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