1. Techniques for the analysis of GM structure: VBM, DBM, cortical thickness
Jason Lerch

2. Why should I care about anatomy?
Anatomy - behaviour
Verbal Learning
Nieman et al, 2007
Dickerson et al, 2008

3. The methods. Manual segmentation/volumetry.
Voxel Based Morphometry (VBM).
Deformation/Tensor Based Morphometry (DBM).
optimized VBM.
automated volumetry.
cortical thickness.

4. Processing Flow

5. Manual Segmentation Identify one or more regions of interest.
Carefully segment these regions for all subjects.
Statistics on volumes.

6. Segmentation example

7. And it was good. Cons: Labour intensive and time consuming.
Need to compute inter and intra rater reliability measures.
Pros:
Can be highly accurate.
Can discern boundaries still invisible to machine vision.

8. Preprocessing

9. Non-uniformity correction
Sled, Zijdenbos, Evans: IEEE-TMI Feb 1998

10. Voxel Classification T2 T1 PD

11. MS Lesion Classification

12. Positional Differences
Brain 2
Brain 1

13. Overall Size Differences

14. Spatial Normalization
Before Registration
After Registration

15. Voxel Based Morphometry
The goal: localize changes in tissue concentration.

16. Proportion of neighbourhood occupied by tissue class
Tissue Density
Proportion of neighbourhood occupied by tissue class

17. Real world example

18. VBM statistics Tissue density modelled by predictor(s).
I.e.: at every voxel of the brain is there a difference in tissue density between groups (or correlation with age, etc.)?
Millions of voxels tested, multiple comparisons have to be controlled.

19. Example 111 healthy children Aged 4-18
Paus et al., Science 283: , 1999
111 healthy children
Aged 4-18

20. And it was good. Pros: Extremely simple and quick.
Can look at whole brain and different tissue compartments.
By far most common automated technique - easy comparison to other studies.
Cons
Hard to explain change (WM? GM?).
Hard to precisely localize differences.
Hard time dealing with different size brains.

21. Tensor Based Morphometry
The goal: localize differences in brain shape.

22. Non-linear deformation

23. Deformations

24. Jacobians
Chung et al. A unified statistical approach to deformation-based morphometry. Neuroimage (2001) vol. 14 (3) pp

25. Childhoo d Music
Hyde et al., 2008

27. And it was good. Pros: Excellent for simple topology (animal studies).
Excellent for longitudinal data.
Does not need tissue classification.
Cons:
hard matching human cortex from different subjects.
Can be quite algorithm dependent.

28. Optimized VBM
The goal: combine the best of VBM and TBM

29. Modulation x

30. And it was good. Pros: More accurate localization than plain VBM.
Cons:
Dependent on non-linear registration algorithm.
Is it really better than either VBM or TBM alone?

31. Automatic segmentation
The goal: structure volumes without manual work.

33. Segmentation

34. Backpropagation

35. And it was good. Pros: A lot less work than manual segmentation.
Excellent if image intensities can be used.
Excellent if non-linear registration is accurate.
Cons:
Not always accurate for small structures.
Hard time dealing with complex cortical topology.

36. Cortical Thickness
The goal: measure the thickness of the cortex.

37. Processing Steps in Pictures

38. Processing Continued
4.5mm
1.0mm

39. Surface-based Blurring

40. And it was good.
Pros:
Extremely accurate localization of cortical change.
Sensible anatomical measure.
Sensible blurring.
Cons:
Only covers one dimension of one part of the brain.
Computationally very expensive and difficult.

41. automatic segmentation
Methods Summary
Method
Computation
Comparisons
Localization
Coverage
manual segmentation
Manual
one-few
depends
ROI
VBM
Easy
millions
poor
cerebrum
TBM
Moderate OK
brain
optimized VBM
automatic segmentation
few
large structures
cortical thickness
Hard
thousands
excellent
cortex

42. Advice, part 1 MRI anatomy studies need more subjects than fMRI
aim for at least 20 per group.
Acquire controls on same hardware.
Isotropic sequences are your friend.
T1 is enough unless you’re looking for lesions.

43. Advice, part 2 Group comparison, strong hypothesis?
manual segmentation.
automatic segmentation: FreeSurfer.
Group comparison, few hypotheses?
VBM: SPM, FSL, MINC tools.
Group comparison, cortical hypothesis?
cortical thickness: FreeSurfer, MINC tools.
sulcal morphology/shape: BrainVisa/anatomist.
Lesion/stroke?
classification: MINC tools.
Longitudinal data?
deformations: SPM (Dartel), ANTS, FSL (SIENA), MINC tools.

44. Acknowledgements Judith Rapoport Jay Giedd Dede Greenstein
Rhoshel Lenroot
Philip Shaw
Jeffrey Carroll
Michael Hayden
Harald Hampel
Stefan Teipel
Alan Evans
Alex Zijdenbos
Krista Hyde
Claude Lepage
Yasser Ad-Dab’bagh
Tomas Paus
Jens Pruessner
Veronique Bohbot
John Sled
Mark Henkelman
Matthijs van Eede
Jurgen Germann