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Techniques for the analysis of GM structure: VBM, DBM, cortical thickness

Techniques for the analysis of GM structure: VBM, DBM, cortical thickness. Jason Lerch. Why should I care about anatomy?. Anatomy - behaviour. Verbal Learning. Nieman et al, 2007. Dickerson et al, 2008. The methods. Manual segmentation/volumetry. Voxel Based Morphometry (VBM).

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Techniques for the analysis of GM structure: VBM, DBM, cortical thickness

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  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. Brain 2 Brain 1 Positional Differences

  13. Overall Size Differences

  14. Spatial Normalization Before Registration After Registration

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

  16. 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 Paus et al., Science 283:1908-1911, 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. 595-606

  25. Childhood Music Hyde et al., 2008

  26. 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.

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

  28. Modulation x

  29. 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?

  30. Automatic segmentation • The goal: structure volumes without manual work.

  31. Segmentation

  32. Backpropagation

  33. 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.

  34. Cortical Thickness • The goal: measure the thickness of the cortex.

  35. Processing Steps in Pictures

  36. Processing Continued 4.5mm 1.0mm

  37. Surface-based Blurring

  38. 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.

  39. Methods Summary

  40. 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.

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

  42. 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

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