Voxel-based Morphometric Analysis Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States

Outline Voxel-based Morphometry Surface-based Analysis Comparisons 2

Voxel-based Morphometry

Anatomical Changes GM Subject 1 88 yo Subject 2 19 yo

Voxel-based Morphometry (VBM) How do the sizes of gray/white matter and CSF structures change between subjects/populations? GM WM CSF Subject 1 88 yo Subject 2 19 yo

Voxel-based Morphometry (VBM) How to define a volume without defining a boundary? How to compare regions without defining a region? WM CSF

Non-linear Spatial Normalization Subject 2 (Target) Subject 1 Subject 1  Subject 2 CSF Eyes move closer together Lips curl and get wider

Non-linear Spatial Normalization Subject 2 (Target) Subject 1 Subject 1  Subject 2 CSF Eyes move closer together Lips curl and get wider

Keep Track of Changes in Size CSF Voxel on the left side of mouth does not change size Voxel on the right gets much larger Eyes do not change size Quantification: Gray Matter “Density”

Jacobian Map of change in volume at each voxel in target space Subject 2 (Target) Subject 2 (Target) Subject 1 CSF Map of change in volume at each voxel in target space Eyes are 0 Left side of lips are 0 (black) Right side of lips are yellow (expansion)

Normalization and Segmentation Individual T1 (Template Space) Jacobian Individual T1 Spatial Normalization Expansion Compression Group Template (Target) Segmentations CSF WM GM Values between 0 and 1 “Density”, Partial Volume Note: in FSL, Segmentations computed in native space “Unified Segmentation”, Ashburner and Friston, NI, 2005 “Optimized VBM” Good, et al, NI, 2001. Douaud, et al, Brain. 2007.

Modulation and Smoothing (Template Space) GM Segmentation (Concentration) Multiply 3D Smooth GM Density Jacobian

Aging Gray Matter Volume Study Statistical Maps (SPM8/VBM8) p<.01 Positive Age Correlation Negative Age Correlation GM Density

Surface-based Analysis

Surface-based Analysis: Cortex Outer layer of gray matter White/Gray Surface Pial Surface

Cortical Thickness pial surface Distance between white and pial surfaces along normal vector. 1-5mm

IndividualThickness Maps 46 yo 88 yo 18 yo Male Female Salat, et al, 2004, Cerebral Cortex

A Surface-Based Coordinate System Common space for group analysis (like Talairach). Fischl, et al, 1998, NI.

Surface Spatial Smoothing 5 mm apart in 3D 25 mm apart on surface! Kernel much larger Averaging with other tissue types (WM, CSF) Averaging with other functional areas

Aging Thickness Study N=40 p<.01 Positive Age Correlation Negative Age Correlation p<.01

VBM and Thickness vs Age VBM (SPM8/VBM8) Thickness (FreeSurfer 5.0) p<.01

Comparisons Thickness does not require modulation False positive rates are much higher in VBM because of Jacobian modulation (same for surface-based when area or volume are used; Greve and Fischl, 2017) Thickness is independent of registration. VBM – harder to interpret because “density” is a mixture of thickness, surface area, gyrification, registration, volume-based smoothing, and intensity. VBM allows subcortical analysis Young (20) Old (80) 22

Which is better? Still an open question Voets, et al, 2008, NI – mixed results Hutton, et al, 2009, NI – voxel-based cortical thickness (VBCT) was more sensitive to aging than VBM False Positive Rate considerations 23

VBM Software Statistical Parametric Mapping (SPM) www.fil.ion.ucl.ac.uk/spm uses the VBM toolbox dbm.neuro.uni-jena.de/vbm FMRIB Software Library (FSL) www.fmrib.ox.ac.uk/fsl 24

Thanks! 25

VBM Summary Spatial Normalization Volume = Jacobian X Segmentation Strengths Cortical and subcortical Gray Matter, White Matter, CSF Easy to use Weaknesses Volume metric derived from normalization Sensitive to registration and segmentation errors Segmentation is atlas-dependent 27

Surface-based Summary Thickness (can also use area and volume) Strengths Surface-based Normalization Surface-based Smoothing Thickness independent of normalization Surface extraction atlas-independent Weaknesses No subcortical, White Matter, or CSF More complicated to analyze 28