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/

1

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

2

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 3

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.

4

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

5

Segmentation

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

6

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.

Images from Christian Gaser

T 1 gray white CSF

7

Segmentation I: Image Intensity

estimate for GM p=0.95

p=0.05

Image intensity back ground CSF Images from Christian Gaser GM WM 8

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

Probability maps (n=152)

Images and text from Christian Gaser

T 1 GM WM CSF

9

Segmentation overview

Source Image Intensity based estimate for GM p=0.95

Final result p=0.05

p=0.95

p=0.90

p=0.05

a priori

GM map p=0.95

10

Voxel Based Morphometry Steps

11

Homogeneity correction crucial

  Field inhomogeneity will disrupt intensity based segmentation. Bias correction required.

no correction Estimate T 1 GM WM

12

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.

13

Two step segmentation

segmentation I segmentation II Step I: Creation of customized template segmentation I segmentation II averaging norma lization customized template Step II: Optimized segmentation norma lization MNI template

14

Image cleanup

T 1 segmented masked mask

15

T 1

Overview of ‘Optimized VBM’

segmented I normalized segmented II masked smoothed customized template mask

16

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.

17

SPM5 segmentation

40 iterations segmentation 40 iterations bias correction • 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 20 iterations warping significant change of estimate no significant change of estimate

18

Voxel Based Morphometry

 We can statistically analyze gray matter atrophy

Epilepsy 19

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 20

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.

Without modulation Source Template Modulated 21

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.

22

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

Control WS

Eckert et al. 2006b,c

23

Modulation and shape

 Shape differences influence modulated data.  Deformation Based Morphometry can identify shape/gross volumetric differences.

Eckert et al., 2006a 24

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]

25

DBM (from Henson)

 Deformation-based Morphometry examines absolute displacements.

 E.G. Mean differences (mapping from an average female to male brain).

26

Cortical Thickness

 New methods can complement VBM.

 Freesurfer’s cortical thickness is powerful tool.

 Requires very good T1 scans.

Modulated VBM Freesurfer Age-related declines in gray matter volume and cortical thickness

27

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/

28

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 29

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).

30

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 differs FA differs 31

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 32

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.

33

MD

DTI

FA DTI Tutorial Principle Tensor Vector

34

Tractography

 DTI can be used for tractography.

 This can identify whether pathways are abnormal.

Inferior frontal occipital tract

35

Kissing or crossing?

 Modelling each voxel as a tensor has limitations.

 Cannot model fiber crossings.

36