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2004 All Hands Meeting
Analysis of a Multi-Site fMRI Study Using Parametric Response Surface Models
Seyoung Kim
Padhraic Smyth
Hal Stern
(University of California, Irvine)
Multi-Site fMRI Study
Data collection - sensorimotor task• Human Phantom Data for Five Subjects
10 sites 4 runs per visit, 2 visits in each site
• Preprocessing with SPM99 The correction of head motion, normalization to a common brain
space, spatial smoothing β map : an activation map estimated from the fMRI time series using
general linear model
• Regions of interest Left/right precentral gyrus (motor region) Left/right superior temporal gyrus (auditory region) Left/right occipital lobe (visual region)
fMRI Activation Pattern
Spatial correlation of activation across voxels• bell shapes in local regions
location of the activation centers size of peak activations area of the local activation cluster
Beta C
oefficien
ts
Whole brain 2D slice of β-map
(Sensorimotor task)
Activation Shape
Variability in activation shape• More consistency in the location of activation centers across runs
within sites than between sites
Extract shape features and analyze variability on the features
Run 1-4, subject 3, visit 2
A 2-dimensional slice of right precentral gyrus at z=53
Parametric Response Surface Model
Superposition of M Gaussian surfaces with background• For βvalue at pixel x = (x1, x2)
(2-dimensional slice)
M : number of Gaussian components µ : background activation level For each of the mth Gaussian component (m = 1, …, M)
• bm : location of activation center
• km : size of peak activation
• σm : volume under the surface
Parameter Estimation with Stochastic Search
Posterior simulation in Bayesian framework• Markov chain Monte Carlo (MCMC)
Useful when direct sampling is not possible in highly nonlinear model
Summarize the posterior distribution with the mean of samples
• In our implementation Run MCMC for 20,000 iterations Estimate the parameters as the sample mean of the last 10,000
iterations
Analysis
For preliminary analysis, focus on• Sensorimotor data
Subject 1, 3 (from 10 sites, 2 visits, 4 runs)
• 2D cross sections Right precentral gyrus at z=53 Left superior temporal gyrus at z=33
• Number of Gaussian components (M) were chosen from visual inspection
Raw Data vs. Learned Surface
Raw data
Subject 3, visit 2, run 3
Estimated surface
Right precentral gyrus at z=53 Left superior temporal gyrus at z=33
Cross Site Variability (in Estimated Activation Centers bm, Right Precentral Gyrus z=53, Subject 3)
Visit level variability
Run level variability
Cross Site Variability (in Estimated Activation Centers bm, Right Precentral Gyrus at z=53, Subject 3)
Site level variability
Variance Component Analysis
Quantifying the contributions of different effects to the total variability in estimated shape parameters
Variance component model
• yijk : response, shape parameters
• u : overall mean effect
• si : effect from site i
• vij : effect from visit j of site i
• rijk : effect from run k of site i, visit j
Experiments
Estimation with Gibbs sampler • winBugs implementation• 1,000,000 iterations
• Use the mean of the last 200,000 samples as variance component
estimates Analyzed each subject, activation component separately Report the proportions of variance components
Variance Components Estimates(Right Precentral Gyrus at z=53)
Subject 1 Subject 3
Height Location Height Location
Bump1 Bump2 Bump1 Bump2
Site 0.51 0.86 0.50 0.58 0.67 0.90
Visit 0.22 0.07 0.13 0.18 0.02 0.03
Runs 0.27 0.07 0.37 0.24 0.31 0.07
Variance Component Estimates(Left Superior Temporal Gyrus at z=33)
Subject 1 Subject 3
Height Location Height Location
Bump1 Bump2 Bump1 Bump2 Bump1 Bump2 Bump1 Bump2
Site 0.25 0.52 0.05 0.06 0.49 0.59 0.40 0.45
Visit 0.42 0.25 0.18 0.03 0.12 0.05 0.04 0.02
Run 0.33 0.23 0.77 0.91 0.39 0.36 0.56 0.53
Conclusions and Future Work
The parametric response surface modeling is potentially useful in the analysis of multi-site fMRI data
Need to develop methods to automatically determine M Analysis of the data in 3D space Build a hierarchical model for estimating the surface models
across subjects and sites Analysis in the flattened cortical surface rather than in 3D
volumes