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