scholarly journals Population-Averaged Atlas of the Macroscale Human Structural Connectome and Its Network Topology

2017 ◽  
Author(s):  
Fang-Cheng Yeh ◽  
Sandip Panesar ◽  
David Fernandes ◽  
Antonio Meola ◽  
Masanori Yoshino ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Network Topology ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
Cerebral Hemispheres ◽  
Brain White Matter ◽  
Structural Connectome ◽  
Commissural Pathways ◽  
Multi Level

AbstractA comprehensive map of the structural connectome in the human brain has been a coveted resource for understanding macroscopic brain networks. Here we report an expert-vetted, population-averaged atlas of the structural connectome derived from diffusion MRI data (N=842). This was achieved by creating a high-resolution template of diffusion patterns averaged across individual subjects and using tractography to generate 550,000 trajectories of representative white matter fascicles annotated by 80 anatomical labels. The trajectories were subsequently clustered and labeled by a team of experienced neuroanatomists in order to conform to prior neuroanatomical knowledge. A multi-level network topology was then described using whole-brain connectograms, with subdivisions of the association pathways showing small-worldness in intra-hemisphere connections, projection pathways showing hub structures at thalamus, putamen, and brainstem, and commissural pathways showing bridges connecting cerebral hemispheres to provide global efficiency. This atlas of the structural connectome provides representative organization of human brain white matter, complementary to traditional histologically-derived and voxel-based white matter atlases, allowing for better modeling and simulation of brain connectivity for future connectome studies.

scholarly journals Prevalence of white matter pathways coming into a single diffusion MRI voxel orientation: the bottleneck issue in tractography

2021 ◽  
Author(s):  
Kurt Schilling ◽  
Chantal M.W. Tax ◽  
Francois M.W. Rheault ◽  
Bennett A Landman ◽  
Adam W Anderson ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
False Negative ◽  
Fiber Tractography ◽  
Single Voxel ◽  
Ill Posed ◽  
Pass Through

Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so-called "crossing fiber problem" has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub-cortical terminations. These so-called "bottleneck" regions contribute to the ill-posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50-70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered "single fiber voxels" often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain.

Real-Time Ex-Vivo Magnetic Resonance Image—Guided Dissection of Human Brain White Matter: A Proof-of-Principle Study

2019 ◽  
Vol 125 ◽  
pp. 198-206 ◽  
Author(s):  
Giacomo Bertolini ◽  
Emanuele La Corte ◽  
Domenico Aquino ◽  
Elena Greco ◽  
Zefferino Rossini ◽  
...  
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Human Brain ◽  
Real Time ◽  
Magnetic Resonance Image ◽  
Ex Vivo ◽  
Image Guided ◽  
Brain White Matter ◽  
Proof Of Principle

Characterization of Myelin Fractions from Human Brain White Matter

1985 ◽  
Vol 44 (5) ◽  
pp. 1411-1418 ◽  
Author(s):  
Tony F. Cruz ◽  
Mario A. Moscarello
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Brain White Matter

Microstructural Hyperelastic Characterization of Brain White Matter in Tension

2019 ◽  
Author(s):  
Mohammadreza Ramzanpour ◽  
Mohammad Hosseini-Farid ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Finite Element ◽  
White Matter ◽  
Human Brain ◽  
Volume Fraction ◽  
Fibrous Composite ◽  
Diffuse Axonal Injury ◽  
Stiffness Ratio ◽  
Fibrous Composite Material ◽  
Brain White Matter ◽  
The Brain

Abstract Axons as microstructural constituent elements of brain white matter are highly oriented in extracellular matrix (ECM) in one direction. Therefore, it is possible to model the human brain white matter as a unidirectional fibrous composite material. A micromechanical finite element model of the brain white matter is developed to indirectly measure the brain white matter constituents’ properties including axon and ECM under tensile loading. Experimental tension test on corona radiata conducted by Budday et al. 2017 [1] is used in this study and one-term Ogden hyperelastic constitutive model is applied to characterize its behavior. By the application of genetic algorithm (GA) as a black box optimization method, the Ogden hyperelastic parameters of axon and ECM minimizing the error between numerical finite element simulation and experimental results are measured. Inverse analysis is conducted on the resultant optimized parameters shows high correlation of coefficient (>99%) between the numerical and experimental data which verifies the accuracy of the optimization procedure. The volume fraction of axons in porcine brain white matter is taken to be 52.7% and the stiffness ratio of axon to ECM is perceived to be 3.0. As these values are not accurately known for human brain white matter, we study the material properties of axon and ECM for different stiffness ratio and axon volume fraction values. The results of this study helps to better understand the micromechanical structure of the brain and micro-level injuries such as diffuse axonal injury.

Is water diffusion restricted in human brain white matter? An echo-planar NMR imaging study

Neuroreport ◽  
1993 ◽  
Vol 4 (7) ◽  
pp. 887-890 ◽  
Author(s):  
Denis Le Bihan ◽  
Robert Turner ◽  
Philippe Douek
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Imaging Study ◽  
Water Diffusion ◽  
Nmr Imaging ◽  
Brain White Matter ◽  
Echo Planar

scholarly journals The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

2020 ◽  
pp. 1-15
Author(s):  
Tommy Boshkovski ◽  
Ljupco Kocarev ◽  
Julien Cohen-Adad ◽  
Bratislav Mišić ◽  
Stéphane Lehéricy ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Added Value ◽  
Modular Organization ◽  
Sensitive Measure ◽  
Higher Order Functions ◽  
Insight Into

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.

scholarly journals Teaching Anatomy to Neuroscientific Health-Care Professionals: Are They Receiving the Best Anatomical Education?

2019 ◽  
Vol 30 (1) ◽  
pp. 41-45
Author(s):  
Francesco Latini ◽  
Mats Ryttlefors
Keyword(s):  
Health Care ◽  
White Matter ◽  
Medical Students ◽  
Health Care Professionals ◽  
Brain Connectivity ◽  
Care Staff ◽  
Health Care Staff ◽  
Brain Anatomy ◽  
Brain White Matter ◽  
White Matter Anatomy

Abstract University neuroanatomical courses seldom teach the anatomical-functional connectivity of the brain. White matter dissection improves understanding of brain connectivity, but until now has been restricted to neurosurgeons and in some cases to medical students, never to health-care non-medical professionals. Our aim was to teach white matter anatomy to medical and non-medical students to evaluate this technique in groups with different education. A standardized lab demonstration of white matter anatomy was performed with high appreciation rate in both groups, suggesting a suboptimal neuroanatomical education provided by basic course. We encourage to include this technique of teaching brain anatomy into basic neuroanatomical courses to improve the level of comprehension and competence in all health-care staff within the field of neuroscience.

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