scholarly journals Early detection of human glioma sphere xenografts in mouse brain using diffusion MRI at 14.1 T

2016 ◽  
Vol 29 (11) ◽  
pp. 1577-1589 ◽  
Author(s):  
P. Porcari ◽  
M. E. Hegi ◽  
H. Lei ◽  
M-F. Hamou ◽  
I. Vassallo ◽  
...  
Keyword(s):  
Early Detection ◽  
Mouse Brain ◽  
Diffusion Mri ◽  
Human Glioma

scholarly journals Probing mouse brain microstructure using oscillating gradient diffusion MRI

2011 ◽  
Vol 67 (1) ◽  
pp. 98-109 ◽  
Author(s):  
Manisha Aggarwal ◽  
Melina V. Jones ◽  
Peter A. Calabresi ◽  
Susumu Mori ◽  
Jiangyang Zhang
Keyword(s):  
Mouse Brain ◽  
Diffusion Mri ◽  
Gradient Diffusion ◽  
Brain Microstructure

scholarly journals A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data

2015 ◽  
Vol 25 (11) ◽  
pp. 4628-4637 ◽  
Author(s):  
Evan Calabrese ◽  
Alexandra Badea ◽  
Gary Cofer ◽  
Yi Qi ◽  
G. Allan Johnson
Keyword(s):  
Mouse Brain ◽  
Diffusion Mri

Synchrotron X‐ray micro‐CT as a validation dataset for diffusion MRI in whole mouse brain

2021 ◽  
Author(s):  
Scott Trinkle ◽  
Sean Foxley ◽  
Narayanan Kasthuri ◽  
Patrick La Rivière
Keyword(s):  
Mouse Brain ◽  
Diffusion Mri ◽  
Validation Dataset ◽  
Micro Ct ◽  
X Ray

scholarly journals Electron microscopy 3-dimensional segmentation and quantification of axonal dispersion and diameter distribution in mouse brain corpus callosum

2018 ◽  
Author(s):  
Hong-Hsi Lee ◽  
Katarina Yaros ◽  
Jelle Veraart ◽  
Jasmine Pathan ◽  
Feng-Xia Liang ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Corpus Callosum ◽  
Time Dependence ◽  
Mouse Brain ◽  
Fiber Orientation ◽  
Diffusion Mri ◽  
Orientation Distribution ◽  
Diffusion Time ◽  
3 Dimensional ◽  
Axonal Diameter

AbstractTo model the diffusion MRI signal in brain white matter, general assumptions have been made about the microstructural properties of axonal fiber bundles, such as the axonal shape and the fiber orientation dispersion. In particular, axons are modeled by perfectly circular cylinders with no diameter variation within each axon, and their directions obey a specific orientation distribution. However, these assumptions have not been validated by histology in 3-dimensional high-resolution neural tissue. Here, we reconstructed sequential scanning electron microscopy images in mouse brain corpus callosum, and introduced a semi-automatic random-walker (RaW) based algorithm to rapidly segment individual intra-axonal spaces and myelin sheaths of myelinated axons. Confirmed with a conventional machine-learning-based interactive segmentation method, our semi-automatic algorithm is reliable and less time-consuming. Based on the segmentation, we calculated histological estimates of size-related (e.g., inner axonal diameter, g-ratio) and orientation-related (e.g., Fiber orientation distribution and its rotational invariants, dispersion angle) quantities, and simulated how these quantities would be observed in actual diffusion MRI experiments by considering diffusion time-dependence. The reported dispersion angle is consistent with previous 2-dimensional histology studies and diffusion MRI measurements, though the reported diameter is larger than those in other mouse brain studies. Our results show that the orientation-related metrics have negligible diffusion time-dependence; however, inner axonal diameters demonstrate a non-trivial time-dependence at diffusion times typical for clinical and preclinical use. In other words, the fiber dispersion estimated by diffusion MRI modeling is relatively independent, while the "apparent" axonal size estimated by axonal diameter mapping potentially depends on experimental MRI settings.

scholarly journals Emerging advances of in vivo detection of chronic traumatic encephalopathy and traumatic brain injury

2019 ◽  
Vol 92 (1101) ◽  
pp. 20180925 ◽  
Author(s):  
Julian D. Dallmeier ◽  
Somayeh Meysami ◽  
David A. Merrill ◽  
Cyrus A. Raji
Keyword(s):  
Early Detection ◽  
Diffusion Mri ◽  
Neurodegenerative Disorder ◽  
Chronic Traumatic Encephalopathy ◽  
Neurofibrillary Tangle ◽  
Detection Methods ◽  
Detection Techniques ◽  
In Vivo Detection ◽  
Neuroimaging Techniques

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disorder that is of epidemic proportions in contact sports athletes and is linked to subconcussive and concussive repetitive head impacts (RHI). Although postmortem analysis is currently the only confirmatory method to diagnose CTE, there has been progress in early detection techniques of fluid biomarkers as well as in advanced neuroimaging techniques. Specifically, promising new methods of diffusion MRI and radionucleotide PET scans could aid in the early detection of CTE. The authors examine early detection methods focusing on various neuroimaging techniques. Advances in structural and diffusion MRI have demonstrated the ability to measure volumetric and white matter abnormalities associated with CTE. Recent studies using radionucleotides such as flortaucipir and 18F-FDDNP have shown binding patterns that are consistent with the four stages of neurofibrillary tangle (NFT) distribution postmortem. Additional research undertakings focusing on fMRI, MR spectroscopy, susceptibility-weighted imaging, and singlephoton emission CT are also discussed as are advanced MRI methods such as diffusiontensor imaging and arterial spin labeled. Neuroimaging is fast becoming a key instrument in early detection and could prove essential for CTE quantification. This review explores a global approach to in vivo early detection. Limited data of in vivo CTE biomarkers with postmortem confirmation are available. While some data exist, they are limited by selection bias. It is unlikely that a single test will be sufficient to properly diagnosis and distinguish CTE from other neurodegenerative diseases such as Alzheimer disease or Frontotemporal Dementia. However, with a combination of fluid biomarkers, neuroimaging, and genetic testing, early detection may become possible.

Complex Mouse Brain Anatomical Network Attributes Estimated via Diffusion- MRI Data and Graph Theory

2013 ◽  
pp. 65-68
Author(s):  
Y. Iturria-Medina ◽  
M. Ontivero-Ortega ◽  
E. J. Canales-Rodríguez ◽  
L. Melie-García ◽  
P. Valdés-Hernández ◽  
...  
Keyword(s):  
Graph Theory ◽  
Mouse Brain ◽  
Diffusion Mri
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