Techniques for the analysis of GM structure: VBM, DBM, cortical thickness Jason Lerch.

Techniques for the analysis of GM

structure: VBM, DBM, cortical

thickness

Jason Lerch

Why should I care about anatomy?

Nieman et al, 2007 Dickerson et al, 2008

Verbal Learning

Anatomy - behaviour

The methods.

•Manual segmentation/volumetry.

•Voxel Based Morphometry (VBM).

•Deformation/Tensor Based Morphometry (DBM).

•optimized VBM.

•automated volumetry.

•cortical thickness.

Processing Flow

Manual Segmentation

•Identify one or more regions of interest.

•Carefully segment these regions for all subjects.

•Statistics on volumes.

Segmentation example

And it was good.

•Cons:

•Labour intensive and time consuming.

•Need to compute inter and intra rater reliability measures.

•Pros:

•Can be highly accurate.

•Can discern boundaries still invisible to machine vision.

Preprocessing

Non-uniformity Non-uniformity correctioncorrectionSled, Zijdenbos, Evans: IEEE-TMI Feb 1998

Voxel ClassificationVoxel ClassificationT2

PD

T1

MS Lesion MS Lesion ClassificationClassification

Positional Differences

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Brain 1

Brain 2

Overall Size Differences

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Spatial NormalizationSpatial Normalization

Before Registration

After Registration

Voxel Based Morphometry

•The goal: localize changes in tissue concentration.

Tissue Density

Proportion of neighbourhood occupied by tissue class

Real world example

VBM statistics

•Tissue density modelled by predictor(s).

•I.e.: at every voxel of the brain is there a difference in tissue density between groups (or correlation with age, etc.)?

•Millions of voxels tested, multiple comparisons have to be controlled.

ExampleExamplePaus et al., Science 283:1908-1911, 1999

111 healthy children

Aged 4-18

And it was good.•Pros:

•Extremely simple and quick.

•Can look at whole brain and different tissue compartments.

•By far most common automated technique - easy comparison to other studies.

•Cons

•Hard to explain change (WM? GM?).

•Hard to precisely localize differences.

•Hard time dealing with different size brains.

Tensor Based Morphometry

•The goal: localize differences in brain shape.

Non-linear deformation

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Deformations

Jacobians

Chung et al. A unified statistical approach to deformation-based morphometry. Neuroimage (2001) vol. 14 (3) pp. 595-606

Childhood

Music

Hyde et al., 2008

And it was good.

•Pros:

•Excellent for simple topology (animal studies).

•Excellent for longitudinal data.

•Does not need tissue classification.

•Cons:

•hard matching human cortex from different subjects.

•Can be quite algorithm dependent.

Optimized VBM

•The goal: combine the best of VBM and TBM

Modulation

x

And it was good.

•Pros:

•More accurate localization than plain VBM.

•Cons:

•Dependent on non-linear registration algorithm.

•Is it really better than either VBM or TBM alone?

Automatic segmentation

•The goal: structure volumes without manual work.

Segmentation

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Backpropagation

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And it was good.

•Pros:

•A lot less work than manual segmentation.

•Excellent if image intensities can be used.

•Excellent if non-linear registration is accurate.

•Cons:

•Not always accurate for small structures.

•Hard time dealing with complex cortical topology.

Cortical Thickness

•The goal: measure the thickness of the cortex.

Processing Steps in Pictures

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Processing Continued

4.5mm

1.0mm

Surface-based Blurring

And it was good.

•Pros:

•Extremely accurate localization of cortical change.

•Sensible anatomical measure.

•Sensible blurring.

•Cons:

•Only covers one dimension of one part of the brain.

•Computationally very expensive and difficult.

Methods Summary

MethodComputatio

nComparison

sLocalizatio

nCoverag

emanual

segmentation

Manual one-few depends ROI

VBM Easy millions poor cerebrumTBM Moderate millions OK brain

optimized VBM

Moderate millions OK cerebrum

automatic segmentati

onModerate few poor

large structure

scortical

thicknessHard thousands excellent cortex

Advice, part 1

•MRI anatomy studies need more subjects than fMRI

•aim for at least 20 per group.

•Acquire controls on same hardware.

•Isotropic sequences are your friend.

•T1 is enough unless you’re looking for lesions.

Advice, part 2• Group comparison, strong hypothesis?

• manual segmentation.

• automatic segmentation: FreeSurfer.

• Group comparison, few hypotheses?

• VBM: SPM, FSL, MINC tools.

• automatic segmentation: FreeSurfer.

• Group comparison, cortical hypothesis?

• cortical thickness: FreeSurfer, MINC tools.

• sulcal morphology/shape: BrainVisa/anatomist.

• Lesion/stroke?

• manual segmentation.

• classification: MINC tools.

• Longitudinal data?

• deformations: SPM (Dartel), ANTS, FSL (SIENA), MINC tools.

Acknowledgements

Alan EvansAlex Zijdenbos

Krista HydeClaude Lepage

Yasser Ad-Dab’baghTomas Paus

Jens PruessnerVeronique Bohbot

John SledMark HenkelmanMatthijs van EedeJurgen Germann

Judith RapoportJay Giedd

Dede GreensteinRhoshel Lenroot

Philip ShawJeffrey Carroll

Michael HaydenHarald HampelStefan Teipel