Presentation on theme: "1 Detecting Subtle Changes in Structure Chris Rorden –Voxel Based Morphometry Segmentation – identifying gray and white matter Modulation- adjusting for."— Presentation transcript:
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 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 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 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 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 Segmentation Start with high quality MRI scan Classify tissue types (gray matter in this example)
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 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 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 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
12 Homogeneity correction crucial Field inhomogeneity will disrupt intensity based segmentation. Bias correction required. no correction T1T1 WMGMEstimate
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 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
17 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
18 SPM8 new segmentation SPM8’s default segmentation similar to SPM5 There is a hidden new segmentation –From Graphics window, Choose Tasks/SPM/Tools/NewSegment –Allow multiple channels, for example combine T1 and T2 for better tissue classification –Includes more tissue types (gray, white, CSF, bone, other soft tissue, air. Not as finicky with regards to starting estimate
19 Building a better template: data 25 participants scanned –High resolution: 0.85mm 3 vs 1mm 3 –Large FOV: 320x320 matrix vs 256x256 matrix –Both T1 and T2-weighted images for each person (with same orientation and coverage) –Uses revolutionary 3D SPACE sequence that provides high resolution –T2 has opposite contrasts for CSF (bright) and bone (dark) T1 T2
20 Building a better template: normalization SPM8 affine coregistration to MNI space. ANTS used to generate mean image (25 Core i7 * 70 hours) Mean image normalized to MNI space using SPM8 ‘new seg’. Subj1 Subj2 Mean
21 Building a better template: segmentation Crude TPM created: –Default newseg TPM for brain case –Neck region generated from mean image Refined TPM created: –Each individual’s T1+T2 scans segmented using newseg –TPM created from mean segmentation –TPM manually edited to remove errors –TPM smoothed by 2mm FWHM –Repeat previous steps four times. TPM : CSF
22 Building a better template: results Our new template accurately segments tissues Includes neck regions Works well, even if only provided with a T1 scan –We no longer need a CT scan for bone, or even a T2 scan… Single subject segmentation
23 Building a better template: future MUSC ‘brain attack’ CT protocol: –identify people with suspected acute stroke who did not have a stroke. –People are identified by neurologist who ensures no subsequent brain injury or other structural abnormalities
24 Voxel Based Morphometry We can statistically analyze gray matter atrophy Epilepsy
25 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
26 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
27 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.
28 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
29 Modulation and shape Eckert et al., 2006a Shape differences influence modulated data. Deformation Based Morphometry can identify shape/gross volumetric differences.
30 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]
31 DBM (from Henson) Deformation-based Morphometry examines absolute displacements. E.G. Mean differences (mapping from an average female to male brain).
32 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
33 VBM comments 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. VBM findings are first step in understanding structural changes. –www.tina-vision.net/docs/memos/2003-011.pdf –Bookstein, 2001 –Davatzikos, 2004
34 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
35 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).
36 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
37 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
38 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.