place_dmat_on_time_grid: Function to convert dmat (runs x regressor list) to a...
In UNCDEPENdLab/fmri.pipeline: Analysis of fMRI Data on HPC clusters

place_dmat_on_time_grid

R Documentation

Function to convert dmat (runs x regressor list) to a time-oriented representation. This yields a list of runs where each element is data.frame of volumes x regressors

Description

Function to convert dmat (runs x regressor list) to a time-oriented representation. This yields a list of runs where each element is data.frame of volumes x regressors

Usage

place_dmat_on_time_grid(
  dmat,
  convolve = TRUE,
  run_timing = NULL,
  bdm_args,
  lg = NULL
)

Arguments

`dmat`	A runs x regressors 2-d list where each element is a matrix containing onset, duration, and value for a signal
`convolve`	If `TRUE` (default), convolve the time-oriented signals with an HRF
`run_timing`	A vector of cumulative start times for each run in a multi-run dataset
`bdm_args`	A list of arguments passed to build_design_matrix, as well as a few fields added during the initial argument parsing. See build_design_matrix for details. Should contain: convolve_wi_run TRUE/FALSE run_volumes Numeric vector of run length normalizations Character vector of HRF normalizations for each regressor. Options are "none", "durmax_1", or "evtmax_1". add_derivs A logical vector (`TRUE/FALSE`) of regressors whose temporal derivatives should be included. Temporal derivatives are only applied if `convolve` is `TRUE`. convmax_1 A logical vector (`TRUE/FALSE`) denoting whether to rescale max height to 1 after convolution high_pass The cutoff frequency (in Hz) used for high-pass filtering. If `NULL`, no filtering is applied. tr The repetition time (sometimes called TR) in seconds hrf_parameters The parameters for the double-gamma HRF
`lg`	An lgr logger object used for logging messages

Details

Note that any volumes dropped from the beginning of each run should already be reflected in the timings of regressors in dmat. This prevents us from needing to have a drop_volumes implementation inside convolve_regressor, which is confusing anyhow. Likewise, run_timing should reflect the post-drop cumulative volumes.