1. The importance of visualization
12° 0° V1 V1
Calcarine
Calcarine V1
Calcarine
Upper lip
Upper lip
Lower lip
Lower lip
Left hemisphere
gyri
sulci
1cm

2. The importance of visualization
Precision
Understanding, understanding, understanding

3. Visualization should offer precise communication
Why precision?
(a)
(b)

4. Visualization should offer precise communication
Why precision?
Neighborhoods – topology
Measurement of distances, surface areas, and volumes

5. Cortical Topology: distances on the volume and cortical surface are not the same
15mm
5mm
Figure 5. A problem with volume-based data visualizations is reconciled using cortical surface visualizations in single subjects. Left: Example axial slice from a single subject. Middle: Zoomed portion surrounding the posterior inferotemporal sulcus indicated by the dotted red outline. Two regions of interest (green, red) are illustrated in different anatomical locations that would appear to be one contiguous region using large functional voxels (3-5mm on a side) and spatial smoothing (e.g. Figures 3-4). Notably, neurons close to one another in volume space due to the sulcal and gyral folding patterns may perform different functions (e.g. Figure 9). Right: Inflated cortical surface illustrating the precise anatomical locations of these ROIs. The distance on the gray matter between these two ROIs is 15mm rather than 5mm in volume space.
Weiner & Grill-Spector, Psychological Research 2013.

6. Understanding maps on multi-slice format
Visual field map
Color indicates stimulus eccentricity most effective at driving a response

7. Maps are easier to understand on the cortical surface

8. Surface based visualization provides a better understanding of anatomical features than the volume
OTS
CoS FG
lateral
lateral
medialFG
posterior
Weiner et al. NeuroImage, 2014

9. Surface based visualization provides a better understanding of anatomical features than the volume
OTS
CoS FG
lateral
lateral
medialFG
posterior
Weiner et al. NeuroImage, 2014

10. Surface based visualization provides a better understanding of anatomical features than the volume
OTS
CoS FG
lateral
lateral
medialFG
posterior
Weiner et al. NeuroImage, 2014

11. Surface based visualization provides a better understanding of anatomical features than the volume
MFS
OTS
CoS
(a)
(b)
medialFG
lateral FG
lateral
posterior FG
Figure 1. The mid-fusiform sulcus (MFS). Example right hemisphere from a ten-year old male. (a) Inflated cortical surface with sulci illustrated in dark gray. The MFS (outlined in red) is a longitudinal sulcus dividing the fusiform gyrus (FG) into lateral and medial partitions, flanked by the occipito-temporal sulcus (OTS) laterally and the collateral sulcus (CoS) medially (inset for location of zoomed portion). (b) The MFS, OTS, and CoS have a distinctive ω pattern on single coronal slices where the MFS is the shallower sulcus flanked by the much deeper CoS and OTS. Top: Example coronal slice from the position of the dotted line in (a). Bottom: Schematic of the ω pattern of the MFS, OTS, and CoS.
MFS= Mid fusiform sulcus
Weiner et al. NeuroImage, 2014

12. Understanding anatomical features may reveal functional-structural relationships
MFS
OTS
CoS
(a)
(b)
medialFG
lateral FG
lateral
posterior FG
1cm
Functional ROI
CoS-places/PPA
mFus-faces.FFA2
Weiner et al. NeuroImage, 2014

13. Surface based visualization enables comparing different maps on a common surface revealing relationships among maps and the cortical folding
Multiple representations superimposed on VTC are aligned to the mid fusiform sulcus (MFS)
Eccentricity bias
Domain specificity
Animacy
Real world object size
MFS
MFS
MFS
MFS
OTS
CoS
MFS
anterior
foveal
peripheral
faces
places
animate
inanimate
small
big
Weiner et al., 2014
Nasr et al., 2011
Haxby et al., 2011
Konkle et al., 2012
Grill-Spector & Weiner, Nature reviews neuroscience, 2014

14. Visualization techniques How to manual
Iso-voxel
Gray/white segmentation
Surface boundaries (Marching cubes)
Surface smoothing

15. If the original scan did not obtain isotropic voxels
(0.9375,0.9375,1.2) mm
If the original scan did not obtain isotropic voxels

16. Reslice data to obtain isotropic voxels
(0.9,0.9,0.9) mm
Reslice data to obtain isotropic voxels
Note noise in the image

17. (0.9,0.9,0.9) mm
Co-register and average multiple anatomical scans to reduce noise in anatomical image

18. Averaging makes segmentation of gray and white matter easier
Better separation
Gray
White
N voxels
N voxels
Intensity level
Intensity level

19. Visualization techniques How to manual
Iso-voxel
Segment brain into gray and white matter
Surface boundaries (Marching cubes)
Surface smoothing

20. Gray-white matter segmentation is essential
(b)

21. White and gray matter topology: Solids, handlesWM
CSF

22. White Matter Topology: Avoiding Cavities and Handles
Euler number is a means of determining the presence of such imperfections
Algorithms for locating and replacing exist (Kriegeskorte and Goebel)
Cavity (hole)
Handle
Euler Number and Connectivity Indexes of
a Three Dimensional Digital Picture
Junichiro TORIWAKI1* and Tatsuhiro YONEKURA2
1Department of Information Engineering, Graduate School of Engineering, Nagoya University,
Furo-cho, Chikusa-ku, Nagoya , Japan
2Department of Computer and Information Sciences, School of Engineering, Ibaraki University,
4-12-1, Nakanarusawa-cho, Hitachi, Ibaraki , Japan
* address:

23. Finding the First Gray Matter layer and determine the connectivity based on white matter
CSF WM GM

24. Thickening the Gray Matter and Retaining Connectivity
CSF WM GM

25. White, Gray, Connectivity
Classification
Gray matter
(a)
(c)
Connectivity
(b)
(d)
Gray nodes are connected if (a) they share a common parent, or (b) their parents are connected

26. The Importance of Being a Mesh
From the white matter classification we create
A surface mesh for visualization
A gray node connection graph
Gray nodes are properly connected
No connections across sulci
Visualization simplified
Measurement of area and distance

27. Visualization techniques How to manual
Iso-voxel
Segment brain into gray and white matter
Determine the surface boundaries (Marching cubes algorithm)
Surface smoothing

28. Creating the surface mesh from the classified voxels
Start by building a mesh at the boundary of the white matter;

29. Surface boundary identification “Marching squares”
Black: Shape
Green: Inside vertices
Red: New vertices. Draw boundary through these. There are basically only a few lines we can draw for the boundaries.
The first step is to calculate the corners that are inside the shape (represented by the green dots). We can now insert some vertices, since we know which points are inside and which are outside we can guess that a vertex should be positioned approximately halfway between an inside corner and any outside corners that are connected by the edge of a cell. The diagram shows the discussed vertices as small red dots

30. Surface boundary identification “Marching squares”
Black: Shape
Green: Inside vertices
Red: New vertices. Draw boundary through these. There are basically only a few lines we can draw for the boundaries.
The diagram shows the matching surface formed by joining the vertices with lines

31. Surface boundary identification
Each line is like one of the above, except for a possible rotation. Very simple.
The surface is an OK representation of the circle, but it is more jagged than we would like.
Smoothing is required.

32. For surfaces, there are 15 basic shapes that replace each cube pattern: Marching cubes
Unimportant differences
Rotation by any degree over any of the 3 primary axis
Mirroring the shape across any of the 3 primary axis
Inverting the state of all corners and flipping the normals of the relating polygons.
We are now of course dealing with cubes that have 8 corners and therefore a potential 256 possible combinations of corner status. However to simplify the algorithm we can reduce the complexity by taking into account cell combinations that duplicate under rotation, mirroring & inversion. Taking this into account we can resolve the original 256 combinations down to a total of 15 combinations (see image). The blue spheres denote corners that have tested as inside the shape and the green arrows denote the surface normals of the relevant triangles.

33. Visualization techniques How to manual
Iso-voxel
Segment brain into gray and white matter
Determine the surface boundaries (Marching cubes algorithm)
Smooth the resultant cortical surface

34. Surface boundary identification
Each line is like one of the above, except for a possible rotation. Very simple.
The surface is an OK representation of the circle, but it is more jagged than we would like.
Smoothing is required.

35. Boundary smoothing Many ideas about smoothing. Illustrated here:
Find the normals, smooth their orientation changes, and reconstruct the curve from normals

36. Smoothing Many ideas about smoothing. Illustrated here:
Find the normals, smooth their orientation changes, and reconstruct the curve from normals

37. Viewing the Gray Matter Surface

38. Smoothing the mesh
3D Graphical Tools for further smoothing, without destroying the good topological properties of the surface, are available in ITK gray (visualization toolkit) and other open graphics toolkits

39. Inflating the cortical mesh to better visualize activations buried in sulci

40. Inflating the cortical mesh to better visualize activations buried in sulci
Places > Faces, bodies, words, objects 3
T-value 8

41. Shading the surface: gyri and sulci
Local curvature is inverse to the radius of the circle that best fits the local curve
High curvature (+)
Low curvature (+)

42. Despite extreme smoothing topology is unchanged