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

Why should I care about anatomy? Anatomy - behaviour Verbal Learning Nieman et al, 2007 Dickerson et al, 2008

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 correction Sled, Zijdenbos, Evans: IEEE-TMI Feb 1998

Voxel Classification T2 T1 PD

MS Lesion Classification

Positional Differences Brain 2 Brain 1

Overall Size Differences

Spatial Normalization Before Registration After Registration

Voxel Based Morphometry The goal: localize changes in tissue concentration.

Proportion of neighbourhood occupied by tissue class 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.

Example 111 healthy children Aged 4-18 Paus 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

Deformations

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

Childhoo d 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

Backpropagation

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

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.

automatic segmentation Methods Summary Method Computation Comparisons Localization Coverage manual segmentation Manual one-few depends ROI VBM Easy millions poor cerebrum TBM Moderate OK brain optimized VBM automatic segmentation few large structures cortical thickness Hard 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. Group comparison, cortical hypothesis? cortical thickness: FreeSurfer, MINC tools. sulcal morphology/shape: BrainVisa/anatomist. Lesion/stroke? classification: MINC tools. Longitudinal data? deformations: SPM (Dartel), ANTS, FSL (SIENA), MINC tools.

Acknowledgements Judith Rapoport Jay Giedd Dede Greenstein Rhoshel Lenroot Philip Shaw Jeffrey Carroll Michael Hayden Harald Hampel Stefan Teipel Alan Evans Alex Zijdenbos Krista Hyde Claude Lepage Yasser Ad-Dab’bagh Tomas Paus Jens Pruessner Veronique Bohbot John Sled Mark Henkelman Matthijs van Eede Jurgen Germann