Structural connectivity of the brain measured by diffusion tensor imaging [DTI]

Lars T. Westlye CSHC / Centre for Advanced Study

PSY4320 06.05.2012 l.t.westlye@psykologi.uio.no

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~ 40 % of the brain is white matter (WM)

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Cortex (GM)

WM

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Cortex (GM)

Cortex: cell bodies WM: myelinated axons

WM

At the age of 20, the brain has a total myelinated fibre length of about 160 000 km By the age of 80 this has been reduced by almost 50 %

5 Sherman & Brophy, 2007 Nat Rev Neuroscience

Myelin is produced by glial cells - CNS: Oligodendrocytes - PNS: Schwann cells

Myelin is an electrically insulating phospholipid layer surrounding the axon

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Neurobiology of white matter

Electrochemical signaling

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Neurobiology of white matter

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Neurobiology of white matter

Peters 2009, Frontiers in neuroanatomy Cross-section of myelinated axons (Rhesus monkey)

1 µm

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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

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What is diffusion?

Diffusion is the random motion of a given entitiy causing the distribution of the entity to spread in space.

Imaging the white matter using diffusion tensor imaging

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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 TX is visualized as a circle with r = x

Imaging the white matter using diffusion tensor imaging

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Imaging the white matter using diffusion tensor imaging

Free diffusion

Restricted diffusion

Diffusion of water in the cerebral white

matter

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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

Perp

endi

cula

r

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Non-hindered diffusion in all directions (x, y, z) yields an isotropic diffusion

Isotropic: x = y = z (circle) Anisotropy: x ≠ y ≠ z (elipse)

λ = eigenvalues, strenght ε = eigenvectors, direction

Imaging the white matter using diffusion tensor imaging

X

Y

Z ε1 Λ1

ε2 Λ2

ε3 Λ3

Vectors (x,y,z) define the direction of a straight line in 3D space

Corresponding eigenvalues define the length of the vectors

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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

Imaging the white matter using diffusion tensor imaging

Isotropic: x = y = z (circle) Anisotropy: x ≠ y ≠ z (elipse)

λ = eigenvalues, strenght ε = eigenvectors, direction

”Diffusion is stronger along ε1 than ε2 and ε3”

λ 1 > λ 2 > λ 3

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In DTI, eigenvectors (ε, directions) and eigenvalues (λ, the length of each vector) are calculated for each voxel

1 × 1 × 1 mm 2 × 2 × 2 mm 3 × 3 × 3 mm

Pixel = picture element (2D) Voxel = volume element (3D)

1 mm3 8 mm3 27 mm3

Imaging the white matter using diffusion tensor imaging

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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

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We can only measure diffusion in one direction at a time

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Fractional anisotropy (FA)

FA is an index of the directional coherence of the diffusion in each voxel (derived from the three eigenvalues)

Based on Pierpaoli & Basser, 1996 FA is an index between 0 (isotropic) and 1 (anisotropic)

Imaging the white matter using diffusion tensor imaging

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Fractional anisotropy (FA)

Imaging the white matter using diffusion tensor imaging

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Higher FA → stronger directional coherence

Brain areas with high FA = white

Corpus callosum

Cingulate

Fractional anisotropy (FA)

Imaging the white matter using diffusion tensor imaging

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Green = longitudinal association fibres (posterior-anterior) Red = interhemispheric fibres (corpus callosum etc) (left-right) Blue = corticospinal fibres (pyramidal motor tracts etc) (superior-inferior)

Visualizing DTI data

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Cingulum bundle

Corpus callosum Tractus pyramidalis

Green = longitudinal association fibres (posterior-anterior) Red = interhemispheric fibres (corpus callosum etc) (left-right) Blue = corticospinal fibres (pyramidal motor tracts etc) (superior-inferior)

Visualizing DTI data (tractography)

Visualizing DTI data (tractography)

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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 = 0.1

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λ = eigenvalues, strength ε = eigenvectors, direction

What creates anisotropy in the brain?

Along fibre orientation (λ1,principal diff.): - microtubules - shred axons (i.e. in diffuse axonal injury (DAI)) - etc…

Perpendicular to axon orientation (λ2+ λ3/2) 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)

Imaging the white matter using diffusion tensor imaging

Along

Perp

endi

cula

r

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Question: Is FA always neurobiologically informative?

Everything that hinders diffusion along the axon → reduced principal diffusion (λ1) Everything that hinders diffusion perpendicular to the axon → reduced radial diffusion (λ2 + λ3) /2)

Changes in FA is can thus be caused by either reduced AD or elevated RD (or a combination).

Imaging the white matter using diffusion tensor imaging

Along

Perp

endi

cula

r

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Imaging the white matter using diffusion tensor imaging

1 × 1 × 1 mm 2 × 2 × 2 mm 3 × 3 × 3 mm

1 mm3 8 mm3 27 mm3

An important limitation to all imaging methods is the voxel size

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micrometer(10-6 m)

millimeter (10-6 m)

8 mm3

Imaging the white matter using diffusion tensor imaging

The axon

Each voxel may contain thousands of axons

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Fractional anisotropy (FA) – the case of crossing fibre tracts

Low FA

High FA

High FA

Tract 1

Tract 2

Crossing fibres is a challenge in DTI research

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Interim summary

Diffusion properties in the brain are neurobiologically informative, but

interpretations should be made with caution (crossing fibres etc).

Diffusion is a naturally occuring phenomenon in

the brain

Diffusion weighted imaging is sensitive to diffusion

properties in the brain (in vivo)

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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)

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Processing and analyses of diffusion weighted images – software tools used in our lab

FSL (FMRIB Software library), Oxford University http://www.fmrib.ox.ac.uk/fsl/ fMRI DTI (Tract Based Spatial Statistics)

Freesurfer (Massachusetts General Hospital, Harvard Medical School, Boston) http://surfer.nmr.mgh.harvard.edu/ Morphometry (cortical thickness, automatic cortical, subcortical parcellation and WM parcellation schemes)

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Processing of diffusion weighted images in FSL

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How do we compare different brains?

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Manually drawn regions of interest (ROIs)

Time consuming Operator dependent (subjective) Analysis confined to voxels within restricted and predefined regions

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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

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Automatic white matter parcellation from FreeSurfer as ROIs

Precise regional parcellation, but does not enable voxel based approaches

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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

2. Compute mean FA of all subjects

Subject 1 Subject 2

Subject 3 Subject 4

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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.

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

41 Smith et al., 2006, Neuroimage

Initially, no perfect alignment between skeleton and each

subject’s actual fibre tracts

Individual FA data is projected to the

skeleton

Subject 1 Subject 2 Subject 3

Subject 4 Subject 5 Subject 6

Subject 7 Subject 8 Subject 9

Subject 10 Subject 11 Subject 12

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Tract-based spatial statistics (TBSS) is a tool for voxel based analyses of structurally complex and spatially variable fiber tracts

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5. The tract invariant skeleton for each subject is fed to voxel based statistical analysis

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All methods have their strength and weaknesses

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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

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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)

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White matter volume increases until the 50ths, then decreases.

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FA increases until the late 20s and early 30s, then decreases

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Regional variability?

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Most voxels peak within 30 years of age

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What does this say about brain maturation and aging?

Probably related to changes in degree of myelination, fibre architecture, axonal density etc

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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?

Are DTI indices of WM microstructure sensitive to individual differences trial-to-trial fluctuations in

reaction time?

“… an inconsistent response is one of the most striking consequences of lesions to the cerebral cortex”

- Henry Head (1926, in MacDonald et al., 2006)

Variability comes in different forms

MacDonald, Li & Bäckman, 2009, Psychol Aging

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

U-curved lifespan age-related differences in IIV on choice RT

Williams et al., 2005, Neuropsychology

# trials = 32, n = 273, 6-81 yrs. Differences in mean performance level, practice effects etc controlled for

IIV as a predictor of cognitive aging?

MacDonald, Li & Bäckman, 2009, Psychol Aging

Hypothesis: Increased IIV in RT at baseline is associated with accelerated cognitive deterioration with aging

Lövden et al., 2007; in MacDonald, Li & Bäckman, 2009, Psychol Aging

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

Westlye et al. 2010, Cereb Cortex

U-curved IIV life-span differences

U-curved DTI white matter microstructural life-span differences

Brain structural correlates of IIV – white matter microstucture as measured by DTI

Williams et al., 2005, Neuropsychology

Study 1: - N = 270 healthy subjects aged 20–83 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

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416 trials in two blocks 50 % probability of an incongruent trial Instruction: ”Respond as swift and accurate as possible”

Westlye et al., 2009, CerCor

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Results

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Increased IIV associated with FA, AD, RD and MD in widespread areas of the brain WM (covarying for age, gender and median RT)

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Increased IIV associated with FA, AD, RD and MD in widespread areas of the brain WM (covarying for age, gender and median RT)

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Age by sdRT interactions on DTI indices indicate stronger associations in the oldest part of the sample

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

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Results

Behavioral age differences

Williams et al., 2005, Neuropsychology Tamnes et al., 2012, J Neuroscience

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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

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Increased IIV associated with FA, AD, RD and MD No associations between mRT and any of the DTI indices

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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

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Thanks!