Presentation on theme: "Introduction to FreeSurfer"— Presentation transcript:

1 Introduction to FreeSurfer

2 Overview Format: who, what, where, how, why, when

Processing stream run-through Primary themes based on history: Cortical surfaces Subcortical segmentations Home page walk-through Warning! FreeSurfer has a steep learning curve!

3 What is FreeSurfer? A suite of software tools for the analysis of neuroimaging data Full characterizes anatomy Cortex – thickness, folding patterns, ROIs Subcortical – structure boundaries Surface-based inter-subject registration Multi-modal integration fMRI (task, rest, retinotopy) DTI tractography PET, MEG, EEG

4 Why is FreeSurfer special?

There are other cortical and subcortical tools: BrainVoyager, Caret, BrainVisa, SPM, FSL (of late) Each has varying degrees of segmentation accuracy w/ varying levels of user intervention FreeSurfer is highly specialized in it’s: cortical surface representation from the grey matter segmentation surface-based group registration capabilities accuracy of subcortical structure measurements

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.

6 Problems with Affine (12 DOF) Registration

Subject 2 aligned with Subject 1 (Subject 1’s Surface) Subject 1

7 FreeSurfer Analysis Pipeline Overview

Surface Mesh Inflation Surface ROI Group Template E D J Curvature Sphere C F I Spatial Normalization Individual T1 A A Surface Extraction B Thickness 2mm 4mm Deformation Field G H Apply Deformation Volume ROI O Statistical Map Statistical Map N M Group Analysis L K Smooth p<.01 p<.01 Thickness (Group Space) 7 Other Subjects

8 History Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach, Dale, A.M., and Sereno, M.I. (1993). Journal of Cognitive Neuroscience 5: Constrain the inverse solution by creation of a surface model

9 Dale and Sereno, 1993 Electric and magnetic dipole locations (left) constrained by surface model created by shrink-wrapping grey matter (right).

10 History (cont.) Cortical Surface-Based Analysis I: Segmentation and Surface Reconstruction, Dale, A.M., Fischl, B., Sereno, M.I., (1999). NeuroImage 9(2): Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System, Fischl, B., Sereno, M.I., Dale, A.M., (1999). NeuroImage, 9(2): Automated Manifold Surgery: Constructing Geometrically Accurate and Topologically Correct Models of the Human Cerebral Cortex, Fischl, B., Liu, A. and Dale, A.M., (2001). IEEE Transactions on Medical Imaging, 20(1):70-80.

11 Cortical Surface-based Analysis

Prior surface models used pial surface representation for visualization and secondary analysis This set of papers outlined the method of white surface creation followed by grey matter surface creation based on intensity gradient and smoothness constraints Allowed accurate morphometry and inter-subject registration based on folding patterns

12 Surfaces: White and Pial

13 Cortical Thickness pial surface

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

14 A Surface-Based Coordinate System

15 Inter-Subject Averaging

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

16 History (cont.) 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: 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. 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):

17 Volumetric Segmentation (aseg)

Caudate Pallidum Putamen Amygdala Hippocampus Lateral Ventricle Thalamus White Matter Cortex Not Shown: Nucleus Accumbens Cerebellum

18 Surface Segmentation (aparc)

Precentral Gyrus Postcentral Gyrus Superior Temporal Gyrus Based on individual’s folding pattern

19 Combined Segmentation

aparc aparc+aseg aseg

20 Today Longitudinal processing

Segmentation of white matter fascicles using diffusion MRI Combined volume and surface registration Segmentation of hippocampal subfields Estimation of architectonic boundaries from in-vivo and ex- vivo data

21 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

22 Who Massachusetts General Hospital + MIT + Harvard, Martinos Center for Biomedical Imaging Boston community: Boston University, Tufts, Northeastern, Brandeis, Brigham and Womens, Childrens, McClean, Veterans Administration Bruce Fischl, P.I.

23 Home page walk-through

Mailing list (provide a useful bug report please!) Wiki, and wiki account Download and install License Tutorials Acknowledgements Papers