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 Coefficients 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 = (x 1, x 2 ) (2-dimensional slice)  M : number of Gaussian components  µ : background activation level  For each of the mth Gaussian component (m = 1, …, M) b m : location of activation center k m : size of peak activation σ m : 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 b m, Right Precentral Gyrus z=53, Subject 3) Visit level variability Run level variability

Cross Site Variability (in Estimated Activation Centers b m, 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 y ijk : response, shape parameters u : overall mean effect s i : effect from site i v ij : effect from visit j of site i r ijk : 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 1Subject 3 HeightLocationHeightLocation Bump1Bump2Bump1Bump2 Site Visit Runs

Variance Component Estimates (Left Superior Temporal Gyrus at z=33) Subject 1Subject 3 HeightLocationHeightLocation Bump1Bump2Bump1Bump2Bump1Bump2Bump1Bump2 Site Visit Run

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