater myelin content in women's brain, in agreement with recent investigations.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Richard W Kim
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Nikkita Khattar
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Wenshu Qian
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Christopher M Bergeron
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Denise Melvin
- Clinical Research Core, Office of the Scientific Director, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Linda M Zukley
- Clinical Research Core, Office of the Scientific Director, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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9
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Mohammadi S, Callaghan MF. Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging. J Neurosci Methods 2021; 348:108990. [PMID: 33129894 PMCID: PMC7840525 DOI: 10.1016/j.jneumeth.2020.108990] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/21/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. NEW METHOD This is the second review on the topic of g-ratio mapping using MRI. RESULTS This review summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. COMPARISON WITH EXISTING METHODS Using simulations based on recently published data, this review reveals caveats to the state-of-the-art calibration methods that have been used for in vivo g-ratio mapping. It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. CONCLUSIONS We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the full potential of many novel techniques yet to be investigated.
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Affiliation(s)
- Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
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10
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Xiong H, Zhou Z, Wu Z, Feng Y, Xie F. BALB/c mice infected with Angiostrongylus cantonensis: A new model for demyelination in the brain. Anat Rec (Hoboken) 2020; 304:1084-1093. [PMID: 33068322 DOI: 10.1002/ar.24538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 07/14/2020] [Accepted: 08/13/2020] [Indexed: 02/03/2023]
Abstract
In this study, we present a new model for demyelination of the central nervous system (CNS). BALB/c mice were infected with Angiostrongylus cantonensis and analyzed 7, 14, and 21 days postinfection. Neurological scale evaluation, magnetic resonance imaging (MRI), histology, real-time quantitative polymerase chain reaction, and western blotting were all performed on days 7, 14, and 21. The results showed that the neurological functions and weight of A. cantonensis-infected mice decreased markedly after 21 days of infection. MRI showed subdural effusion and white high signals in the corpus callosum in both T1WI and T2WI, while hematoxylin and eosin and luxol fast blue staining showed hemorrhage and demyelination in the corpus callosum. Transmission electron microscopy revealed that the ultrastructure of the myelin sheath in the corpus callosum was dispersed or disintegrated. The percentage of myelinated axons was significantly decreased, and the g-ratio was lower than that in the normal group. Both protein and mRNA levels of myelin basic protein decreased markedly at 21 days postinfection. Immunofluorescence revealed that the number of CC1 positive cells in the corpus callosum also decreased, which confirmed the damage of A. cantonensis to oligodendrocytes. Our experiments confirmed that A. cantonensis infection caused demyelination in the CNS of BALB/c mice after 21 days, and its clinical manifestations and pathological changes were similar to those of multiple sclerosis and other CNS demyelination models. Thus, mice infected with A. cantonensis could be used as a new model to study acute demyelination of the CNS.
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Affiliation(s)
- Huihui Xiong
- Histology and Embryology Department of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Zongpu Zhou
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Zhongdao Wu
- Parasitology Department of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, China
| | - Ying Feng
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Fukang Xie
- Histology and Embryology Department of Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
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11
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Gazdzinski LM, Mellerup M, Wang T, Adel SAA, Lerch JP, Sled JG, Nieman BJ, Wheeler AL. White Matter Changes Caused by Mild Traumatic Brain Injury in Mice Evaluated Using Neurite Orientation Dispersion and Density Imaging. J Neurotrauma 2020; 37:1818-1828. [PMID: 32242488 DOI: 10.1089/neu.2020.6992] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Mild traumatic brain injury (mTBI) is common and can lead to persistent cognitive and behavioral symptoms. Although diffusion tensor imaging (DTI) has demonstrated some sensitivity to changes in white matter following mTBI, recent studies have suggested that more complex geometric models of diffusion, including the neurite orientation dispersion and density imaging (NODDI) model, may be more sensitive and specific. Here, we evaluate microstructural changes in white matter following mTBI using DTI and NODDI in a mouse model, and compare the time course of these changes to behavioral impairment and recovery. We also assess volumetric changes for a comprehensive picture of the structural alterations in the brain and histological staining to identify cellular changes that may contribute to the differences detected in the imaging data. Increased orientation dispersion index (ODI) was observed in the optic tracts of mTBI mice compared with shams. Changes in fractional anisotropy (FA) were not statistically significant. Volume deficits were detected in the optic tract as well as in several gray matter regions: the lateral geniculate nuclei of the thalamus, the entorhinal cortex, and the superior colliculi. Glial fibrillary acidic protein (GFAP) and ionized calcium binding adaptor molecule 1 (Iba1) staining was increased in the optic tracts of mTBI brains, and this staining correlated with ODI values. A transient impairment in working memory was observed, which resolved by 6 weeks, whereas increased ODI, GFAP, and Iba1 persisted to 18 weeks post-injury. We conclude that the optic tracts are particularly vulnerable to damage from the closed-skull impact model used in this study, and that ODI may be a more sensitive metric to this damage than FA. Differences in ODI and in histological measures of astrogliosis, neuroinflammation, and axonal degeneration persist beyond behavioral impairment in this model.
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Affiliation(s)
- Lisa M Gazdzinski
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Miranda Mellerup
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Physiology and University of Toronto, Toronto, Ontario, Canada
| | - Tong Wang
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Physiology and University of Toronto, Toronto, Ontario, Canada
| | - Seyed Amir Ali Adel
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Physiology and University of Toronto, Toronto, Ontario, Canada
| | - Jason P Lerch
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Wellcome Centre for Integrative Neuroimaging, Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - John G Sled
- Translational Medicine, Hospital for Sick Children, Toronto, Ontario, Canada.,Mouse Imaging Centre at The Centre for Phenogenomics, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Brian J Nieman
- Translational Medicine, Hospital for Sick Children, Toronto, Ontario, Canada.,Mouse Imaging Centre at The Centre for Phenogenomics, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Anne L Wheeler
- Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Physiology and University of Toronto, Toronto, Ontario, Canada
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12
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Li CX, Patel S, Zhang X. Evaluation of multi-shell diffusion MRI acquisition strategy on quantitative analysis using multi-compartment models. Quant Imaging Med Surg 2020; 10:824-834. [PMID: 32355646 DOI: 10.21037/qims.2020.03.11] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background Multi-compartment diffusion models such as Neurite Orientation Dispersion and Density Imaging (NODDI) have been increasingly used for diffusion MRI (dMRI) data processing in biomedical research. However, those models usually require multiple HARDI shells that may increase scanning duration substantially, and their application can be hindered in uncooperative patients (like infants) accordingly. Also, it is highly expected that the same dataset can be explored with multiple diffusion models for retrieving complementary information. Methods Multiple gradient-encoding schemes which consisted of 4-6 shells, moderate b-values (bmax =1,500 or 2,000 s/mm2), and 32-80 gradient directions were explored. The corresponding time of acquisition (TA) for a single scan ranged from 3 to 8 minutes respectively. The dMRI protocols were tested on macaque monkeys using a 3T clinical setting. The data were analysed using both NODDI and diffusion basic spectrum imaging (DBSI) models. Results The maps of orientation dispersion index (ODI) and CSF were consistent across the 4-6 shell sampling schemes. However, the corresponding intra-cellular volume fraction (ICVF) maps showed reduced pixel counts [1,100±98 (80 directions) vs. 806±70 (32 directions), one slice] in white matter when fewer gradient directions or lower b-value was applied. The hindered diffusion and CSF ratio maps were comparable across these sampling schemes. The maps of restricted diffusion ratio varied across the schemes. However, its mean ratios (0.23±0.02 vs. 0.22±0.01) and pixel counts (1,540±70 vs. 1,510±38, one slice) between the schemes of 80 and 32 directions with b=2,000 s/mm2 were comparable. Conclusions The present study reports a fast multi-shell dMRI data acquisition and processing strategy which allows for obtaining complementary information about microstructural alteration and inflammation from a single dMRI data set with both NODDI and DBSI models. The proposed approach may be particularly useful for characterizing the neurodegenerative disorders in uncooperative patients like children or acute stroke patients in which brain injury is associated with inflammation.
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Affiliation(s)
- Chun-Xia Li
- Yerkes Imaging Center, Emory University, Atlanta, GA, USA
| | - Sudeep Patel
- Yerkes Imaging Center, Emory University, Atlanta, GA, USA
| | - Xiaodong Zhang
- Yerkes Imaging Center, Emory University, Atlanta, GA, USA.,Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
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13
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Chuhutin A, Hansen B, Wlodarczyk A, Owens T, Shemesh N, Jespersen SN. Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis. Neuroimage 2019; 208:116406. [PMID: 31830588 PMCID: PMC9358435 DOI: 10.1016/j.neuroimage.2019.116406] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 11/20/2019] [Accepted: 11/25/2019] [Indexed: 01/22/2023] Open
Abstract
Diffusion kurtosis imaging (DKI) is an imaging modality that yields novel
disease biomarkers and in combination with nervous tissue modeling, provides
access to microstructural parameters. Recently, DKI and subsequent estimation of
microstructural model parameters has been used for assessment of tissue changes
in neurodegenerative diseases and associated animal models. In this study, mouse
spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of
multiple sclerosis (MS) were investigated for the first time using DKI in
combination with biophysical modeling to study the relationship between
microstructural metrics and degree of animal dysfunction. Thirteen spinal cords
were extracted from animals with varied grades of disability and scanned in a
high-field MRI scanner along with five control specimen. Diffusion weighted data
were acquired together with high resolution T2*
images. Diffusion data were fit to estimate diffusion and kurtosis tensors and
white matter modeling parameters, which were all used for subsequent statistical
analysis using a linear mixed effects model. T2*
images were used to delineate focal demyelination/inflammation. Our results
reveal a strong relationship between disability and measured microstructural
parameters in normal appearing white matter and gray matter. Relationships
between disability and mean of the kurtosis tensor, radial kurtosis, radial
diffusivity were similar to what has been found in other hypomyelinating MS
models, and in patients. However, the changes in biophysical modeling parameters
and in particular in extra-axonal axial diffusivity were clearly different from
previous studies employing other animal models of MS. In conclusion, our data
suggest that DKI and microstructural modeling can provide a unique contrast
capable of detecting EAE-specific changes correlating with clinical
disability.
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Affiliation(s)
| | | | - Agnieszka Wlodarczyk
- Department of Neurobiology Research, Institute for Molecular Medicine,University of South Denmark, Odense, Denmark
| | - Trevor Owens
- Department of Neurobiology Research, Institute for Molecular Medicine,University of South Denmark, Odense, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sune Nørhøj Jespersen
- CFIN, Aarhus University, Aarhus, Denmark; Department of Physics, Aarhus University, Aarhus, Denmark
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14
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Fieremans E, Lee HH. Physical and numerical phantoms for the validation of brain microstructural MRI: A cookbook. Neuroimage 2018; 182:39-61. [PMID: 29920376 PMCID: PMC6175674 DOI: 10.1016/j.neuroimage.2018.06.046] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 06/08/2018] [Accepted: 06/13/2018] [Indexed: 12/24/2022] Open
Abstract
Phantoms, both numerical (software) and physical (hardware), can serve as a gold standard for the validation of MRI methods probing the brain microstructure. This review aims to provide guidelines on how to build, implement, or choose the right phantom for a particular application, along with an overview of the current state-of-the-art of phantoms dedicated to study brain microstructure with MRI. For physical phantoms, we discuss the essential requirements and relevant characteristics of both the (NMR visible) liquid and (NMR invisible) phantom materials that induce relevant microstructural features detectable via MRI, based on diffusion, intra-voxel incoherent motion, magnetization transfer or magnetic susceptibility weighted contrast. In particular, for diffusion MRI, many useful phantoms have been proposed, ranging from simple liquids to advanced biomimetic phantoms consisting of hollow or plain microfibers and capillaries. For numerical phantoms, the focus is on Monte Carlo simulations of random walk, for which the basic principles, along with useful criteria to check and potential pitfalls are reviewed, in addition to a literature overview highlighting recent advances. While many phantoms exist already, the current review aims to stimulate further research in the field and to address remaining needs.
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Affiliation(s)
- Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
| | - Hong-Hsi Lee
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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