1. Introduction to FreeSurfer http://surfer.nmr.mgh.harvard.edu

2. Post Your Questions! http://surfer.nmr.mgh.harvard.edu/cgi-bin/fsurfer/questions.cgi

3. 3 Why FreeSurfer? Anatomical analysis is not like functional analysis – it is completely stereotyped. Registration to a template (e.g. MNI/Talairach) does not account for individual anatomy. Even if you do not care about the anatomy, anatomical models allow functional analysis not otherwise possible.

4. 4 ICBM Atlas 12 DOF (Affine) Why not just register to an ROI Atlas?

5. 5 Subject 1 Subject 2 aligned with Subject 1 (Subject 1’s Surface) Problems with Affine (12 DOF) Registration (you will get sick of this slide)

6. 6 Surface and Volume Analysis Cortical Reconstruction and Automatic Labeling Inflation and Functional Mapping Surface Flattening Surface-based Inter-subject Alignment and Statistics Automatic Subcortical Gray Matter Labeling Automatic Gyral White Matter Labeling

7. 7 Talk Outline 1.Cortical (surface-based) Analysis. 2.Volume Analysis.

8. Talk Outline 1.Cortical (surface-based) Analysis. 2.Volume Analysis.

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

10. 10 Flat Map of Monkey Visual Areas D.J. Felleman and D.C. Van Essen, CC, 1991

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

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

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

14. 14 Thanks to Larry Wald for this slide. M-G-H

15. 15 Surfaces: White and Pial

16. 16 Inflation

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

18. 18 Surface Model Mesh of triangles gives a measurable size Allows us to measure Area, Curv., Thickness (distance b/w vertices) Vertex = point of 6 triangles Triangles/Faces ~ 150,000 per hemi 1:1 correspondence of vertices XYZ at each vertex

19. 19 Cortical Thickness white/gray surface pial surface lh.thickness, rh.thickness Distance between white and pial surfaces One value per vertex

20. 20 A Surface-Based Coordinate System

21. Comparing Coordinate Systems and Brodmann Areas Cumulative histogram (red=surface, blue=nonlinear Talairach) Ratio of surface accuracy to volume accuracy

22. 22 Automatic Surface Segmentation Precentral Gyrus Postcentral Gyrus Superior Temporal Gyrus Based on individual’s folding pattern

23. 23 Inter-Subject Averaging Subject 1 Subject 2 Native Spherical Surface-to- Surface Surface-to- Surface GLM Demographics mri_glmfit cf. Talairach

24. 24 Borrowed from (Halgren et al., 1999) Visualization

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

26. Talk Outline 1.Cortical (surface-based) Analysis. 2.Volume Analysis.

27. 27 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)!

28. 28 Volumetric Segmentation (aseg) Caudate Pallidum Putamen Amygdala Hippocampus Lateral Ventricle Thalamus White Matter Cortex Not Shown: Nucleus Accumbens Cerebellum

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

30. 30 Combined Segmentation aparc+aseg aseg aparc

31. 31 Gyral White Matter Segmentation Nearest Cortical Label to point in White Matter wmparc + + aparc aparc+aseg

32. 32 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 FreeSurferBe Happy

33. 33 MGH Bruce Fischl Allison Stevens Nick Schmansky Andre van der Kouwe Doug Greve David Salat Evelina Busa Lilla Zöllei Koen Van Leemput Sita Kakunoori Ruopeng Wang Rudolph Pienaar Krish Subramaniam Diana Rosas Acknowledgements UC San Diego Anders Dale MIT Polina Golland B. T. Thomas Yeo Mert Sabuncu Florent Segonne Peng Yu Ramesh Sridharan MGH Jean Augustinack Martin Reuter Anastasia Yendiki Jon Polimeni Kristen Huber UCL Marty Sereno NINDS MGH (past) Brian T Quinn Xiao Han Niranjini Rajendran Jenni Pacheco Sylvester Czanner Gheorghe Postelnicu Sean Marrett