Structural connectivity of the brain measured by diffusion tensor imaging [DTI]
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1 Structural connectivity of the brain measured by diffusion tensor imaging [DTI] Lars T. Westlye CSHC / Centre for Advanced Study PSY l.t.westlye@psykologi.uio.no
2 ~ 40 % of the brain is white matter (WM) 2
3 Cortex (GM) WM 3
4 Cortex (GM) Cortex: cell bodies WM: myelinated axons At the age of 20, the brain has a total myelinated fibre length of about km WM By the age of 80 this has been reduced by almost 50 % 4
5 Myelin is an electrically insulating phospholipid layer surrounding the axon Myelin is produced by glial cells - CNS: Oligodendrocytes - PNS: Schwann cells Sherman & Brophy, 2007 Nat Rev Neuroscience 5
6 Neurobiology of white matter Electrochemical signaling 6
7 Neurobiology of white matter 7
8 Neurobiology of white matter Cross-section of myelinated axons (Rhesus monkey) 1 µm Peters 2009, Frontiers in neuroanatomy 8
9 Neurobiology of white matter Myelin sheaths covering the axons facilitate speeded and synchronous neural communication Degeneration of myelin sheaths may cause sensory, perceptual, cognitive and/or motor dysfunctions Myelin degeneration can be seen in various diseases (including MS), but also in normal/healthy aging 9
10 Imaging the white matter using diffusion tensor imaging What is diffusion? Diffusion is the random motion of a given entitiy causing the distribution of the entity to spread in space. 10
11 Imaging the white matter using diffusion tensor imaging DTI: MR sequences optimized to quantify degree and direction of the naturally occuring diffusion of water Random diffusion of water molecules in non-hindered environments: A molecule diffuses randomly from green to red in time t The probability distribution of after time T X is visualized as a circle with r = x 11
12 Imaging the white matter using diffusion tensor imaging Diffusion of water in the cerebral white matter Restricted diffusion Free diffusion 12
13 Imaging the white matter using diffusion tensor imaging Diffusion in cerebral WM is restricted by myelin sheaths, axonal membranes etc etc Thus, stronger diffusion along than perpendicular to the axons Along Perpendicular 13
14 Imaging the white matter using diffusion tensor imaging Non-hindered diffusion in all directions (x, y, z) yields an isotropic diffusion Vectors (x,y,z) define the direction of a straight line in 3D space ε 3 Λ 3 Z X ε 1 Λ 1 Corresponding eigenvalues define the length of the vectors Y ε 2 Λ 2 Isotropic: x = y = z (circle) Anisotropy: x y z (elipse) λ = eigenvalues, strenght ε = eigenvectors, direction 14
15 Imaging the white matter using diffusion tensor imaging The diffusion of water molecules in the brain is typically restricted (myelin, axonal membranes etc) in one or several directions, yielding an anisotropic distribution Anisotropic diffusion λ 1 > λ 2 > λ 3 Diffusion is stronger along ε1 than ε2 and ε3 Isotropic: x = y = z (circle) Anisotropy: x y z (elipse) λ = eigenvalues, strenght ε = eigenvectors, direction 15
16 Imaging the white matter using diffusion tensor imaging mm mm mm 1 mm 3 8 mm 3 27 mm 3 Pixel = picture element (2D) Voxel = volume element (3D) In DTI, eigenvectors (ε, directions) and eigenvalues (λ, the length of each vector) are calculated for each voxel 16
17 Imaging the white matter using diffusion tensor imaging In DTI, eigenvectors (ε, directions) and eigenvalues (λ, the length of each vector) are calculated for each voxel 17
18 We can only measure diffusion in one direction at a time
19 Imaging the white matter using diffusion tensor imaging Fractional anisotropy (FA) FA is an index of the directional coherence of the diffusion in each voxel (derived from the three eigenvalues) FA is an index between 0 (isotropic) and 1 (anisotropic) Based on Pierpaoli & Basser,
20 Imaging the white matter using diffusion tensor imaging Fractional anisotropy (FA) 20
21 Imaging the white matter using diffusion tensor imaging Fractional anisotropy (FA) Higher FA stronger directional coherence Brain areas with high FA = white Cingulate Corpus callosum 21
22 Visualizing DTI data Green = longitudinal association fibres (posterior-anterior) Red = interhemispheric fibres (corpus callosum etc) (left-right) Blue = corticospinal fibres (pyramidal motor tracts etc) (superior-inferior) 22
23 Visualizing DTI data (tractography) Cingulum bundle Tractus pyramidalis Corpus callosum Green = longitudinal association fibres (posterior-anterior) Red = interhemispheric fibres (corpus callosum etc) (left-right) Blue = corticospinal fibres (pyramidal motor tracts etc) (superior-inferior) 23
24 Visualizing DTI data (tractography)
25 Imaging the white matter using diffusion tensor imaging Other relevant parameters obtained from DTI: Mean diffusivity (MD) The average of the three eigenvalues ([L1+L2+L3]/3). Radial diffusivity (RD) The average of the second and third eigenvalue ([L2+L3]/2) Axial diffusivity (AD) The principal eigenvalue (L1) FA = 0.75 MD = 0.5 FA = 0.75 MD =
26 Imaging the white matter using diffusion tensor imaging What creates anisotropy in the brain? Along fibre orientation (λ 1, principal diff.): - microtubules - shred axons (i.e. in diffuse axonal injury (DAI)) - etc Perpendicular to axon orientation (λ 2 + λ 3 /2) radial diff): - axonal membranes - axonal caliber (diameter) - axonal density - myelin sheaths covering the axon (approx 20 % decrease in FA in non-myelinated axons, Beulieu, 2002, NMR) Along Perpendicular λ = eigenvalues, strength ε = eigenvectors, direction 26
27 Imaging the white matter using diffusion tensor imaging Everything that hinders diffusion along the axon reduced principal diffusion (λ 1 ) Everything that hinders diffusion perpendicular to the axon reduced radial diffusion (λ 2 + λ 3 ) /2) Changes in FA is can thus be caused by either reduced AD or elevated RD (or a combination). Along Perpendicular Question: Is FA always neurobiologically informative? 27
28 Imaging the white matter using diffusion tensor imaging mm mm mm 1 mm 3 8 mm 3 27 mm 3 An important limitation to all imaging methods is the voxel size 28
29 Imaging the white matter using diffusion tensor imaging The axon micrometer(10-6 m) millimeter (10-6 m) Each voxel may contain thousands of axons 8 mm 3 29
30 Fractional anisotropy (FA) the case of crossing fibre tracts Tract 1 Low FA High FA Tract 2 High FA Crossing fibres is a challenge in DTI research 30
31 Interim summary Diffusion is a naturally occuring phenomenon in the brain Diffusion weighted imaging is sensitive to diffusion properties in the brain (in vivo) Diffusion properties in the brain are neurobiologically informative, but interpretations should be made with caution (crossing fibres etc). 31
32 Outline Theory - Neurobiology of white matter (WM) - The basics of diffusion weighted imaging (DWI) - DTI derived indices of WM properties (fractional anisotropy etc) Methods - ROI based analyses - Tract Based Spatial Statistics (TBSS) 32
33 Processing and analyses of diffusion weighted images software tools used in our lab FSL (FMRIB Software library), Oxford University fmri DTI (Tract Based Spatial Statistics) Freesurfer (Massachusetts General Hospital, Harvard Medical School, Boston) Morphometry (cortical thickness, automatic cortical, subcortical parcellation and WM parcellation schemes) 33
34 Processing of diffusion weighted images in FSL 34
35 How do we compare different brains? 35
36 Manually drawn regions of interest (ROIs) Time consuming Operator dependent (subjective) Analysis confined to voxels within restricted and predefined regions 36
37 Neuroanatomical atlases may define ROIs Less time consuming Not operator dependent (objective) Atlas dependent (not always applicable to clinical samples, children) Analysis confined to voxels within restricted and predefined regions 37
38 Automatic white matter parcellation from FreeSurfer as ROIs Precise regional parcellation, but does not enable voxel based approaches 38
39 Tract-based spatial statistics (TBSS) is a tool for voxel based analyses of structurally complex and spatially variable fiber tracts 1. Alignment of all FA volumes into a standard space Subject 1 Subject 2 2. Compute mean FA of all subjects Subject 3 Subject 4 39
40 Westlye, Cand. psychol thesis, 2007 Tract-based spatial statistics (TBSS) is a tool for voxel based analyses of structurally complex and spatially variable fiber tracts 3. Generation of a skeleton representing the center of each WM tract 4. Mean skeleton thresholded and aligned to each subject s FA volume. Warping highest FA in the neighbourhood to skeleton. 40
41 Initially, no perfect alignment between skeleton and each subject s actual fibre tracts Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6 Individual FA data is projected to the skeleton Subject 7 Subject 8 Subject 9 Subject 10 Subject 11 Subject Smith et al., 2006, Neuroimage
42 Tract-based spatial statistics (TBSS) is a tool for voxel based analyses of structurally complex and spatially variable fiber tracts 42
43 5. The tract invariant skeleton for each subject is fed to voxel based statistical analysis 43
44 All methods have their strength and weaknesses 44
45 Outline Theory - Neurobiology of white matter (WM) - The basics of diffusion weighted imaging (DWI) - DTI derived indices of WM properties (fractional anisotropy etc) Methods - ROI based analyses - Tract Based Spatial Statistics (TBSS) Applications - Life span differences in DTI measures - Intraindividual differences in reaction time and DTI 45
46 Application 1 Context: Previous studies have found white matter volume increases into 5th and 6th decade, suggesting WM development well into adulthood Question: Does this pattern hold also for DTI measures of WM microstructure? Aim: Testing the hypothesis of protracted WM development by comparing DTI and WM volumetry in a large healthy life-span sample (n=430, 8-85 years of age) 46
47 White matter volume increases until the 50ths, then decreases. 47
48 FA increases until the late 20s and early 30s, then decreases 48
49 49
50 Regional variability? 50
51 Most voxels peak within 30 years of age 51
52 Estimating the peak in every voxel 52
53 What does this say about brain maturation and aging? Probably related to changes in degree of myelination, fibre architecture, axonal density etc 53
54 Conclusions 1. WM volume increases until the 50s and 60s 2. DTI measures indicate WM microstructural maturation until late 20s and early 30s Limitations? 1. Cross-sectional vs longitudinal design 2. The neurobiological interpretations of the DTI measures are not clear 3. What are the functional (cognitive) implications of the changes? 54
55 Are DTI indices of WM microstructure sensitive to individual differences trial-to-trial fluctuations in reaction time?
56 an inconsistent response is one of the most striking consequences of lesions to the cerebral cortex - Henry Head (1926, in MacDonald et al., 2006)
57 Variability comes in different forms Adaptive variability: Plasticity functional malleability to obtain large learning gains following task exposure Diversity exploratory behaviors and various strategies used for performing a complex task Adaptability ability to quickly recover peak functioning in the face of challenging task conditions Maladaptive variability : Fluctuation - subsequent to mastering a given level of functioning indicates a lack of processing robustness, defined by increased ebb and flow in processing and diminished stability in performance over brief intervals Li, Huxhold, & Schmiedek, 2004, Gerontology Li, Lindenberger, el al., 2004, Psychol Science MacDonald, Li & Bäckman, 2009, Psychol Aging
58 U-curved lifespan age-related differences in IIV on choice RT # trials = 32, n = 273, 6-81 yrs. Differences in mean performance level, practice effects etc controlled for Williams et al., 2005, Neuropsychology
59 IIV as a predictor of cognitive aging? Hypothesis: Increased IIV in RT at baseline is associated with accelerated cognitive deterioration with aging MacDonald, Li & Bäckman, 2009, Psychol Aging
60 IIV as a predictor of cognitive aging? Increased IIV in RT on a speeded perceptual task at baseline associated with steeper rate of cognitive decline in category fluency Lövden et al., 2007; in MacDonald, Li & Bäckman, 2009, Psychol Aging
61 Brain structural correlates of IIV white matter microstucture as measured by DTI U-curved DTI white matter microstructural life-span differences U-curved IIV life-span differences Williams et al., 2005, Neuropsychology Westlye et al. 2010, Cereb Cortex
62 Study 1: - N = 270 healthy subjects aged years (M: 48,6, SD: 16.9) - Median and SD RT calculated based on performance on a Flanker Task (Eriksen & Eriksen, 1974; Westlye et al., 2009) - Imaging performed on 1.5T Siemens Avanto scanner at OUS - Diffusion sampled along 30 directions, b=700, NEX=2 - DTI data analysed using TBSS (Smith et al., 2006) - Associations between IIV and WM microstructure were tested for FA, AD, RD, and MD
63 416 trials in two blocks 50 % probability of an incongruent trial Instruction: Respond as swift and accurate as possible Westlye et al., 2009, CerCor 63
64 64
65 Results 65
66 Increased IIV associated with FA, AD, RD and MD in widespread areas of the brain WM (covarying for age, gender and median RT) 66
67 Increased IIV associated with FA, AD, RD and MD in widespread areas of the brain WM (covarying for age, gender and median RT) 67
68 Age by sdrt interactions on DTI indices indicate stronger associations in the oldest part of the sample 68
69 Study 2: - N = 92 healthy subjects aged 8 19 years (M: 14.3, SD: 3.4) - Median and SD RT calculated based on performance on a Flanker Task (Eriksen & Eriksen, 1974; Westlye et al., 2009) - Imaging performed on 1.5T Siemens Avanto scanner at OUS - Diffusion sampled along 30 directions, b=700, NEX=2 - DTI data analysed using TBSS (Smith et al., 2006) - Associations between IIV and WM microstructure were tested for FA, AD, RD, and MD
70 Results 70
71 Behavioral age differences Williams et al., 2005, Neuropsychology Tamnes et al., 2012, J Neuroscience
72 Increased IIV associated with FA, AD, RD and MD in select regions (covarying for age, gender and median RT), including corticospinal pathways and corpus callosum 72
73 Increased IIV associated with FA, AD, RD and MD No associations between mrt and any of the DTI indices 73
74 Trial-by-trial variability in a speeded response task is associated with WM microstructural properties in children, adolescents and adults Effects are seen independent of median RT, and cannot be explained by a trivial correlation between motor execution or speed of processing and DTI IIV may provide a specific and sensitive cognitive phenotype in genetic association studies Future studies may explicitly model various theoretical/mathematical parameters in order to further disentangle the associations between response processes and structural imaging biomarkers, e.g. by using the Ratcliff diffusion etc 74
75 Thanks! 75
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