techniques for the analysis of gm structure: vbm, dbm, cortical thickness jason lerch
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Techniques for the analysis of GM
structure: VBM, DBM, cortical
thickness
Jason Lerch
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Why should I care about anatomy?
Nieman et al, 2007 Dickerson et al, 2008
Verbal Learning
Anatomy - behaviour
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The methods.
•Manual segmentation/volumetry.
•Voxel Based Morphometry (VBM).
•Deformation/Tensor Based Morphometry (DBM).
•optimized VBM.
•automated volumetry.
•cortical thickness.
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Processing Flow
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Manual Segmentation
•Identify one or more regions of interest.
•Carefully segment these regions for all subjects.
•Statistics on volumes.
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Segmentation example
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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.
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Preprocessing
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Non-uniformity Non-uniformity correctioncorrectionSled, Zijdenbos, Evans: IEEE-TMI Feb 1998
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Voxel ClassificationVoxel ClassificationT2
PD
T1
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MS Lesion MS Lesion ClassificationClassification
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Positional Differences
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Brain 1
Brain 2
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Overall Size Differences
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Spatial NormalizationSpatial Normalization
Before Registration
After Registration
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Voxel Based Morphometry
•The goal: localize changes in tissue concentration.
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Tissue Density
Proportion of neighbourhood occupied by tissue class
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Real world example
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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.
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ExampleExamplePaus et al., Science 283:1908-1911, 1999
111 healthy children
Aged 4-18
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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.
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Tensor Based Morphometry
•The goal: localize differences in brain shape.
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Non-linear deformation
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Deformations
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Jacobians
Chung et al. A unified statistical approach to deformation-based morphometry. Neuroimage (2001) vol. 14 (3) pp. 595-606
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Childhood
Music
Hyde et al., 2008
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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.
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Optimized VBM
•The goal: combine the best of VBM and TBM
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Modulation
x
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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?
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Automatic segmentation
•The goal: structure volumes without manual work.
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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.
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Cortical Thickness
•The goal: measure the thickness of the cortex.
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Processing Steps in Pictures
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Processing Continued
4.5mm
1.0mm
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Surface-based Blurring
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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.
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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
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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.
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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.
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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