Baxter Rogers (VUIIS): fMRI Brain

• Finding location in brain where there are signals when math problems being solved, then look at differential math problems
• 10-20 subjects each doing 4 math problems
• One cell in 20x4 table may be a time series from one pixel, averaged over several activations. There are baseline levels when the math problem is not being done
• Done over 10,000 pixels
• Randomized order of math problems; within a problem there are runs over time with alternating 40s control periods; data use differences
• An analysis with a multiplicity adjustment that ignores the spatial correlation between pixels will be conservative
• Test for existence of any signal; Wilcoxon signed rank test can be used to test for a signal for one math problem (e.g., A) (analog of paired t-test); to handle 4 simultaneously need a multivariate test or an adjustment for cluster sampling; a nonparametric cluster method may not have enough power unless there were more subjects
• A more comprehensive mixed effects model could use original data, not differences from control. This model can solve the one-sample (Wilcoxon sign-rank type) problem also, since it can provide a contrast with control
• R functions to look at include lme() and nlme()
• 27Mar06

setwd('/media/sda1/clinic') library(Hmisc) ls() xless(taskdata) xless(taskdata) summary(aov(PctChg~Task+Error(Subject),subset=which(ROI==1))) lm(PctChg~Task) summary(lm(PctChg~Task)) library(Design) f <- ols(PctChg ~ Task, x=TRUE, y=TRUE) anova(f) g <- robcov(f, Subject) anova(g) h <- bootcov(f, Subject, B=1000) anova(h) anova(g) lot(summary(g)) g <- robcov(f, Subject) # Cluster sandwich covariance matrix estimator to account for # intra-subject correlation without assuming correlation structure dd <- datadist(taskdata); options(datadist='dd') plot(summary(g)) plot(g, Task=NA, method='dot') for(a in levels(Task)[1:4]) for(b in levels(Task)[1:4]) { if(a==b) next cat(a,':',b,'\n') print(contrast(g, list(Task=a), list(Task=b))) } anova(f) anova(g)

We considered a bootstrap ranking procedure. A simpler approach but one that requires 6 separate bootstrap rankings is to, for each of 6 pairs of tasks, ranks the 15 regions from 1-15 on the difference in the two tasks. This leads to a 95% coverage interval for the rank of any given region across the 16 subjects. The 16 rankings are independent.

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