fmri.stimulus function

Linear Model for FMRI Data

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 (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):

(td1)a1exp⁡(−t−d1b1)−c(td2)a2exp⁡(−t−d2b2)(x/d1)a1∗exp(−(x−d1)/b1)−c∗(x/d2)a2∗exp(−(x−d2)/b2) \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)(x/d1)^a1 * exp(-(x - d1)/b1) - c * (x/d2)^a2 * exp(-(x - d2)/b2)

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

1kτh(k−1)!(tτh)kexp⁡(−tτh) \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.

Returns

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(s)

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

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")
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