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Exploring Connectivity of the Brain’s White Matter with Dynamic Queries

Exploring Connectivity of the Brain’s White Matter with Dynamic Queries. Anthony Sherbondy, David Akers, Rachel Mackenzie, Robert Dougherty, and Brian Wandell. IEEE Transactions on Visualization and Computer Graphics, V11, No 4, July/August 2005. Presented by: Eugene (Austin) Stoudenmire

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Exploring Connectivity of the Brain’s White Matter with Dynamic Queries

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  1. Exploring Connectivity of the Brain’s White Matter with Dynamic Queries Anthony Sherbondy, David Akers, Rachel Mackenzie, Robert Dougherty, and Brian Wandell IEEE Transactions on Visualization and Computer Graphics, V11, No 4, July/August 2005 Presented by: Eugene (Austin) Stoudenmire 14 Feb 2007

  2. Problem • New technology emerged • Diffusion Tensor Imaging (DTI) • White matter connections, i.e. fiber tracts, can now be measured • Need to take advantage of it • Requires better visualization

  3. We Care • Better visualization would • Assist research • Interactive

  4. Approach • Combine types of data • Anatomical – White – DTI • Functional – Gray – fMRI • Functional Magnetic Resonance Imaging • Precompute • Query Interface • Pictoral • Labeled • Ranges

  5. DTI • Diffusion Tensor Imaging • New Technology • Measures white matter pathways • Estimates water molecule diffusion • Water diffuses lengthwise along axons • Diffusion direction  nerve fiber orientation

  6. One Method of DTI Visualization • MR Tractography • Traces principle direction of diffusion • Connects points into fiber tracts • Fiber tracts = pathways • Anatomical connections between endpoints of the pathways are implied • Therefore, implied white matter structure

  7. These Pathways • Not individual nerves • Not Bundles • But something • Abstract, white matter route “possibilities”

  8. fMRI • Functional Magnetic Res Imaging • Correlate activity • Suggests gray matter connections

  9. The Combination • Take the MR Tractography data • Precompute paths, statistical properties • Interactive manipulation • Regions of interest – Box / Ellipsoid • Path properties – Length / Curvature • Combine with fMRI • Search for anatomical paths that might connect functionally-defined regions • Saves time over existing approaches

  10. Query Interface

  11. Query Interface – Partial Blowup

  12. Query Interface – Partial Blowup

  13. Query Interface – Partial Blowup

  14. Query Interface – Partial Blowup

  15. Acqusition DTI & fMRI

  16. Subject • Neurologically Normal • Male • Human • 35

  17. DTI • Eight 3-minute whole brain scans • Averaged • 38 axial slices • 2 x 2 x 3 mm voxels • 8-minute high res anat images • 1 x 1 x 1 mm voxel • Coregistered • DTI resampled to 2 mm

  18. fMRI • 21-30 obliquely oriented slices • 2 x 2 x 3 mm voxel • Registered with anatomy • Mapped to cortical surface mesh

  19. Precomputation

  20. Fractional Anisotropy (FA) • Diffusion orientation ratio 0 = spherical = gray matter 0.5 = linear or planar ellipsoid 1 = very linear • Uses • Algorithm termination criteria • Queries • Navigational aid

  21. Approaches • Typical • Interactively trace pathways • Authors’ • Precompute pathways • Over entire white matter • Then let software “prune”

  22. Cortical Surface • Classified white matter • Semi-manually – neuroscientist • Marching-Cubes -> t-mesh • Smoothed • Kept both • 230,000 vertices

  23. Precomputation • Statistical properties • Length • Avg FA • Avg Curvature • Tractography Algorithm

  24. Implementation

  25. Path Rendering • Lines vs streamtubes (for speed) • Pathways – luminance offset • Groups of pathways – hue • User defined hue • Virtual staining • Queries modified – stains remain

  26. Hardware/Software • Visualization C++ • ToolKit (VTK) • RAPID • Fast VOI / Path Intersection Comp • 80K-120K paths/sec (w/SGI RE) • Allowed 3-8 • 510MB for 26K paths @ 20KB/path • 160MB for cortical meshes

  27. Sequential Dynamic Queries

  28. All 13,000 Pathways

  29. Length > 4 cm

  30. Through VOI 1

  31. Through VOI 1 AND (2 or 3)

  32. Volumes of Interest Surface-constrained

  33. VOI on Cortical Surface

  34. Same VOI, Smoothed Surface

  35. Validation of Known Pathways

  36. Occipital Lobe

  37. Occipital to Right Frontal Lobe

  38. Occipital to Left Frontal Lobe

  39. Occipital to R & L, w/Context

  40. Forming Hypotheses

  41. Known and Unknown Paths

  42. Algorithm Comparison STT – Streamlines Tracking Techniques Vs TEND – Tensor Deflection

  43. STT (blue) vs TEND (yellow)

  44. Exploration of Connections Between Functional Areas

  45. fMRI Areas Colormapped

  46. VOI Placement

  47. Surface Removed  Paths Visible

  48. VOI Adjusted  Different Paths

  49. Evaluation • Types of functions • Validation of known pathways • Hypothesis generation • Time to explore – 10 minutes for significant exploration • Speed – Interactive rates • Interface – Interactive queries

  50. Alternative Methods

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