1. Introduction to FreeSurfer
Presented by Sarah Whittle & Dominic Dwyer 19th March 2009

2. Talk Outline Introduction
Individual Cortical (surface-based) and Volumetric Analysis
Group Analyses (brief)
Structure-Function Integration (brief)
Working with Freesurfer

3. Intro: What can we do with Freesurfer?
Surface inflation and manipulation: Visualize structural and functional data; reveal data in sulcal depths
Intersubject registration: Alternate spatial normalization
Morphometric analysis: Cortical thickness; analysis of folding patterns
Cortical Parcellation: Analysis of cortical subregions; fMRI ROI analysis
Subcortical segmentation: Volumetric analysis; fMRI ROI analysis
White matter parcellation: Volumetric analysis; DTI region of interest analysis
Integrate with FSL tools: Spatial normalization of fMRI data; ROI analyses

4. Intro: FreeSurfer Resources
FreeSurfer Wiki can be VERY useful:
“can”: too much info & not 100% logically organised!
2008 Brisbane FSL/FreeSurfer workshop booklet available to loan from MNC lab

5. Cortical (surface-based) Analysis Surface Reconstruction Theory
Input: T1-weighted (MPRAGE,SPGR)
Segment white matter from rest of brain.
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
Subcortical Segmentation along the way

6. Surface Model Mesh (“Finite Element”) Vertex = point of 6 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.

7. White Matter Surface Nudge orig surface Follow T1 intensity gradients
Smoothness constraint
Vertex Identity Stays

8. Pial Surface Nudge white surface Follow T1 intensity gradients
Vertex Identity Stays

9. Cortical Thickness pial surface
Distance between white and pial surfaces
One value per vertex
Surface-based more accurate than volume-based
white/gray surface
lh.thickness, rh.thickness

10. Curvature (Radial) Circle tangent to surface at each vertex
Curvature measure is 1/radius of circle
One value per vertex
Signed (sulcus/gyrus)
Actually use gaussian curvature
lh.curv, rh.curv

11. Rosas et al., 2002
Sailer et al., 2003
Kuperberg et al., 2003
Fischl et al., 2000
Gold et al., 2005
Applications of thickness measurement
Salat et al., 2004
Rauch et al., 2004

12. Surface “Inflation” Inflated Sphere Vertex Identity (index) Preserved
All surfaces have the same number of vertices. Different surfaces are made by moving the triangles around to achieve some goal. There is a one-to-one correspondence between surfaces which allows the user to cross-reference vertices from one surface to another (even across subjects) and to the volume. This feature is critical to get properly oriented. In the flat surface, there’s still a one-to-one correspondence, but not all vertices in the orig surface may be represented.
Vertex Identity (index) Preserved
White
Pial

13. Non-Cortical Areas of Surface
Amygdala
Amygdala, Putamen, Hippocampus, Caudate, Ventricles, CC

14. “Spherical” Registration
Sulcal Map
Spherical Inflation
High-Dimensional
Registration to
Spherical Template

15. Inter-Subject Registration of Cortical Folding Patterns
More in group analysis section

16. Volume Analysis: Automatic Individualized Segmentation

17. ROI Atlas Creation Hand label N data sets
Volumetric (subcortical): CMA
Surface Based:
Desikan/Killiany
Destrieux
Map labels to common coordinate system (using spherical registration).
Probabilistic Atlas
Probability of a label at a vertex/voxel (global spatial info)
Sulcal & gyral geometry
Neighborhood relationships
You can create your own atlases
Incorporates the probable location of a ROI + potential inter-subject variance of the location of the region, derived from the training set employed.
Curvature info used to

18. Automatic Labeling Transform ML labels to individual subject*
Adjust boundaries based on
Curvature/Intensity statistics
Neighborhood relationships
Result: labels are customized to each individual.
* Formally, we compute maximum a posteriori estimate of the labels given the input data

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

20. Subject 2 aligned with Subject 1
Problems with Affine (12 DOF) Registration
ROIs need to be individualized.
Subject 2 aligned with Subject 1
(Subject 1’s Surface)
Subject 1

21. Can’t segment on intensity alone
Fischl – 2002 paper in Neurotechnique

22. Volumetric Segmentation (aseg)
Caudate
Pallidum
Putamen
Amygdala
Hippocampus
Lateral Ventricle
Thalamus
White Matter
Cortex
Not Shown:
Nucleus Accumbens
Cerebellum
Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain, Fischl, B., D.H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A.M. Dale, (2002). Neuron, 33:

23. Volumetric Segmentation Atlas Description
39 Subjects
14 Male, 39 Female
Ages 18-87
Young (1-22): 10
Mid (40-60): 10
Old Healthy (69+): 8
Old Alzheimer's (68+): 11
Siemens 1.5T Vision (Wash U)
Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain, Fischl, B., D.H. Salat, E. Busa, M. Albert, M. Dieterich, C. Haselgrove, A. van der Kouwe, R. Killiany, D. Kennedy, S. Klaveness, A. Montillo, N. Makris, B. Rosen, and A.M. Dale, (2002). Neuron, 33:

24. Automatic Surface Parcellation: Desikan/Killiany Atlas
Precentral Gyrus
Postcentral Gyrus
Superior Temporal Gyrus
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, Desikan, R.S., F. Segonne, B. Fischl, B.T. Quinn, B.C. Dickerson, D. Blacker, R.L. Buckner, A.M. Dale, R.P. Maguire, B.T. Hyman, M.S. Albert, and R.J. Killiany, (2006). NeuroImage 31(3):

25. Desikan/Killiany Atlas
40 Subjects
14 Male, 26 Female
Ages 18-87
34 cortical regions
Siemens 1.5T Vision (Wash U)
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, Desikan, R.S., F. Segonne, B. Fischl, B.T. Quinn, B.C. Dickerson, D. Blacker, R.L. Buckner, A.M. Dale, R.P. Maguire, B.T. Hyman, M.S. Albert, and R.J. Killiany, (2006). NeuroImage 31(3):

26. Automatic Surface Parcellation: Destrieux Atlas
Automatically Parcellating the Human Cerebral Cortex, Fischl, B., A. van der Kouwe, C. Destrieux, E. Halgren, F. Segonne, D. Salat, E. Busa, L. Seidman, J. Goldstein, D. Kennedy, V. Caviness, N. Makris, B. Rosen, and A.M. Dale, (2004). Cerebral Cortex, 14:11-22.

27. Automatic Surface Parcellation: Destrieux Atlas
58 Parcellation Units
12 Subjects
Automatically Parcellating the Human Cerebral Cortex, Fischl, B., A. van der Kouwe, C. Destrieux, E. Halgren, F. Segonne, D. Salat, E. Busa, L. Seidman, J. Goldstein, D. Kennedy, V. Caviness, N. Makris, B. Rosen, and A.M. Dale, (2004). Cerebral Cortex, 14:11-22.

28. Gyral White Matter Segmentation+ +
aparc+aseg
wmparc
Nearest Cortical Label
to point in White Matter

29. Surface-based group analysis
Processing Stages
Specify Subjects and Surface measures
Assemble Data (mris_preproc):
Resample into Common Space (fsaverage)
Smooth
Concatenate into one file
Model and Contrasts (GLM)
Fit Model (Estimate) (mri_glmfit)
Correct for multiple comparisons
Visualize (tksurfer)

30. Surface-based Measures
Morphometric (eg, thickness)
Functional
PET
MEG/EEG
Diffusion (?) sampled just under the surface

31. Surface-based Group Analysis in FreeSurfer
GLM
Command-line based
Create a FreeSurfer Group Descriptor File (FSGD)
FreeSurfer creates design matrix
You still have to specify contrasts
Fit model (mri_glmfit)
QDEC (GUI)
Limited to 2 discrete variables, 2 levels max
Limited to 2 continuous variables

32. Visualisation with tksurfer
Saturation:
-log10(p),
Eg, 5=.00001
Threshold:
-log10(p),
Eg, 2=.01
False Discovery
Rate, Eg, .01
View->Configure->Overlay
File->LoadOverlay

33. Function-Structure Integration in FreeSurfer Why Is a Model of the Cortical Surface Useful?
Local functional organization of cortex is largely 2-dimensional! Eg, functional mapping of primary visual areas:
Analysis of fMRI data using cortical surface models offers at
least three advantages over more conventional 3-dimensional
analysis methods. First, cortical surface models allow for better
visualization of activations, providing a more global view than
single slices and a better view of the spatial extent of activation foci
and their locations relative to each other and to sulcal/gyral landmarks (Dale and Sereno, 1993). Second, statistical methods for
the analysis of single subject data can benefit from the exclusion of
non-gray matter signals, and smoothing signals along the cortical
surface, rather than in 3D results in superior resolution and
sensitivity (Kiebel et al., 2000; Andrade et al., 2001; Formisano et
al., 2004). Finally, group analysis with cortical surface models
employs inter-subject alignment based on the patterns of sulci and
gyri, as opposed to Talairach registration, which often ignores
sulcal/gyral landmarks and tends to blur activity across neighboring
banks of a sulcus (Fischl et al., 1999a,b).
The highly folded nature of the cortical surface also
makes it difficult to view functional activity in a
meaningful way. The typical means of visualization of
this type of data is the projection of functional activation
onto a set of orthogonal slices. This procedure is
problematic as regions of activity which are close
together in the volume may be relatively far apart in
terms of the distance measured along the cortical
surface. In addition, the naturally two-dimensional
organization of cortical maps is largely obscured by the
imposition of an external coordinate system in the form
of orthogonal slices. These problems have led an increasing
number of studies to make use of surface-based
techniques for visualization.
Yellow = mirror image representation of visual field (eg, V1)
Blue = non-mirror image (eg, V2)
Convenient way to draw borders between areas because adjoining areas often have the opposite visual field sign.
Responses to phase-encoded retinal stimulation were recorded with fMRI – analysed with visual field sign method to distinguish mirror- from non-mirror image representations. Allowed accurate tracing of borders between V1, V2, Vp, V3, & V4 in human brain.
Also, smooth along surface
From (Sereno et al, 1995, Science).

34. Function-Structure Integration in FreeSurfer
Basic Overview of process:
First: analyze your data with FEAT (No Smoothing)
Register FEAT to FreeSurfer Anatomical
Automatic (FLIRT)
Manual (tkregister2)
Sample FEAT output on the surface
Individual
Common Surface Space (Atlas/fsaverage)
** Can display any functional data, eg, zstat, fzstat, cope, pe, etc

35. Function-Structure Integration in FreeSurfer
Basic Overview of process (cont’d):
Mapping FreeSurfer Segmentations to FEAT
ie, displaying functional data on subcortical/cortical segmentation
ROI analysis based on segmentation
Group Analysis
Using GFEAT data
Mri_glmfit
See Comprehensive Instructions at:

36. Working with Freesurfer
Unix command-line (Linux, MacOSX)
GUI’s for viewing/editing
Tkmedit, tksurfer, tkregister
Directory structure, naming conventions
Pipeline
Recon-all: automated surface/volume analysis

37. File Formats Can store 4D (like NIFTI)
FreeSurfer uses a unique file format (mgz = compressed MGH file)
Can store 4D (like NIFTI)
cols, rows, slices, frames
Generic: volumes and surfaces
Surface: 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, GE, AFNI

38. FreeSurfer Directory Tree
Subject ID
$SUBJECTS_DIR
bert
fred
jenny
margaret …

39. FreeSurfer Directory Tree
Each data set has its own unique SubjectId (eg, bert)
Subject ID
Subject Name
bert
bem label morph mri scripts surf tiff label
orig T1 brain wm aseg
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).

40. Add Your Data bert bem label morph mri scripts surf tiff label
cd $SUBJECTS_DIR
mkdir –p bert/orig
mri_convert yourdicom.dcm bert/mri/orig/001.mgz
mri_convert yourdicom.dcm bert/mri/orig/002.mgz
bert
bem label morph mri scripts surf tiff label
orig
001.mgz 002.mgz

41. Fully Automated Reconstruction
1. Create directory for data:
mkdir –p $SUBJECTS_DIR/bert/orig
2. Copy/Convert data into directory:
mri_convert file.dcm $SUBJECTS_DIR/bert/orig/001.mgz
3. Launch reconstruction:
recon-all –s bert –autorecon-all
Come back in 48 hours …
Check your results – do the white and pial surfaces follow the boundaries?
-- Can be broken up

42. Individual Stages Green = Manual Intervention? recon-all -help
Volumetric Processing Stages (subjid/mri):
1. Motion Cor, Avg, Conform (orig.mgz)
2. Talairach transform computation
3. Non-uniform inorm (nu.mgz)
4. Intensity Normalization 1 (T1.mgz)
5. Skull Strip (brain.mgz)
6. EM Register (linear volumetric registration)
7. CA Intensity Normalization
8. CA Non-linear Volumetric Registration
9. CA Label (Volumetric Labeling) (aseg.mgz)
10. Intensity Normalization 2 (T1.mgz)
11. White matter segmentation (wm.mgz)
12. Edit WM With ASeg
13. Fill and cut (filled.mgz)
Surface Processing Stages (subjid/surf):
14. Tessellate (?h.orig)
15. Smooth1 (?h.smoothwm)
16. Inflate1 (?h.inflated)
17. QSphere (?h.qsqhere)
18. Automatic Topology Fixer (?h.orig)
19. Euler Number
20. Smooth2
21. Inflate2
22. Final Surfs (?h.white,?h.pial)
23. Cortical Ribbon Mask
24. Spherical Morph
25. Spherical Registration
26. Spherical Registration
27. Map average curvature to subject
28. Cortical Parcellation (Labeling)
29. Cortical Parcellation Statistics
30. Cortical Parcellation mapped to ASeg
Green = Manual Intervention?
recon-all -help
Note: ?h.orig means lh.orig or rh.orig

43. Workflow in Stages recon-all –autorecon1 (Stages 1-5)
Check talairach transform, skull strip, normalization (?)
recon-all –autorecon2 (Stages 6-23)
Check surfaces
Add control points: recon-all –autorecon2-cp (Stages 10-23)
Edit wm.mgz: recon-all –autorecon2-wm (Stages 13-23)
Edit brain.mgz: recon-all –autorecon2-pial (Stage 23)
recon-all –autorecon3 (Stages 24-30)
Note: all stages can be run individually
Roughly 15% need alterations/edits

44. Results
Volumes
Surfaces
Surface Overlays
ROI Summaries

45. Volumes Volume Viewer: tkmedit orig.mgz T1.mgz brainmask.mgz wm.mgz
filled.mgz
Subcortical Mass
$SUBJECTS_DIR/bert/mri
All “Conformed” 2563, 1mm3
Many more …
aseg.mgz
aparc+aseg.mgz
Volume Viewer:
tkmedit

46. Surfaces orig white pial inflated sphere,sphere.reg patch (flattened)
All surfaces have the same number of vertices. Different surfaces are made by moving the triangles around to achieve some goal. There is a one-to-one correspondence between surfaces which allows the user to cross-reference vertices from one surface to another (even across subjects) and to the volume. This feature is critical to get properly oriented. In the flat surface, there’s still a one-to-one correspondence, but not all vertices in the orig surface may be represented.
inflated
sphere,sphere.reg
patch (flattened)
$SUBJECTS_DIR/bert/surf
Number/Identity of vertices stays the same (except patches)
XYZ Location Changes
Flattening not done as part of standard reconstruction
Surface Viewer:
tksurfer

47. Surface Overlays lh.sulc on inflated lh.curv on inflated
lh.thickness on inflated
lh.sulc on pial
lh.curv on inflated
fMRI on flat
All surfaces have the same number of vertices. Different surfaces are made by moving the triangles around to achieve some goal. There is a one-to-one correspondence between surfaces which allows the user to cross-reference vertices from one surface to another (even across subjects) and to the volume. This feature is critical to get properly oriented. In the flat surface, there’s still a one-to-one correspondence, but not all vertices in the orig surface may be represented.
Value for each vertex
Color indicates value
Color: gray,red/green, heat, color table
Rendered on any surface
fMRI/Stat Maps too
lh.aparc.annot on inflated

48. ROI Summaries: $SUBJECTS_DIR/bert/stats aseg.stats – volume summaries
?h.aparc.stats – desikan/killiany parcellation summaries
?h.aparc.2005.stats – destrieux parcellation summaries
wmparc.stats – white matter parcellation
Index SegId NVoxels Volume_mm3 StructName normMean normStdDev normMin normMax normRange
Left-Cerebral-Exterior
Left-Cerebral-White-Matter
Left-Cerebral-Cortex
Left-Lateral-Ventricle
Left-Inf-Lat-Vent
Left-Cerebellum-Exterior ….
Routines to generate spread sheets of group data
asegstats2table --help
aparcstats2table --help