convolve_regressor: This function convolves a regressor with a normalized HRF...

In PennStateDEPENdLab/dependlab: Useful functions for the UNC Chapel Hill DEPENd Lab (Director: Michael Hallquist)

This function convolves a regressor with a normalized HRF whose peak amplitude is 1.0

Description

It extends fmri.stimulus by allowing for two normalization approaches (building on AFNI dmUBLOCK): 1) "evtmax_1": pre-convolution HRF max=1.0 normalization of each stimulus regardless of duration: identical to dmUBLOCK(1) 2) "durmax_1": pre-convolution HRF max=1.0 normalization for long events (15+ sec) – height of HRF is modulated by duration of event: identical to dmUBLOCK(0)

Usage

convolve_regressor(
  n_vols,
  reg,
  tr = 1,
  normalization = "none",
  rm_zeros = TRUE,
  center_values = TRUE,
  convmax_1 = FALSE,
  demean_convolved = FALSE,
  high_pass = NULL,
  convolve = TRUE,
  beta_series = FALSE,
  ts_multiplier = NULL,
  drop_volumes = 0,
  hrf_parameters = c(a1 = 6, a2 = 12, b1 = 0.9, b2 = 0.9, cc = 0.35)
)

Arguments

n_vols	The number of volumes (scans) to be output in the convolved regressor
reg	A matrix containing the trial, onset, duration, and value for each event
tr	The repetition time in seconds
normalization	The HRF normalization method used: "none", "durmax_1", or "evtmax_1"
rm_zeros	Whether to remove zeros from events vector prior to convolution. Generally a good idea since we typically center values prior to convolution, and retaining zeros will lead them to be non-zero after mean centering.
center_values	Whether to demean values vector before convolution. Default TRUE.
convmax_1	Whether to rescale the convolved regressor to a maximum height of 1.
demean_convolved	Whether to demean the regressor after convolution (default: TRUE)
high_pass	The cutoff frequency (in Hz) used for high-pass filtering. If NULL, no filtering is applied.
convolve	If TRUE, the regressor is convolved with the HRF. If FALSE, the regressor values are simply aligned onto the time grid without convolution based on the corresponding onsets, durations, and values.
beta_series	If TRUE, split reg into separate regressors for each event (row). These can be used to estimate separate betas in the GLM for each event.
ts_multiplier	A vector that is n_vols in length that will be multiplied against the stimulus vector before convolution.
drop_volumes	The number of volumes to drop from the beginning of the regressor
hrf_parameters.	A named vector of parameters passed to fmri.stimulus that control the shape of the double gamma HRF. Default: c(a1 = 6, a2 = 12, b1 = 0.9, b2 = 0.9, cc = 0.35).

Author(s)

Michael Hallquist

PennStateDEPENdLab/dependlab documentation built on April 10, 2024, 5:15 p.m.