1. Biomedical Informatics Research Network Gregory G. Brown, Shaunna Morris, and Amanda Bischoff Grethe, VASDHS and University of California, San Diego Proportional Scaling of Multisite fMRI Data October 13, 2004, Boston, MA

2. Simplified Signal Equations: Proportional Scaling of Gray Scale Units

3. Subject 5 T 2 -star Weighted Images in Constant Gray Scale Units Acquired at Four Sites Sites vary by the proportionality constant k

4. Scaling of BOLD Response Depends on an Intrinsic Scaling Parameter M  M{1-(f  (  )  m  )}. %Signal change = M{1-(f  (  )  m  )} %Signal change  TE  V b [dHb]{1-(f  (  )  m  )}

5. Impact of Proportionality Constants K and M on fMRI Activation Measures Common fMRI measures Regression weight %Signal Change Z score fMRI Theory predicts that Proportionality constants, K and M, will have differential impact on fMRI measures

6. Time Series Regression Weight Regression Weight = Regression Coefficient = Mean Difference Image C experimental – C control C experimental = numerical code for the experimental condition C control = numerical code for the control condition Regression Coefficients inherit the original gray scale units. They are influenced by variation in both K and M scaling factors.

7. %Signal Change Regression Weight = Percent Signal Change = Regression Coefficient Mean MR Signal control condition Gray scale constant K is canceled. %Signal Change is influenced only by variation in BOLD constant M.

8. Z Score Regression Weight = t score = Regression Weight Standard Error z score: Φ(z) = T(t,df) The standard error reflects variation in the regression weight. So both proportionality constants, K and M, cancel. Yet site differences are reflected in both regression weight and standard error. Thus, it is unclear whether proportional scaling will reduce site effects of Z scores.

9. Proportional Scaling  For each ROI Mean of all sites divided by mean of a particular site

10. Calibration Study Outline Multi-Site BIRN Study: 11 Sites (BWH, Duke-UNC: 1.5T, Duke-UNC: 4.0T, Iowa, MGH, Minnesota, New Mexico, Stanford, UCI, UCLA, UCSD) 5 Healthy males as “Human Phantoms” 2 Visits on separate days per site per subject, 4 Sensorimotor runs, 2 breath-hold runs, Sternberg Scanning Task, and Auditory Mismatch Task per visit 3 Regions of Interest

11. Proportional Scaling  Sensorimotor Adjustment: For each ROI calculate the ratio of the mean sensorimotor response of all sites divided by the mean of a particular site. Multiply each subject’s BOLD response in an ROI by the site specific sensorimotor ratio.  Breath Hold Adjustment: For all cortical voxels calculate the ratio of the mean breath hold response at all sites divided by the mean of a particular site. Multiply each subject’s BOLD response to the sensorimotor task by the site specific breath hold ratio.

12. Between Site Variation – fBIRN Sensorimotor Task:: % Signal Change Range of Median Site Differences is about.5%

13. Generalizability and Dependability of Sensorimotor Scaled and Breath Hold Scaled Regression Weights Region of Interest Unadjusted Data Data Proportionally Scaled (sensorimotor data) Data Proportionally Scaled (breath hold data) Dependability Visual.22.92.86 Hand.35.89.88 Auditory.06.46.34 Generalizability Visual.67.92.96 Hand.67.89.90 Auditory.33.48.50 BOLD Response from Sensorimotor Task

14. Generalizability and Dependability of Sensorimotor Scaled and Breath Hold Scaled % Signal Change BOLD Response from Sensorimotor Task Region of Interest Regression Weights % Signal Change Z Scores Dependability Visual.86.91 Hand.88.85.86 Auditory.34Neg Variance.64 Generalizability Visual.96.94.91 Hand.90.87.95 Auditory.50Neg Variance.95

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