In bbuchsbaum/fmrireg: R package for the anlysis of fmri data

fMRI event models

An "event model" is a description of an fMRI experiment, which consists of a collection of experimental "events" (stimuli/conditions/responses) which are embedded in an experimental design. By convolving each event type with a hemodynamic response function we from a set of regressors that are used to model and make inferences about the BOLD response.

A simple fMRI design

Consider a basic design, consisting of four stimulus types, each repeated 4 times over the course of a single scanning run. The stimuli are pictures of faces, scenes, tools and object presented for 2s a piece in a pseudorandom order. There is a uniformly distributed (4-7s) inter-stimulus interval (ISI) separating each stimulus presentation.

library(fmrireg)

cond <- c("face", "scene", "tool", "object")
NSTIM <- length(cond)*4

Now we contruct a "design table" by randomly sampling from the four conditions:

simple_design <- data.frame(stim=factor(sample(rep(cond, 4))), 
                     ISI=sample(4:7, NSTIM, replace=TRUE), 
                     run=rep(1, NSTIM),
                     trial=factor(1:NSTIM))

Event onsets are calculated as the cumulative sum of the ISI variables. Then we construct a sampling_frame which define the temporal sampling and block structure of the experiment. Here we have only 1 block and a repetition frequency (TR) of 2.

simple_design$onset <- cumsum(simple_design$ISI+2) -2
TR <- 2
sframe <- sampling_frame(blocklens=70, TR)

Now the fun part. We create an event_model that describes the experimental model in terms of the stimulus onsets, conditions, and hemodynamic response function ('hrf').

emodel <- event_model(onset ~ hrf(stim), data=simple_design, block = ~ run, sampling_frame=sframe)

print(emodel)

And we can plot the model:

plot(emodel)

A simple fMRI design with multiple blocks

cond <- c("face", "scene", "tool", "object")
NSTIM <- length(cond)*4

## construct a design table with two blocks
design1 <- data.frame(stim=factor(sample(rep(cond, 4))), 
                     ISI=sample(4:7, 16, replace=TRUE), 
                     run=rep(1, NSTIM))
design2 <- data.frame(stim=factor(sample(rep(cond, 4))), 
                     ISI=sample(4:7, 16, replace=TRUE), 
                     run=rep(2, NSTIM))
design1$onset <- cumsum(design1$ISI+2) -2
design2$onset <- cumsum(design2$ISI+2) -2

design <- rbind(design1, design2)

## create a 'sampling_frame' describing the temporal outline of the scan consisting of 70 images collected every once every 2 seconds.
TR <- 2
sframe <- sampling_frame(blocklens=c(70,70), TR)

## create the event model d
emodel <- event_model(onset ~ hrf(stim), data=design, block = ~ run, sampling_frame=sframe)
print(emodel)
plot(emodel)

A two-factor fMRI design with multiple blocks

cond1 <- c("face", "scene", "tool", "object")
cond2 <- c("attend", "ignore")
comb <- expand.grid(stim=cond1, task=cond2)
NSTIM <- nrow(comb) * 4

design <- do.call(rbind, lapply(1:2, function(i) {
  ind <- sample(rep(1:nrow(comb), length.out=NSTIM))
  d <- data.frame(stim=factor(comb$stim[ind]),
             task=factor(comb$task[ind]),
             ISI=sample(2:6, NSTIM, replace=TRUE), 
             run=rep(i, NSTIM))
  d$onset <- cumsum(d$ISI+2) -2
  d
}))

head(design)

## create a 'sampling_frame' describing the temporal outline of the scan consisting of 70 images collected every once every 2 seconds.
TR <- 2
sframe <- sampling_frame(blocklens=c(100,100), TR)

## create the event model d
emodel <- event_model(onset ~ hrf(stim, task), data=design, block = ~ run, sampling_frame=sframe)
print(emodel)
plot(emodel)

An event-related design with an amplitude-modulated variable

TR <- 2
sframe <- sampling_frame(blocklens=70, TR)
simple_design$RT <- scale(rnorm(nrow(simple_design), mean=700, sd=100))[,1]


## create the event model d
emodel <- event_model(onset ~ hrf(stim) + hrf(RT), data=simple_design, block = ~ run, sampling_frame=sframe)
print(emodel)
plot(emodel, "RT")

An event-related design with an amplitude-modulated variable crossed with factor

TR <- 2
sframe <- sampling_frame(blocklens=70, TR)


simple_design$RT <- scale(rnorm(nrow(simple_design), mean=700, sd=100))

## create the event model d
emodel <- event_model(onset ~ hrf(stim) + hrf(stim, RT), data=simple_design, block = ~ run, sampling_frame=sframe)
print(emodel)
plot(emodel, "stim:RT")

Trialwise event model to construct "beta-series" estimates

TR <- 2
sframe <- sampling_frame(blocklens=70, TR)

# create the event model d
emodel <- event_model(onset ~ trialwise(), data=simple_design, block = ~ run, sampling_frame=sframe)
print(emodel)
plot(emodel, "trialwise")