MRI : Clinical and research applications

1. «Medical Imaging» PhD Course May 2014 MRI: Clinical and research applications

2. «Medical Imaging» PhD Course May 2014 OBJECTIVES Introduce you to the world of Magnetic Resonance Imaging applications for either CLINICAL and RESEARCH Explain you what MRI can tell you about brain structure and functioning Give you the fundamentals to properly read and understand a scientific paper employing MRI technique

3. «Medical Imaging» PhD Course May 2014 SUMMARY STRUCTURAL MRI Gray matter density (VBM) thickness White matter voxel-wise integrity (TBSS) Tractography FUNCTIONAL MRI task-related resting-state Within-networks functional connectivity (FC) analysis Whole brain analysis FC (seed-based) Between-networks (connectoma)

4. «Medical Imaging» PhD Course May 2014 MOST IMPORTANT SEQUENCES and their parameters ANATOMICAL MRI (T1) Voxel size: 1x1x1 Slices: 140-160 (full head coverage) DIFFUSION TENSOR IMAGING (DTI) Voxel size: 2x2x2 Slices: 60-80 #directions: 30+ ECHO-PLANAR IMAGING (EPI) Voxel size: 4x4x4 Slices: 35-45 TR: 2-3 seconds

5. «Medical Imaging» PhD Course May 2014 • The problem: • You want to compare two MRI sequence of the same subject • You want to compare different subjects in a group-analysis • You have to co-registrate each images in another space Co-registration functional anatomical template Each co-registration produces a co-registration matrix

6. «Medical Imaging» PhD Course May 2014 Within the same subject 6 DOF in 3D – 3 Rotations – 3 Translations

7. «Medical Imaging» PhD Course May 2014 Between different subjects 12 DOF in 3D – 3 Rotations – 3 Translations – 3 Scalings – 3 Skews/Shears 7 DOF in 3D – 3 Rotations – 3 Translations - 1 Global scaling

8. «Medical Imaging» PhD Course May 2014 In FSL DOF controlled by array of control points or knot (splines) What you get is a deformation matrix which represent the morphing that must be applied for each voxel. Non-linear registration Along a specific direction we get: mm

9. «Medical Imaging» PhD Course May 2014 Normalization When : you want to compare different subjects in a group-analysis Solution: Standard anatomical template are available. Made by merging several subject or repeating Scan several times on the same subjects. Individual subject T1 is always recorded to be Co-registered with these template. Then fMRI / DTI sequence are registered to Individual T1. The two transformation are concatenated and Applied to fMRI/DTI.

10. «Medical Imaging» PhD Course May 2014 Normalization Individual 1x1x1 mm Template 2x2x2 mm

11. «Medical Imaging» PhD Course May 2014 ANATOMICAL MRI • Grey matter density (VBM) • Cortical Thickness • White matter integrity

12. «Medical Imaging» PhD Course May 2014 This technique assess and compare the GRAY MATTER DENSITY The exact interpretation of gray matter density is complicated, and depends on the preprocessing steps used *  IT IS NOT interpretable as neuronal packing density or other cytoarchitectonic tissue properties *  The hope is that changes in these microscopic properties may lead to macro- or mesoscopic VBM-detectable differences Voxel Based Morphometry (VBM) • CHARACTERISTICS: • No a priori required = whole-brain unbiased analysis • Automated = Reproducible intra/inter-rater • QUICK! • Localization of the GM differences across subjects => non-linear registration

13. «Medical Imaging» PhD Course May 2014 VBM 1: Brain Segmentation • Subdivision of brain volume in three tissues: • GRAY MATTER • WHITE MATTER • LIQUOR Each tissue is characterized by a specific intensity Many voxel cannot be uniquely classified

14. «Medical Imaging» PhD Course May 2014 VBM 1: Brain Segmentation GRAY M. WHITE M. LIQUOR The output is a probability map, which can be threshold or not

15. «Medical Imaging» PhD Course May 2014 VBM 2: Smoothing • Even setting a threshold, data must be smoothed, because: • Anatomies must be compared between subjects and thus need to be less sensitive to individual gyral/sulci geometry. • The data will be more Gaussian and closer to a continuous random field, more suitable for statistical analysis

16. «Medical Imaging» PhD Course May 2014 VBM 3: GM normalization VBM 4: Template Creation VBM 5: Non-linear registration ( & modulation) of individual MRI to the template Segmentation normalization modulation • TRADE-OFF: • not enough non-linear = no correspondence • too much non-linear = no difference • Modulation can be applied to correct for aggressive non linear morphing which tend to • reduce volume differences

17. «Medical Imaging» PhD Course May 2014 VBM 6: Statistical analysis

18. «Medical Imaging» PhD Course May 2014 ANATOMICAL MRI • Grey matter density (VBM) • Cortical Thickness • White matter integrity

19. «Medical Imaging» PhD Course May 2014 Cortical Thickness (surface approach) • The cortical surface is represented by a finite element model using triangles to cover the cortical surface. • The reconstruction process assign an xyz to each corner of each triangle. • Then it is possible to characterize the entire cortical surface in terms of: • Area • Distance • Curvature • Thickness.

20. pial surface «Medical Imaging» PhD Course May 2014 Cortical Thickness : measure • Distance between white and pial surfaces along normal vector. • 1-5mm

21. «Medical Imaging» PhD Course May 2014 Cortical Thickness : Aging results Salat, et al, 2004, Cerebral Cortex

22. «Medical Imaging» PhD Course May 2014 ANATOMICAL MRI • Grey matter density (VBM) • Cortical Thickness • White matter integrity

23. «Medical Imaging» PhD Course May 2014 White matter is composed of bundles of myelinated nerve cell (axons), which connect various grey matter areas (nerve cell bodies) of the brain to each other, and carry nerve impulses between neurons. Myelin acts as an (hydrophobic) insulator increasing the speed of transmission of all nerve signals. Around 160.000 Km of myelinated fibers are present in healthy adult.

24. «Medical Imaging» PhD Course May 2014 Three main classes of tracts are present: Projection tracts extend vertically between higher and lower brain and spinal cord centers, and carry information between the cerebrum and the rest of the body.: Association: connect different regions within the same hemisphere of the brain. Long one connect different lobes of a hemisphere. short: connect different gyri within a single lobe Commissural : cross from one cerebral hemisphere to the other through bridges called commissures.

25. «Medical Imaging» PhD Course May 2014 «Medical Imaging» PhD Course May 2014 How to model tracts :Diffusion Tensor Imaging (DTI) CSF GM λ1 v1 λ2 v2 fiber bundle - For each voxel a diffusion Tensor Ellipsoid is estimated - λ are the EIGENVALUES of the diffusion tensor in the coordinate longitudinal and trasverse to the direction of max diffusivity

26. «Medical Imaging» PhD Course May 2014 Quantification of the Diffusion Tensor MD = Dxx + Dyy + Dzz / 3 = λ1 + λ2+ λ3 / 3 Eigenvalues Mean Eigenvalues Variance FA encodes how strongly directional is diffusion => hence good marker for WM integrity FA MD longitudinal ADC transverse ADC λ1 (λ2 + λ3 / 2)

27. «Medical Imaging» PhD Course May 2014 v1 map : Principal Diffusion Direction Assumption!! Direction of maximum diffusivity in voxels with anisotropic profile is an estimate of the major fibreorientation.

28. «Medical Imaging» PhD Course May 2014 1) Eddy Current Correction 2) Diffusion Tensor Model fit Colors represent the main direction of the fibre BLUE: ventral-dorsal (like in the CST) RED: mesial-lateral (like in the Corpus Callosum) GREEN: antero-posterior (like Diffusion Data processing

29. «Medical Imaging» PhD Course May 2014 Instead of using a simple VBM-style evaluation of FA/MD vixel-wise difference, this method uses the main tracts to align subjects data Use medium-DoF nonlinear reg to pre-align all subjects’ FA (nonlinear reg: FNIRT) Create mean FA image (no smoothing) TBSS: Tract-Based Spatial Statistics Threshold Mean FA Skeleton Skeletonize mean FA

30. «Medical Imaging» PhD Course May 2014 • Individual tract projection to mean skeleton For each subject’s warped FA, fill each point on the mean-space skeleton (yellow) with nearest maximum FA value (i.e., from the centre of the subject’s nearby tract)

31. «Medical Imaging» PhD Course May 2014 • Voxel-wise statistics Do cross-subject voxelwise stats on skeleton-projected FA and Threshold (e.g., permutation testing, including multiple comparison correction)

32. «Medical Imaging» PhD Course May 2014 TBSS : WHOLE BRAIN results ALS UMN score is a clinical measure of Upper Motor Neuron involvement FA reduces with AGE almost uniformly FA reduces with UMN only in the CST and in transcallosal connection between the two M1 personal data: 55 ALS patients Age: 30-70

33. «Medical Imaging» PhD Course May 2014 TRACTOGRAPHY

34. «Medical Imaging» PhD Course May 2014 Tractography: the main problem, fibers crossing • In voxels containing two crossing bundles, the tensor ellipsoid is pancake-shaped • (oblate, planar tensor). • In voxels containing three crossing bundles, the tensor ellipsoid is spherical. • ==> In these areas, DTI v1 is meaningless. Spherical Tensor λ1=λ2=λ3 Prolate Tensor λ1 >> λ2, λ3 Oblate Tensor λ1=λ2 >> λ3

35. «Medical Imaging» PhD Course May 2014 TBSS & Tractography Instead of calculating FA/MD values in all the brain we can restrict the TBSS analysis to well-known or specific tracts of interest. So....we can reconstruct our tract of interest create a binary mask repeat TBSS only in that mask We then can perform statistical analysis: voxel-wise: tract restricted TBSS NOT voxel-wise: we calculate tract FA/MD mean value and analyze it

36. «Medical Imaging» PhD Course May 2014 FUNCTIONAL MRI • Functional connectivity at rest • Task related fMRI

37. «Medical Imaging» PhD Course May 2014 Resting State Networks (RSN)

38. «Medical Imaging» PhD Course May 2014 DMN «functions» Thus is anti-correlated with brain systems that are used for focused external visual attention. Task where DMN in involved

39. «Medical Imaging» PhD Course May 2014 Uddin et al. 2009 Changes in anti-phasic relation • Such physiological anti-phasic relation • Reduces with AGE • Can predict performance • Can predict impulsivity trait • Can be associated to pathology Kennedy et al. (2006). less task-induced deactivation => lower social impairment scores (Lustig et al. 2003) Reduced task-induced deativation in AD

40. «Medical Imaging» PhD Course May 2014 MELODIC analysis • Without any apriori hypothesis, the MELODIC algorithms is able to decompose BOLD signal into indipendent components • This procedure create a GROUP TEMPLATE • Then, in order to perform statistical analysis, individual fMRI data are projected on group components with the following procedure • DUAL REGRESSION • Estimate the effect size of each component for every individual subject. • It consists of : • spatial regression using the ICA maps as regressors • temporal regression using the representative time courses as regressors. • The resulting maps are voxel-wise regression coefficients of each component’s representative time course

41. «Medical Imaging» PhD Course May 2014 • Prepropress data • filtering • Motion correction • Regress out nuisance signal • WM, CSF, Brain • Define a set of ROI from • Other analysis • Literature • Co-register ROI to individual EPI space • Extract ROI time-series • Define model • What we get is a subjects FC map standard ROI Seed-Based Functional Connectivity (SBFC) ROI Example of a ROI In PCC

42. «Medical Imaging» PhD Course May 2014 Default Mode Network (DMN) vs action-related RSN SBFC with PCC and vmPFC ROI Uddin et al. 2009

43. «Medical Imaging» PhD Course May 2014 Comparison between ICA & SBFC Seed-based • Good: allows you to ask a straightforward question and get an easily interpretable answer • Bad: only tells you about the seeds you ask about (though see Cohen’s gradient-based parcellation) ICA • Bad: some components can be hard to interpret, and you may not get a component that clearly relates to the brain-bit you cared about • Bad: run-run variability in decomposition • Good: the entire dataset is decomposed into multiple different networks and other sources simultaneously

44. «Medical Imaging» PhD Course May 2014 FUNCTIONAL MRI • Function connectivity at rest • Task related fMRI

45. «Medical Imaging» PhD Course May 2014 END

46. «Medical Imaging» PhD Course May 2014 Preprocessing Motion correction Slice-Timing Temporal filtering

47. «Medical Imaging» PhD Course May 2014 • The problem: • MRI measures involve thousands of voxes (10M for T1, 144K for fMRI, 6M for DTI) • Statistical comparison are voxel-wise, in order to satisfy Statistical Rules which ask you to correct for the number of comparison • Correction types: • Voxel level • Cluster level • Correction algorythms: Correction for multiple comparison

48. «Medical Imaging» PhD Course May 2014 Pipeline FSL-VBM: Brain segmentation GM normalization Template creation Non-linear registration of individual 2 template Modulation and smoothing Statistical analysis Pipeline DARTEL: Brain segmentation GM normalization Modulation Smooth Statistical analysis

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