Tractography Category

Average shape and position of all bundles that are part of the TractEM protocol (association and projection pathways are shown only for the left hemisphere). The average was computed using segmentation from 4 raters and 4 datasets (BLSA) using a majority vote. This highlights the general agreement on the shape, but as seen in the lower right vignette the variability of each individual segmentation can be quite extreme.

TractEM: Evaluation of protocols for deterministic tractography white matter atlas

Dec. 3, 2021—Rheault, Francois, Roza G. Bayrak, Xuan Wang, Kurt G. Schilling, Jasmine M. Greer, Colin B. Hansen, Cailey Kerley et al. “TractEM: Evaluation of protocols for deterministic tractography white matter atlas.” Magnetic Resonance Imaging 85 (2022): 44-56. Full Text Abstract Purpose: One of the key challenges of the manual delineation of white matter pathways is human-rater’s subjectivity in labeling....

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

Jun. 23, 2021—Leon Y. Cai, Qi Yang, Colin B. Hansen, Vishwesh Nath, Karthik Ramadass, Graham W. Johnson, Benjamin N. Conrad, Brian D. Boyd, John P. Begnoche, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Warren D. Taylor, Gavin R. Price, Victoria L. Morgan, Baxter P. Rogers, Kurt G. Schilling, Bennett A. Landman. PreQual: An automated pipeline...

Experimental workflow and generation of Pandora atlases. Data from three repositories (HCP, BLSA, and VU) were curated. Subject-level
processing includes tractography and registration to MNI space. Volumetric atlases for each set of bundle definitions is created by population-averaging
in standard space. Point clouds are displayed which allow qualitative visualization of probability densities of a number of fiber pathways. Finally, surface
atlases are created by assigning indices to the vertices of the MNI template white matter/gray matter boundary.

Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography

Nov. 29, 2020—Colin B. Hansen, Qi Yang, Ilwoo Lyu, Francois Rheault, Cailey Kerley, Bramsh Qamar Chandio, Shreyas Fadnavis, Owen Williams, Andrea T. Shafer, Susan M. Resnick, David H. Zald, Laurie E. Cutting, Warren D. Taylor, Brian Boyd, Eleftherios Garyfallidis, Adam W. Anderson, Maxime Descoteaux, Bennett A. Landman, Kurt G. Schilling. Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography. Neuroinform (2020). Full Text Abstract Brain atlases have proven to be valuable neuroscience tools for...

Joint cortical surface and structural connectivity analysis of Alzheimer’s Disease

Nov. 20, 2020—Leon Y. Cai, Cailey I. Kerley, Chang Yu, Katherine S. Aboud, Lori L. Beason-Held, Andrea T. Shafer, Susan M. Resnick, Lori C. Jordan, Adam W. Anderson, Kurt G. Schilling, Ilwoo Lyu, Bennett A. Landman. Joint cortical surface and structural connectivity analysis of Alzheimer’s Disease. SPIE Medical Imaging, 2021. Full Text: NIHMSID Abstract Joint independent component...

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...

Fig 8. External dataset validation.
External validation of corrected b0’s after applying the synthesized b0 distortion correction pipeline with data from open-sourced studies. The distorted (“D”) and undistorted (“U”) b0 images are shown alongside T1 images. In all cases, effective distortion correction is visually apparent (distortions indicated by arrows).

Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

Aug. 25, 2020—Kurt G Schilling*, Justin Blaber*, Colin Hansen, Leon Cai, Baxter Rogers, Adam W Anderson, Seth A Smith, Praitayini Kanakaraj, Tonia Rex, Susan M. Resnick, Andrea T. Shafer, Laurie Cutting, Neil Woodward, David Zald, Bennett A Landman.“ Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps ”. PLoS ONE 15(7): e0236418. https://doi.org/10.1371/journal.pone.0236418 Full text: https:...

Violin plots of intrasession submissions across both the scanners per tract. (A) Dice similarity coefficients, (B) intraclass correlation coefficients. The top row depicts the median of the top five intrasession submissions. The tracts are in the following order (L/R): a) Uncinate, b) Fornix, c) Fminor & Fmajor, d) Cingulum, e) Corticospinal tract, f) Inferior longitudinal fasciculus, g) Superior longitudinal fasciculus, h) Inferior fronto‐occipital tract.

Tractography reproducibility challenge with empirical data (TRAceD): The 2017 ISMRM diffusion study group challenge

Jan. 17, 2020—Nath V, Schilling KG, Parvathaneni P, Huo Y, Blaber JA, Hainline AE, Barakovic M, Romascano D, Rafael‐Patino J, Frigo M, Girard G. Tractography reproducibility challenge with empirical data (traced): The 2017 ISMRM diffusion study group challenge. Journal of Magnetic Resonance Imaging. 2020 Jan;51(1):234-49. Full text: https://www.ncbi.nlm.nih.gov/pubmed/31179595 Abstract BACKGROUND: Fiber tracking with diffusion-weighted MRI has become an...

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...

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