Introduc)on to FreeSurfer h0p://surfer.nmr.mgh.harvard.edu. Jenni Pacheco.
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1 Introduc)on to FreeSurfer h0p://surfer.nmr.mgh.harvard.edu Jenni Pacheco
2 Outline Processing Stages Command line Stream Assemble Data (mris_preproc, mri_surf2surf) Design/Contrast (GLM Theory) (design matrix) Analyze (mri_glmfit) Visualize (tksurfer, QDEC) Interac)ve/Automated GUI (QDEC) Correc)on for mul)ple comparisons (mri_surfcluster) 2
3 Surface-based Study (Thickness) 3
4 Example: Thickness Study 1. $SUBJECTS_DIR/bert/surf/lh.thickness 2. $SUBJECTS_DIR/fred/surf/lh.thickness 3. $SUBJECTS_DIR/sara/surf/lh.thickness 4. $SUBJECTS_DIR/margaret/surf/lh.thickness 5. lh.thickness is in the subject s surface space, Ie, not a common surface space.
5 Processing Stages Specify Subjects and Surface measures Assemble Data: Resample into common space Smooth Concatenate into one file Model and Contrasts (GLM) Fit Model (Es)mate) Correct for mul)ple comparisons Visualize 5
6 Subject 1 Native Inter Subject Averaging Spherical GLM Spherical Subject 2 Demographics Surface-to- Surface Surface-to- Surface
7 GLM Theory y is surface-based measure X is design matrix Beta are regression coefficients C is contrast matrix GLM: fit for beta Null Hypotheses: gamma = 0 Performed at each surface vertex Need: y X C Conceptually no different than volumebased Output: beta gamma (cope) t/f/p
8 Surface based Sta)s)cal Analysis Making an average subject from your set of subjects (or use fsaverage) Construc)ng a FreeSurfer Group Descriptor File (FSGD) Preprocessing the group data Construc)ng the design matrix Construc)ng contrast matrices to test hypotheses Correc)ng for mul)ple comparisons
9 Average(Target) Subject Use the pre made average subject, fsaverage Average of 40 subjects Create your own average subjects from your data set Use one individual subject from your group Something else
10 Surface based Sta)s)cal Analysis Making an average subject from your set of subjects (or use fsaverage) Construc)ng a FreeSurfer Group Descriptor File (FSGD) Preprocessing the group data Construc)ng the design matrix Construc)ng contrast matrices to test hypotheses Correc)ng for mul)ple comparisons
11 FSGD File FSGDF = FreeSurfer Group Descriptor File, Supplies (most of) X and y. GroupDescriptorFile 1 Class Male red square Class Female blue circle Variables Age Weight IQ Input bert Male Input fred Male Input sara Female Input margaret Female One Discrete Factor (Gender) with Two Levels (M&F) Three Continuous Variables: Age, Weight, IQ
12 Surface based Sta)s)cal Analysis Making an average subject from your set of subjects (or use fsaverage) Construc)ng a FreeSurfer Group Descriptor File (FSGD) Preprocessing the group data Construc)ng the design matrix Construc)ng contrast matrices to test hypotheses Correc)ng for mul)ple comparisons
13 mris_preproc Assembles your subjects into a common space (spherical) and gathers the information from the measurement you are using (thickness) into one file. Will need to specify: fsgd file Hemisphere Measure (thickness, curv, sulc, functional values, etc ) Target subject Input: y X C
14 mris_preproc Command mris_preproc fsgd gender_age.txt target average hemi lh meas thickness out lh.gender_age.thickness.mgh
15 Surface Smoothing Uses output from mris_preproc lh.thickness.mgh 2D surface based smoothing (eg, fwhm = 10 mm) Saves lh.thickness.sm10.mgh Why should you smooth? Improve CNR Improve inter-subject registration How much smoothing? Blob-size Typically mm FWHM More forgiving than volume-based Input: y X C
16 mri_surf2surf Command mri_surf2surf hemi lh s average sval lh.gender_age.thickness.mgh fwhm 10 tval lh.gender_age.thickness.10.mgh
17 Surface based Sta)s)cal Analysis Making an average subject from your set of subjects (or use fsaverage) Construc)ng a FreeSurfer Group Descriptor File (FSGD) Preprocessing the group data Construc)ng the design matrix Construc)ng contrast matrices to test hypotheses Correc)ng for mul)ple comparisons
18 DOSS vs DODS Different Offset, Same Slope Different Offset, Different Slope
19 Design matrix and contrasts DOSS Different Offset, Same Slope Female Class Male Class X = Age for Males and Females C = [ ] } Tests for the difference in offset between groups Input: y X C #Regressors = Nv+Nc = 3+2=5 Fewer regressors than DODS DOF = #Rows - #Regressors
20 Design matrix and contrasts DODS Different Offset, Different Slope Female Group Male Group X = Male Age Female Age C = [ ] } Same test, different vector #Regressors = (Nv+1)*Nc = (3+1)*2=8 DOF = #Rows - #Regressors Input: y X C
21 mri_glmfit Reads in FSGD File and constructs X Reads in your contrasts (C1, C2, etc) Uses output from mris_surf2surf lh.thickness.sm10.mgh Fits GLM Computes contrasts (gamma) t or F ra)os and significances
22 mri_glmfit Command mri_glmfit y lh.thickness.sm10.mgh fsgd gender_age.txt doss surf fsaverage lh glmdir lh.gender_age.glmdir C age.mat C gender.mat
23 mri_glmfit Output Creates: lh.gender_age.glmdir/ beta.mgh parameter estimates eres.mgh residual error rvar.mgh residual error variance y.fsgd fsgd file used for scatter plot etc age/ sig.mgh -log10(p) gamma.mgh, F.mgh gender/ sig.mgh -log10(p) gamma.mgh, F.mgh
24 Visualization with tksurfer Threshold: -log10(p), Eg, 2=.01 Saturation: -log10(p), Eg, 5= False Discovery Rate Eg,.01 View->Configure->Overlay File->LoadOverlay
25 Visualization with tksurfer File-> Load Group Descriptor File
26 Problem of Mul)ple Comparisons p < 0.10 p < 0.01 p <
27 Correc)on for Mul)ple Comparisons Cluster based Monte Carlo simula)on Permuta)on Tests Surface Gaussian Random Fields (GRF) There but not fully tested False Discovery Rate (FDR) built into tksurfer and QDEC. (Genovese, et al, NI 2002) 27
28 Clustering 1. Choose a vertex wise threshold Eg, 2 (p<.01), or 3 (p<.001) Sign (pos, neg, abs) 2. A cluster is a group of connected (neighboring) ver)ces above threshold 3. Cluster has a size (area in mm 2 ) p<.01 (-log10(p)=2) Negative p<.0001 (-log10(p)=4) Negative 28
29 Cluster based Correc)on for Mul)ple Comparisons 1. Simulate data under Null Hypothesis: Synthesize Gaussian noise and then smooth (Monte Carlo) Permute rows of design matrix (Permuta)on, orthog) 2. Analyze, threshold, cluster, max cluster size 3. Repeat 10,000 )mes 4. Analyze real data, get cluster sizes 5. P(cluster) = #MaxClusterSize > ClusterSize/ mri_glmfit-sim 29
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