fmri.stimulus: Linear Model for FMRI Data

In WIAS-BERLIN/fmri: Analysis of fMRI Experiments

Linear Model for FMRI Data

Description

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

Usage

  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 (if times==FALSE)) or seconds (if times==TRUE))
TR	time between scans in seconds (TR)
times	logical. If TRUE onsets and durations are given in units of time not number of scans. Defaults to FALSE.
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 columns 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).

Author(s)

Karsten Tabelow [e-mail], Joerg Polzehl [e-mail]

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 .

See Also

fmri.design, fmri.lm

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")

WIAS-BERLIN/fmri documentation built on Sept. 18, 2023, 4:26 a.m.