1. Introduction to FreeSurfer surfer.nmr.mgh.harvard.edu

2. Why FreeSurfer?
Anatomical analysis is not like functional analysis – it is completely stereotyped.
Registration to a template (e.g. MNI/Talairach) doesn’t account for individual anatomy.
Even if you don’t care about the anatomy, anatomical models allow functional analysis not otherwise possible. 2

3. Why not just register to an ROI Atlas?
12 DOF
(Affine)
ICBM Atlas

4. Problems with Affine (12 DOF) Registration
Subject 2 aligned with Subject 1
(Subject 1’s Surface)
Subject 1

5. Surface and Volume Analysis
Cortical Reconstruction
and Automatic Labeling
Inflation and Functional
Mapping
Automatic Subcortical
Gray Matter Labeling
Surface-based Intersubject
Alignment and Statistics
Automatic Gyral White
Matter Labeling
Surface Flattening

6. Cortical (surface-based) Analysis. Volume Analysis.
Talk Outline
Cortical (surface-based) Analysis.
Volume Analysis.

7. Surface Reconstruction Overview
Input: T1-weighted (MPRAGE,SPGR)
Find white/gray surface
Find pial surface
“Find” = create mesh
Vertices, neighbors, triangles, coordinates
Accurately follows boundaries between tissue types
“Topologically Correct”
closed surface, no donut holes
no self-intersections
Generate surface-based cross-subject registration
Label cortical folding patterns
Subcortical Segmentation along the way

8. Surface Model Mesh (“Finite Element”) Vertex = point of triangles
Neighborhood
XYZ at each vertex
Triangles/Faces ~ 150,000
Area, Distance
Curvature, Thickness
Moveable
The cortical surface is represented by a finite element model using triangles to cover the cortical surface. The reconstruction is the (mostly automated) process of assigning an xyz to each corner of each triangle. Once the xyz of each corner of each triangle is known, then it is possible to characterize the entire cortical surface in terms of area, distance, curvature, and thickness.

9. Surfaces: White and Pial

10. Cortical Thickness pial surface
Distance between white and pial surfaces
One value per vertex
white/gray surface
lh.thickness, rh.thickness

11. A Surface-Based Coordinate System

12. Comparing Coordinate Systems and Brodmann Areas
Can use the ex vivo Brodmann delineations to assess the accuracy of a coordinate system. Of course being better than Talairach isn’t the strongest statement in the world….
Cumulative histogram (red=surface, blue=nonlinear Talairach)
Ratio of surface accuracy to volume accuracy

13. Automatic Surface Segmentation
Precentral Gyrus
Postcentral Gyrus
Superior Temporal Gyrus
Based on individual’s folding pattern

14. Inter-Subject Averaging
Spherical
Spherical
Native
GLM
Subject 1
Surface-to-
Surface
Demographics
Subject 2
Surface-to-
Surface
mri_glmfit
cf. Talairach

15. Visualization
Borrowed from (Halgren et al., 1999)

16. Automatic Cortical Parcellation
Spherical Atlas based on Manual Parcellations (40 of them)
Map to Individual
Thru Spherical Reg
Fine-tune based on
individual anatomy
Note: Similar methodology to volume labeling
More in the Anatomical ROI talk

17. Cortical (surface-based) Analysis. Volume Analysis.
Talk Outline
Cortical (surface-based) Analysis.
Volume Analysis.

18. Volume Analysis: Automatic Individualized Segmentation
Surface-based coordinate system/registration appropriate for cortex but not for thalamus, ventricular system, basal ganglia, etc…
Anatomy is extremely variable – measuring the variance and accounting for it is critical (more in the individual subject talk)!

19. Volumetric Segmentation (aseg)
Caudate
Pallidum
Putamen
Amygdala
Hippocampus
Lateral Ventricle
Thalamus
White Matter
Cortex
Not Shown:
Nucleus Accumbens
Cerebellum

20. Summary Why Surface-based Analysis?
Function has surface-based organization
Visualization: Inflation/Flattening
Cortical Morphometric Measures
Inter-subject registration
Automatically generated ROI tuned to each subject individually
Use FreeSurfer
Be Happy

21. FreeSurfer Directory Tree
Each data set has its own unique SubjectId (eg, bert)
Subject
Subject Name
bert
scripts surf label mri stats
orig.mgz T1.mgz brain.mgz wm.mgz aseg.mgz
Set SUBJECTS_DIR, issue a command (mksubjdirs) to create directory structure. Convert (using mri_convert) data to COR format and store in mri/RRR, run recon-all. Typically, we acquire 3 high-quality T1 volumes to use as input to reconstruction (128-slice sagital, 1 mm in-plane resolution).
recon-all –i file.dcm –subject bert –all

22. Other File Formats Surface: Vertices, XYZ, neighbors (lh.white)
Curv: lh.curv, lh.sulc, lh.thickness
Annotation: lh.aparc.annot
Label: lh.pericalcarine.label
Unique to FreeSurfer
FreeSurfer can read/write:
NIFTI, Analyze, MINC
FreeSurfer can read:
DICOM, Siemens IMA, AFNI