Linear Model for FMRI Data

Create the expected BOLD response for a given task indicator function.

fmri.stimulus(scans = 1, onsets = c(1), durations = c(1), TR = 2,
                times = FALSE, sliceorder = NULL,
                type = c("canonical", "gamma", "boxcar", "user"),
                par = NULL, scale = 10, hrf = NULL, verbose = FALSE)

Arguments

scans	number of scans
onsets	vector of onset times (in scans)
durations	vector of duration of ON stimulus in scans or seconds (if `!is.null(times)`)
TR	time between scans in seconds (TR)
times	onset times in seconds. If present `onsets` arguments is ignored.
sliceorder	order of slice acquisition. If provided separate expected bold responses are calculated for the slices taking slice acquisition times into account. Default: no slice timing.
type	One of `"canonical"`, `"gamma"`, `"boxcar"`, `"user"`
par	Possible parameters to the HRF.
scale	Temporal undersampling factor
hrf	If `type` is `"user"` this should be a function evaluating the hemodynamic response function
verbose	Report more if `TRUE`

Details

The functions calculates the expected BOLD response for the task indicator function given by the argument as a convolution with the hemodynamic response function.

If sliceorder provides an ordering of slice acquisitions a matrix of expected Bold responses with colums corresponding to the slice number is computed.

For type is "canonical" the latter is modelled by the difference between two gamma functions as given in the reference (with the defaults for a1, a2, b1, b2, cc given therein):

$$\left(\frac{t}{d_1}\right)^{a_1} \exp \left(-\frac{t-d_1}{b_1}\right) - c \left(\frac{t}{d_2}\right)^{a_2} \exp \left(-\frac{t-d_2}{b_2}\right) $$

The parameters a1, a2, b1, b2, cc of this function can be changed through the argument par in this order.

Other choices are a simple gamma function

$$\frac{1}{k\tau_h (k-1)!} \left( \frac{t}{\tau_h} \right)^k \exp \left( - \frac{t}{\tau_h} \right)$$

or the "boxcar" stimulus, or a user defined function hrf.

The dimension of the function value is set to c(scans, 1).

If !is.null(times) durations are specified in seconds.

Value

Vector with dimension c(scans, 1) or a matrix with dimension c(scans, number of slices).

References

Worsley, K.J., Liao, C., Aston, J., Petre, V., Duncan, G.H., Morales, F., Evans, A.C. (2002). A general statistical analysis for fMRI data. NeuroImage, 15:1-15.

Polzehl, J. and Tabelow, K. (2007) fmri: A Package for Analyzing fmri Data, R News, 7:13-17 .

Author

Karsten Tabelow tabelow@wias-berlin.de, Joerg Polzehl polzehl@wias-berlin.de

Examples

  # Example 1
  hrf <- fmri.stimulus(107, c(18, 48, 78), 15, 2)
  z <- fmri.design(hrf, 2)
  par(mfrow=c(2, 2))
  for (i in 1:4) plot(z[, i], type="l")