Crossing Fibers Category

Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features

Aug. 31, 2023—Kurt G Schilling, Derek Archer, Francois Rheault, Ilwoo Lyu, Yuankai Huo, Leon Y Cai, Silvia A Bunge, Kevin S Weiner, John C Gore, Adam W Anderson, Bennett A Landman Paper: https://pubmed.ncbi.nlm.nih.gov/37074446 Abstract Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up...

Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context

May. 29, 2023—Leon Y. Cai, Ho Hin Lee, Nancy R. Newlin, Cailey I. Kerley, Praitayini Kanakaraj, Qi Yang, Graham W. Johnson, Daniel Moyer, Kurt G. Schilling, François Rheault, and Bennett A. Landman Paper: https://www.biorxiv.org/content/10.1101/2023.02.25.530046v2 Code: https://github.com/MASILab/cornn_tractography Abstract Diffusion MRI (dMRI) streamline tractography is the gold-standard for in vivo estimation of white matter (WM) pathways in the brain. However, the...

Implementation considerations for deep learning with diffusion MRI streamline tractography

May. 29, 2023—Leon Y. Cai, Ho Hin Lee, Nancy R. Newlin, Michael E. Kim, Daniel Moyer, Francois Rheault, Kurt G. Schilling, and Bennett A. Landman Paper: https://www.biorxiv.org/content/10.1101/2023.04.03.535465v1 Code: https://github.com/MASILab/STrUDeL Abstract One area of medical imaging that has recently experienced innovative deep learning advances is diffusion MRI (dMRI) streamline tractography with recurrent neural networks (RNNs). Unlike traditional imaging studies which...

Figure 4. The response function varies across brain regions. Three locations in the same monkey brain (monkey #2) are shown, along with the histological fiber orientation distribution, signal, and derived response function for all 9 voxels. In addition, the average response function for each z-stack is shown in two views.

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain

Aug. 25, 2020—Kurt G Schilling, Yurui Gao, Iwona Stepniewska, Vaibhav Janve, Bennett A. Landman, Adam W. Anderson. “Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain”. NMR in Biomedicine. 2019 Jun;32(6):e4090. doi: 10.1002/nbm.4090. Epub 2019 Mar 25. Full text: https://pubmed.ncbi.nlm.nih.gov/30908803/ Abstract Understanding the...

Response functions estimated using conventional methods differ from the histologically derived response functions. Kernels estimated using the highest FA voxels, the iterative calibration method, and modeled from the diffusion tensor are shown in two views. The average histologically derived response function for each monkey (across 6 z stacks) is shown (monkeys 1–3 correspond to A‐C) along with the mean (red) and mean ± standard deviation (gray) surfaces to highlight variability. Note that differences in magnitude are due to differing normalization in our work and in MRTrix3. Shape, and not magnitude, determines the final FOD shape

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers-An ex vivo validation study in the squirrel monkey brain.

Mar. 16, 2019—Kurt G. Schilling, Yurui Gao, Iwona Stepniewska, Vaibhav Janve, Bennett A. Landman, Adam W. Anderson. “Histologically derived fiber response functions for diffusion MRI vary across white matter fibers – an ex vivo validation study in the squirrel monkey brain”. NMR in Biomedicine. 2019 Jun;32(6):e4090. doi: 10.1002/nbm.4090. Epub 2019 Mar 25. Full text: https://www.ncbi.nlm.nih.gov/pubmed/30908803 Abstract Understanding the...

Subset of DTI streamlines for each tracking strategy. Labels for crown, wall, and fundi are shown with a zoomed in view of the SFG. DTI streamlines are shown for M1 (whole brain seeding), M2 (WM seeding), and M3 (WMGM boundary seeding), and are colored based on streamline orientation. The dashed arrow highlights a fundus, where no streamlines are able to propagate. The solid arrow points toward the increased curvature of streamlines entering the GM. And the oval highlights a large, homogenous, area of WM, where seeding will contribute to over‐representation of fibers terminating at the crown

Confirmation of a Gyral Bias in Diffusion MRI Fiber Tractography

Dec. 15, 2018—Kurt G Schilling, Yurui Gao, Iwona Stepniewska, Bennett A. Landman, and Adam W Anderson. “Confirmation of a Gyral Bias in Diffusion MRI Fiber Tractography”. Human Brain Mapping. 2018 Mar;39(3):1449-1466. doi: 10.1002/hbm.23936. Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Confirmation+of+a+Gyral+Bias+in+Diffusion+MRI+Fiber+Tractography Abstract Diffusion MRI fiber tractography has been increasingly used to map the structural connectivity of the human brain. However, this technique...

Fiber orientation estimation in a crossing fiber region. The results of structure tensor analysis on a myelin-stained histological slice shown as a color-coded orientation map (A), is shown zoomed in on two regions containing crossing (yellow box), and disperse (blue box) fibers. The resulting FODs are displayed at varying resolution levels (B). From the resulting FODs, voxels are characterized as crossing fiber (C; green) vs. not-crossing fibers (C; red), as well as single fiber (D; red) vs. complex fibers (D; green), at all resolutions. Note that “not-crossing” voxels are those that with single fiber populations in addition to complex fibers that do not contain two discrete local maxima. Voxels from diffusion MRI in the same region are also displayed as single fiber (E; red) vs. crossing fibers (E; green). Histograms for this specific region of interest show percentages of crossing fibers (F; left) and percentages of complex fibers (F; middle) for histology, as well as percentage of crossing fibers (F; right) for dMRI.

Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?

Dec. 15, 2018—Kurt G Schilling, Yurui Gao, Vaibhav Janve, Iwona Stepniewska, Bennett A Landman, Adam W Anderson. “Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?”. NMR in Biomedicine. (2017) 30(12),e3787. https://doi.org/10.1002/nbm.3787. Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Can+increased+spatial+resolution+solve+the+crossing+fiber+problem+for+diffusion+MRI%3F Abstract It is now widely recognized that voxels with crossing fibers or complex geometrical configurations present a challenge for...

Qualitative confocal images. Representative confocal data (of a single slice) are shown for single (A) and crossing (B) fiber regions. Overview images highlight location of full 3D z-stacks (shown as a single, middle slice). A zoomed regions of interest in the middle of the z-stack (equivalent in size to an MRI voxel) are shown in the middle column. Results from structure tensor analysis are shown as color-coded images (with colors scheme as described in Fig. 1), with the histologically-defined FOD overlaid as 3D glyphs (right).

Histological Validation of Diffusion MRI Fiber Orientation Distributions and Dispersion

Dec. 15, 2018—Kurt G Schilling, Vaibhav Janve; Yurui Gao; Iwona Stepniewska; Bennett A Landman; Adam W Anderson. “Histological Validation of Diffusion MRI Fiber Orientation Distributions and Dispersion”. NeuroImage. 2018 Jan 15;165:200-221. doi: 10.1016/j.neuroimage.2017.10.046. Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Histological+Validation+of+Diffusion+MRI+Fiber+Orientation+Distributions+and+Dispersion Abstract Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to...

Methodology pipeline. High resolution BDA micrographs are registered to the corresponding digital photograph of the frozen tissue block, which is registered to the 3D diffusion MRI volume. From the micrograph, BDA is automatically segmented, resulting in a BDA density map. From diffusion MRI, tractography is performed, resulting in tract density maps. Direct, voxel-by-voxel comparisons can now be made between histology and diffusion tractography.

Anatomical Accuracy of Standard-Practice Tractography Algorithms in the Motor System – a Histological Validation in the Squirrel Monkey Brain

Dec. 15, 2018—Kurt G. Schilling, Yurui Gao, Vaibhav Janve, Iwona Stepniewska, Bennett Landman, Adam Anderson. “Anatomical Accuracy of Standard-Practice Tractography Algorithms in the Motor System – a Histological Validation in the Squirrel Monkey Brain”. Magnetic Resonance Imaging. 2018. doi: 1016/j.mri.2018.09.004 Full text: https://www.ncbi.nlm.nih.gov/pubmed/30213755 Abstract For two decades diffusion fiber tractography has been used to probe both the spatial extent of...

HARFI is able to capture complex, asymmetric functional distributions. Fiber fanning (A, B; red arrows), three‐way crossings (C; blue arrows), and bending (D; black arrows) voxels are reconstructed in regions expected to contain complex distributions

Functional Tractography of White Matter by High Angular Resolution Functional-correlation Imaging (HARFI)

Dec. 15, 2018—Kurt G. Schilling, Yurui Gao, Muwei Li, Tung-Lin Wu, Justin Blaber, Bennett A Landman, Adam W Anderson, Zhaohua Ding, John C Gore. “Functional Tractography of White Matter by High Angular Resolution Functional-correlation Imaging (HARFI)”. Magn Reson Med. 2018;00:1-14. https://doi.org/10.1002/mrm.27512. Full text: https://www.ncbi.nlm.nih.gov/pubmed/30277272 https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27512 Abstract Purpose Functional magnetic resonance imaging with BOLD contrast is widely used for detecting...

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