fmri (version 1.9.12.1)

fmri.stimulus: 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)

Value

Vector with dimension c(scans, 1) or a matrix with dimension

c(scans, number of slices).

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

Author

Karsten Tabelow , Joerg Polzehl

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.

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")