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

2. Post Your Questions!

3. To Caffeinate or not to Caffeinate?
Please don’t spill coffee (or anything else on the laptops), or if you feel you must, please be prepared to fund a replacement!
(we will have coffee this afternoon at the break) 3

4. Overview FreeSurfer Intro
General format: talk followed by tutorial (both are on the wiki course page, but please don’t download tutorial data or FreeSurfer– it can kill the network)
Structural, Diffusion, fMRI processing
Group Analysis (for all of the above)
Preprocess (motion correct, eddy correct, etc)
Choose method of comparison
i.e. ROI vs. voxel/vertex-based
Find common coordinate space (voxel/vertex-based)
various registration methods
Evaluate group/differences/similarities/correlations
Multimodal Integration

5. 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. 5

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

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

8. 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

9. Cortical (surface-based) Analysis. Volume Analysis.
Talk Outline
Cortical (surface-based) Analysis.
Volume Analysis. 9

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

11. What Can One Do With A Surface Model?
goal: use model to imposed desired activity pattern on V1
desired shape of activity pattern
required shape of stimulus
w=k log(z+a)
left primary visual cortex
right visual hemifield
Collaboration with Jon Polimeni and Larry Wald.

12. Tangential Resolution Measured with Surface-based Analysis
Collaboration with Jon Polimeni and Larry Wald.

13. Tangential Resolution Measured with Surface-based Analysis
Collaboration with Jon Polimeni and Larry Wald.

14. Aim 1 of our NCRR Center Grant, spelling:
NeuroMarketing!
Aim 1 of our NCRR Center Grant, spelling:
“MGH Center for Functional Neuroimaging Technologies; and NCRR Center for Research Resources.”
(just kidding)
Thanks to Larry Wald for this slide. 14

15. Surfaces: White and Pial

16. Inflation

17. Surface Flattening – Whole Hemisphere
superior temporal
Metrically optimal flat map
calcarine
central
sylvian
anterior
posterior
Inflated surface with cuts

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

19. A Surface-Based Coordinate System

20. 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

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

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

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

24. Rosas et al., 2002
Sailer et al., 2003
Kuperberg et al., 2003
Fischl et al., 2000
Gold et al., 2005
Salat et al., 2004
Rauch et al., 2004

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

26. 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)!

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

28. Volume Differences Predictive of AD
Data courtesy of Drs Marilyn Albert and Ron Killiany

29. Combined Segmentation
aparc
aparc+aseg
aseg

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

31. 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

32. Acknowledgements MGH Allison Stevens MGH Martin Reuter MIT MGH (past)
Nick Schmansky
Andre van der Kouwe
Doug Greve
David Salat
Evelina Busa
Lilla Zollei
Koen Van Leemput
Sita Kakunoori
Ruopeng Wang
Rudolph Pienaar
Krish Subramaniam
Diana Rosas
MGH
Jean Augustinack
Martin Reuter
Anastasia Yendiki
Jon Polimeni
Kristen Huber
MIT
Polina Golland
B. T. Thomas Yeo
Mert Sabuncu
Florent Segonne
Peng Yu
Ramesh Sridharan
MGH (past)
Brian T Quinn
Xiao Han
Niranjini Rajendran
Jenni Pacheco
Sylvester Czanner
Gheorghe Postelnicu
Sean Marrett
UC San Diego
Anders Dale
UCL
Marty Sereno
NINDS

33. 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:
Also, smooth along surface
From (Sereno et al, 1995, Science).

34. Flat Map of Monkey Visual Areas
D.J. Felleman and D.C. Van Essen, CC, 1991