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Biomedical InformaticsResearch 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
Simplified Signal Equations: Proportional Scaling of Gray Scale Units
eS S 0
)e(1e S S TR/T1-TE/T20
)e(1e S k S TR/T1-TE/T20
Subject 5 T2-star Weighted Images in Constant Gray Scale Units Acquired at Four Sites
Sites vary by the proportionality constant k
Scaling of BOLD ResponseDepends on an Intrinsic Scaling Parameter M
eS S 0 M{1-(f() m)}.
SS
%Signal change = M{1-(f() m)} SS
%Signal change TE Vb[dHb]{1-(f() m)}
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
Time Series Regression Weight
Regression Weight = Regression Coefficient = Mean Difference Image
Cexperimental – Ccontrol
Cexperimental = numerical code for the experimental condition
Ccontrol = 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.
%Signal Change
Regression Weight = Percent Signal Change = Regression Coefficient
Mean MR Signalcontrol condition
Gray scale constant K is canceled. %Signal Change is influenced only by variation in BOLD constant M.
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.
Proportional Scaling
For each ROI
Mean of all sites divided by mean of a particular site
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
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.
Between Site Variation – fBIRN Sensorimotor Task:: % Signal Change
Mean %Signal Change by Vendor and Field Strength
Bars Represent 95% Confidence Interval
Vendor
PickerSiemensGE
%S
igna
l Cha
nge:
Ave
rage
of A
ll R
OIs
1.2
1.1
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.10.0
Field Strength
1.5T
3.0T
4.0T
Range of Median Site Differences is about .5%
Generalizability and Dependability of Sensorimotor Scaled and Breath Hold Scaled Regression Weights
Region of Interest
UnadjustedData
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
Generalizability and Dependability of Sensorimotor Scaled and Breath Hold Scaled % Signal Change
BOLD Response from Sensorimotor Task
Region of Interest
RegressionWeights
% SignalChange
Z Scores
Dependability
Visual .86 .86 .91
Hand .88 .85 .86
Auditory .34 Neg Variance .64
Generalizabilit
y
Visual .96 .94 .91
Hand .90 .87 .95
Auditory .50 Neg Variance .95
END