1. 1 Detecting Subtle Changes in Structure Chris Rorden –Voxel Based Morphometry Segmentation – identifying gray and white matter Modulation- adjusting for normalization’s spatial distortions. –Diffusion Tensor Imaging Measuring white matter integrity Tractography and analysis. Many images are from Christian Gaser. You can see his presentations and get his VBM scripts from these sites: fmri.uib.no/workshops/2006/mai/fmri/index.shtml dbm.neuro.uni-jena.de/home/

2. 2 Voxel Based Morphometry Most lectures in course focus on functional MRI. However, anatomical scans can also help us infer brain function. –Do people with chronic epilepsy show brain atrophy? –Which brain regions atrophy with age? –Do people with good spatial memory (taxi drivers) have different anatomy than other people? Voxel based morphometry is a tool to relate gray and white matter concentration with medical history and behavior

3. 3 Morphometry Morphometry examines the shape, volume and integrity of structures. Classically, morphometry was conducted by manually segmenting a few regions of interest. Voxel based morphometry conducts an independent statistical comparison for each voxel in the brain. Images from Christian Gaser

4. 4 Voxel Based Morphometry VBM has some advantages over manual tracing: –Automated: fast and not subject to individual bias. –Able to examine regions that are not anatomically well defined. –Able to see the whole brain –Normalization compensates for overall differences in brain volume, which can add variance to manual tracing of un-normalized images.

5. 5 VBM disadvantages VBM has clear disadvantages –Crucially depends on accurate normalization. –Low power: gray matter random fields are very heterogenous (individual patterns of sulcal folding registration is always poor. –Crucially depends on a priori probability maps. –Assumes normal gray-white contrast. Focal Cortical Dysplasia –Looks for differences in volume, can be disrupted if shape of brain is different: problem for developmental disorders

6. 6 Segmentation Start with high quality MRI scan Classify tissue types (gray matter in this example)

7. 7 Partitioning Tissue Types VBM segments image into three tissue types: gray matter, white matter and CSF. –Typically done on T1 scans (best spatial resolution, good gray-white contrast). –Only three tissue types: will not cope with large lesions. –Probability map: each voxel has a 0..100% chance of being one of the 3 tissue types. T1T1T1T1whitegrayCSF Images from Christian Gaser

8. 8 Segmentation I: Image Intensity CSF WM GM back- ground Image intensity frequency estimate for GM p=0.95 p=0.05 Images from Christian Gaser

9. 9 Segmentation II: Voxel location Maximization of a posteriori probability: Bayesian approach (expectation maximization) Analogy: –We know that last year there were 248 of 365 days with rain in Norway (p=0.68) –the conditional (or posterior) probability for rain in Bergen will be p>0.5 T1T1T1T1WMGMCSF Probability maps (n=152) Images and text from Christian Gaser

10. 10 Segmentation overview Intensity based estimate for GM p=0.95 p=0.95 p=0.90 p=0.05 Final result a priori GM map p=0.95 p=0.05 Source Image

11. 11 Voxel Based Morphometry Steps

12. 12 Homogeneity correction crucial Field inhomogeneity will disrupt intensity based segmentation. Bias correction required. no correction T1T1 WMGMEstimate

13. 13 Normalization is crucial Poor normalization has two problems –Image will not be registered with a priori map = poor segmentation. –Images from different people will not be registered: we will compare different brain areas. Custom template and prior is useful –Accounts for characteristics of your scanner. –Accounts for characteristics of your population (e.g. age). –Must be independent of your analysis: Either formed from combination of both groups (control+experimental) or from independent control group.

14. 14 Two step segmentation segmentation II customized template averaging MNI template segmentation I norma- lization segmentation II Step I: Creation of customized template segmentation I norma- lization Step II: Optimized segmentation

15. 15 Image cleanup segmented mask maskedT1T1

16. 16 Overview of ‘Optimized VBM’ T1T1 normalizedsegmented IIsmoothedsegmented Imasked customized template mask

17. 17 VBM designs Longitudinal VBM: –Sensitive way to detect atrophy through time. Using the same individual reduces variability. Cross sectional studies –Can compare two distinct populations –Can also examine atrophy through time, though will require more people than longitudinal VBM. Most VBM studies use t-test (two group or timepoints), but correlational analysis also powerful.

18. 18 SPM5 segmentation Unified segmentation Iterated steps of segmentation estimation, bias correction and warping Impact Warping of prior images during segmentation makes segmentation more independent from size, position, and shape of prior images much slower than SPM2 40 iterations segmentation 40 iterations bias correction 20 iterations warping no significant change of estimate significant change of estimate

19. 19 Voxel Based Morphometry We can statistically analyze gray matter atrophy Epilepsy

20. 20 Segmentation Problem If someone has atrophy, normalization will stretch gray matter to make brain match healthy template. This will reduce ability to detect differences Normalization will squish this region Normalization will stretch this region

21. 21 Image Modulation –Analogy: as we blow up a balloon, the surface becomes thinner. Likewise, as we expand a brain area it’s volume is reduced. SourceTemplate Modulated Without modulation

22. 22 Image Modulation Optimized Segmentation can adjust for distortions caused during normalization. Areas that had to be stretched are assumed to have less volume than areas that were compressed. –Corrects for changes in volume induced by nonlinear normalization –Multiplies voxel intensities by a modulation matrix derived from the normalization step –Allows us to make inferences about volume, instead of concentration.

23. 23 VBM and developmental syndromes Williams Syndrome –Developmental syndrome: Chromosome 7 –Manual Morphology shows 8-18% decrease in posterior GM/WM Most consistent finding is reduced intra-parietal sulcus depth and superior parietal lobe volume (see figure) Relatively preserved frontal GM/WM Creates unique shape –Unique spatial distribution of gross volume loss influences VBM results depending on whether modulation is used Eckert et al. 2006b,c Control WS

24. 24 Modulation and shape Eckert et al., 2006a Shape differences influence modulated data. Deformation Based Morphometry can identify shape/gross volumetric differences.

25. 25 Modulation is optional and controversial Modulation will smooth images, specificity will decrease Alternatively, you can covary overall brain volume by including GM or GM+WM as nuisance regressor. Example showing danger of modulation. This image comes from an elderly participant, with relatively large ventricles. Normalization adjusts ventricle size, but the deformations are spatially smooth, so tissue near the ventricles (e.g. caudate) are also being spatially compressed. [Deformations exaggerated for exposition]

26. 26 DBM (from Henson) Deformation-based Morphometry examines absolute displacements. E.G. Mean differences (mapping from an average female to male brain).

27. 27 Cortical Thickness New methods can complement VBM. Freesurfer’s cortical thickness is powerful tool. Requires very good T1 scans. Modulated VBMFreesurfer Age-related declines in gray matter volume and cortical thickness

28. 28 VBM comments VBM findings are first step in understanding strucutural changes. Methods are a work in progress. –www.tina-vision.net/docs/memos/2003-011.pdf –Bookstein, 2001 –Davatzikos, 2004 –http://fmri.uib.no/workshops/2006/mai/fmri/index.shtml – Christian Gaser Markov Random Fields dbm.neuro.uni- jena.de/home/

29. 29 Diffusion Weighted Imaging T1/T2 scans do not show acute injury. Diffusion weighted scans do. DW scans identify areas of permanent injury Measures random motion of water molecules. –In ventricles, CSF is unconstrained, so high velocity diffusion –In brain tissue, CSF more constrained, so less diffusion. T2 DW

30. 30 Diffusion Tensor Imaging (DTI) DTI is an extension of DWI that allows us to measure direction of motion. DTI allows us to measure both the velocity and preferred direction of diffusion –In gray matter, diffusion is isotropic (similar in all directions) –In white matter, diffusion is anisotropic (prefers motion along fibers).

31. 31 DTI The amount of diffusion occurring in one pixel of a MR image is termed the Apparent Diffusion Coefficient (ADC) or Mean Diffusivity (MD). The non-uniformity of diffusion with direction is usually described by the term Fractional Anisotropy (FA). MD differsFA differs

32. 32 What is a tensor? A tensor is composed of three vectors. –Think of a vector like an arrow in 3D space – it points in a direction and has a length. The first vector is the longest – it points along the principle axis. The second and third vectors are orthogonal to the first. Sphere: V1=V2=V3 Football: V1>V2 V1>V3 V3 = V2 ???: V1>V2>V3

33. 33 Diffusion Tensor Imaging To create a tensor, we need to collect multiple directions. Typically 12-16 directions. More directions offer a better estimate of optimal tensor.

34. 34 DTI MD FAPrinciple Tensor Vector DTI Tutorial

35. 35 Tractography DTI can be used for tractography. This can identify whether pathways are abnormal. Inferior frontal occipital tract

36. 36 Kissing or crossing? Modelling each voxel as a tensor has limitations. Cannot model fiber crossings.