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

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

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

51. Diffusion tensor visualization

52. White Matter Algorithms Streamlines Tracking Techniques Fiber Assg thru Cont Tracking Tensor-deflection

53. Filters Length Average linear anisotropy Regions of interest

54. Conclusion Multiple data types (DTI & fMRI) New visualization interface Interactive queries Hypothesis generation & testing

55. Next Steps Real work Multiple subjects Normal to abnormal Acquisition technology Path tracing algorithms

56. Question Is there any reason for tools such as this to be validated?

57. Question If validated this early on, wouldn’t every change pretty much negate the validation?

58. Question Should there be some kind of benchmark to use to measure these applications against?