Structural MRI analysis

Transcription

1 Structural MRI analysis volumetry and voxel-based morphometry cortical thickness measurements structural covariance network mapping Boris Bernhardt, PhD Department of Social Neuroscience, MPI-CBS 1

2 brain structure and structural MRI T1-weighted ADNI protocol 2

3 applications Brain organization Individual differences Plasticity Brain disorders 3

4 MRI volumetry idea: trace structure in 3D and count number of voxels 4

5 individual variations Project with Bram Cordemans (in preparation) 5

6 Volumetry processing: full chain input read segment 6

7 example studies Cascino et al. (1991) Ann Neu 7

8 example studies Bickart et al. (2011) Nat Neurosci 8

9 MRI volumetry - pros and cons Pros Focussed, simple methodology Biologically and anatomically meaningful Clinically well established Cons Gold standard remains manual segmentation Labor-intensive requires profound anatomical knowledge inter-rater, intra-rater variability Limited to individual anatomical regions 9

10 voxel-based morphometry (VBM) idea: align images globally and compare GM likelihood at each voxel 10

11 VBM processing: input 11

12 VBM processing: classification 12

13 VBM processing: classification 13

14 VBM processing: classification Different classification algorithms used in different packages often combined with intensity normalization classification commonly combination of intensity based clustering informed by spatial priors mixed tissue classes possible cleaning step (remove non-brain, connected classes) 14

15 VBM processing: normalization T 15

16 VBM processing: normalization T can be of varying complexity linear (scale, rotation, translation, possibly shears as well) non-linear (DCT, DARTEL, ANTs) Basic VBM: simple linear transformation More recent versions (e.g. SPM8) employ complex non-linear transformations Choice of template matters (MNI305, MNI152 linear, MNI152 non-linear,...) 16

17 VBM processing: normalization T 17

18 VBM processing: normalization 18

19 VBM processing: smoothing 19

20 VBM processing: inference Y = β0 + β1x+ ε GM = 1 + GROUP + ε GM = 1 + TIME + ε 20

21 exemplary VBM study Maguire et al. (2000) PNAS 21

22 exemplary VBM study Maguire et al. (2000) PNAS Taxi > Controls Volumetric validation Time as taxi driver Taxi < Controls 22

23 VBM processing: full chain input classification normalization smoothing 23

24 VBM interpretational uncertainty 24

25 VBM pros and cons Pros unbiased and whole-brain analysis freely available in SPM/FSL/MINC-tools fast, easy to run + many helpful add-ons good data exploration tool Cons does not respect cortical topology and anatomy interpretation often unclear clinically questionable 25

26 key methods paper Ashburner and Friston (1999) NeuroImage 26

27 key challenge paper Bookstein (2001) NeuroImage 27

28 measuring cortical thickness from MRI idea: identify GM/WM and GM/CSF, measure their distance 28

29 cortical thickness measurements raw data 29

30 cortical thickness measurements nuc-ed data 30

31 cortical thickness measurements classified data 31

32 cortical thickness measurements classified data 32

33 cortical thickness measurements wm surface 33

34 cortical thickness measurements surface extraction WM/GM surface 34

35 cortical thickness measurements surface extraction GM/CSF surface 35

36 cortical thickness measurements both surfaces 36

37 cortical thickness measurements manual corrections 37

38 cortical thickness measurements measurement GM/CSF surface WM/GM surface 38

39 surface-based processing smoothing 20 mm 39

40 cortical thickness measurements smoothing Surface-based 30mm FWHM Volume-based 30mm FWHM Lerch and Evans (2005) NeuroImage 40

41 surface-based processing surface-registration surfreg subject Template 41

42 surface-based processing surface-registration Fishl and Dale (1999) Hum Brain Mapp 42

43 surface-based statistical inference Controls t-map p-values Patients GLM mm

44 examples: cross-sectional group differences T = 1 + GROUP + ε Project with Leon Skottnik (in preparation) 44

45 examples: longitudinal change in patients T = 1 + random(subject) + GROUP + TIME + GROUP*TIME + ε TLE-HA TLE-NV all TLE n=27 Bernhardt et al. (2010) Neurology 45

46 cortical thickness pros and cons Pros automated, continuous, whole-cortex processing and measurement respect cortical topology direct, biologically meaningful, mm-measure surface-registration may increase sensitivity Cons heavy post-processing (4-25 hours/case) manual corrections necessary limited to (neo)cortex - bad segmentations in MTL 46

47 key methods papers Fischl and Dale (2000) PNAS MacDonald et al. (2000) NeuroImage 47

48 Surface-based analysis of subcortical shape 48

49 Surface-based analysis: subcortical shape modeling subcortical shape modeling methodology Bernhardt et al. (2012) Neurology 49

50 subcortical shape modeling methodology LTLE RTLE left right Bernhardt et al. (2012) Neurology 50

51 Results Clinical effects Bernhardt et al. (2012) Neurology 51

52 subcortical shape modeling pros and cons Pros subnuclear assessment possible analysis of volume change and positional change intrinsic shape correspondence Cons good segmentations necessary (see volumetry) analysis limited to boundary regions 52

53 Structural covariance analysis Covariance modulation by differences in self-reported empathy structural covariance network mapping idea: connections = thickness correlations between regions across subjects Bernhardt, Klimecki, Leiberg, Singer (2013) Cerebral Cortex 53

54 structural covariance network mapping Bernhardt, Klimecki, Leiberg & Singer (2013) Cerebral Cortex 54

55 methods Network cortical construction network construction Bernhardt et al. (2011) Cerebral Cortex 55

56 some graph-theoretical network metrics clustering coefficient (C) characteristic path length C = low C = high L = high L = low 56

57 excurse graph-theoretical network metrics random small-world regime regular lattice network LP CP LP CP LP CP 57

58 some correspondence with diffusion networks positive thickness correlation negative thickness correlation Gong et al. (2010) NeuroImage 58

59 some correspondence to resting-state networks mofc seed mofc seed 59

60 strong correspondence to maturational networks Alexander-Bloch, Bullmore, Giedd (2013) JNeurosci 60

61 examplary covariance papers Bernhardt, Valk, Silani, Bird, Frith & Singer (under review) 61

62 structural covariance network mapping pros and cons Pros T1-MRI are easy to acquire and to obtain relatively straightforward processing and modeling seeding from within grey matter regions good correspondence with maturational processes Cons requirement of relatively large samples group-level analysis 62

63 key methods papers Lerch et al. (2006) NeuroImage Alexander-Bloch et al. (2013) Nat Rev Neurosci 63

64 summary 64

65 summary assessing brain structure in vivo permits assessment of biological basis of individual differences structural networks structural plasticity patients with brain abnormalities different and complimentary tools can be used volumetry VBM Surface-based methods 65

66 future avenues Updates: Improve current pipelines MRI technology: Increase image quality Modeling: Multi-feature integration Biology Histological correlates of MRI signals Anatomical parcellations Structure-function relationships 66

67 thanks 67

68 68

69 backups Intensity normalization in SPM www0.cs.ucl.ac.uk/staff/g.ridgway/zurich/seg_vbm_zurich2010.ppt 69

70 backups Tissue classification in SPM www0.cs.ucl.ac.uk/staff/g.ridgway/zurich/seg_vbm_zurich2010.ppt 70

71 backups Tissue classification in SPM www0.cs.ucl.ac.uk/staff/g.ridgway/zurich/seg_vbm_zurich2010.ppt 71