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Voxel-based morphometry

Voxel-based morphometry. The methods and the interpretation (SPM based) Harma Meffert Methodology meeting 14 april 2009. Outline. General preprocessing steps Preprocessing Comparison two recent tools Data analysis Discussion about ‘ISSUES’. General preprocessing steps …. VBM.

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Voxel-based morphometry

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  1. Voxel-based morphometry The methods and the interpretation (SPM based) Harma Meffert Methodology meeting 14 april 2009

  2. Outline • General preprocessing steps • Preprocessing • Comparison two recent tools • Data analysis • Discussion about ‘ISSUES’

  3. General preprocessing steps …

  4. VBM anatomical scan segmentation normalisation smoothing General preprocessing steps

  5. VBM Normalisation step; a closer look • Determine parameters

  6. VBM Normalisation step; a closer look • Determine parameters • Deform brain to fit template

  7. VBM Unmodulated Modulated Unmodulated * Volume before warping / Volume after warping Normalisation step; a closer look • Determine parameters • Deform brain to fit template • Unmodulated (concentration) • Modulated (volumetric)

  8. Preprocessing … Protocols and toolboxes

  9. Overview ‘toolboxes’ and protocols • Standard VBM – SPM99 / SPM2 • Optimised VBM – SPM99 / SPM2 • VBM with unified segmentation – SPM5 • VBM2 toolbox for SPM2 • VBM5 toolbox for SPM5 • Dartell • …

  10. Standard VBM – SPM99 / SPM2 Normalisation Segmentation Gray matter White matter modulation modulation smoothing smoothing Analysis Analysis Mechelli et al. 2005

  11. Optimised VBM – SPM99 / SPM2 Segmentation Gray matter White matter Normalisation to GM template Normalisation to WM template Apply norm. par. to raw image Apply norm. par. to raw image modulation modulation smoothing smoothing Analysis Analysis Mechelli et al. 2005

  12. VBM with unified segmentation – SPM5 Normalisation / segmentation modulation smoothing Analysis Tissue classification, image registration and bias correction within one model

  13. VBM5 toolbox in SPM5 MRF prior probability Noise reduction with Markov Random Field

  14. Summary: Segmentation and Normalisation Options and considerations: • Normalisation before segmentation • Optimized order (norm  segm  norm) • Unified segmentation (SPM5) • Unified segmentation with the use of customized priors (VBM5) • Unified segmentation without the use of priors for tissue classification (VBM5) • Hidden Markov Random Field (VBM5) • Center of mass as origin doesn’t work

  15. Summary: Modulation Options, considerations and questions • Unmodulated ≈ ‘concentration’ • Modulated ≈ ‘volume’ • Modulation of … • non-linear effects only • affine and non-linear effects (no correction for brain size afterwards) • Smoothing • Less smoothing in modulated images

  16. Comparison two recent tools…

  17. VBM5 vs SPM5

  18. Data analysis …

  19. Data-analysis: Considerations • Corrections for multiple comparisons with local maxima of the t statistic • GLM with SPM, SnPM, machine learning algorithms • Global or localized inferences? Use of covariates • Non-stationary cluster extent correction

  20. Voxel-based morphometry … The Issues!

  21. Issue 1: Unmodulated images… • Compatible with modulated images? • Just registration errors? • Very dependend on used toolbox? • Normalisation proces: Adding or removing voxels… how does that happen?

  22. Issue 2: Covariates • If you modulate for both affine and non-linear effects you do not have to correct for global brain size…. • If global brain size is correlated with ‘treatment’ it is not a good covariate because it will mask ‘treatment’ effects

  23. Issue 3: What do the tissue labels mean • If you add up probabilities in one voxel across different tissue types they can be >1 • Could you use white and gray maps to determine the relative amount of gray for example

  24. Issue 4: How do you assess the quality of segmentation • VBM5 has the option to chack sample homogeneity • Furthermore it is visual inspection

  25. Literature • Ashburner, J. and K. J. Friston (2000). "Voxel-based morphometry--the methods." Neuroimage 11(6 Pt 1): 805-21. • Ashburner, J. and K. J. Friston (2001). "Why voxel-based morphometry should be used." Neuroimage 14(6): 1238-43. • Ashburner, J. and K. J. Friston (2005). "Unified segmentation." Neuroimage 26(3): 839-51. • Bookstein, F. L. (2001). ""Voxel-based morphometry" should not be used with imperfectly registered images." Neuroimage 14(6): 1454-62. • Devlin, J. T. and R. A. Poldrack (2007). "In praise of tedious anatomy." Neuroimage 37(4): 1033-41; discussion 1050-8. • Good, C. D., I. S. Johnsrude, et al. (2001). "A voxel-based morphometric study of ageing in 465 normal adult human brains." Neuroimage 14(1 Pt 1): 21-36. • Mechelli, A., C. J. Price, et al. (2005). "Voxel-based morphometry of the human brain: Methods and applications." Current Medical Imaging Reviews 1(2): 105-113. • Ridgway, G. R., S. M. Henley, et al. (2008). "Ten simple rules for reporting voxel-based morphometry studies." Neuroimage 40(4): 1429-35. • Ridgway, G. R., R. Omar, et al. (2009). "Issues with threshold masking in voxel-based morphometry of atrophied brains." Neuroimage 44(1): 99-111.

  26. NeuroImaging Center – Social Brain lab: • Prof. Dr. Christian Keysers • Dr. Valeria Gazzola • MSc. Jojanneke Bastiaansen • Other members of the lab • Department of Psychiatry, UMCG • Prof. Dr. Hans den Boer • FPC Dr. S. van Mesdag • Dr. Arnold Bartels • Dr. Marinus Spreen • Research department

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