EMC2 (version 3.2.0)

plot_fmri: Plot fMRI peri-stimulus time courses

Description

This function plots average BOLD response around specified events for a single ROI by using FIR based event estimation, all event_types in events are taken into account in the FIR. Posterior predictives can be overlaid via the post_predict argument.

Usage

plot_fmri(
  timeseries,
  post_predict = NULL,
  events,
  event_type,
  high_pass = TRUE,
  high_pass_model = "cosine",
  posterior_args = list(),
  legend_pos = "topleft",
  layout = NA,
  n_cores = 1,
  ...
)

Value

NULL. Produces plots as a side-effect.

Arguments

timeseries

A data frame with columns 'subjects', 'run', 'time', and one ROI measurement column.

post_predict

Optional posterior predictive samples data frame (not shown in examples).

events

A data frame with columns 'subjects', 'run', 'onset', 'duration', 'event_type', and 'modulation'.

event_type

Character string specifying which event_type in events to plot.

high_pass

Logical indicating whether to apply high-pass filtering. Alternatively, specifying 'add' adds the regressors to the design matrix in the FIR. The choice here should be the same as the choice for convolve_design_matrix

high_pass_model

Character indicating which type of high-pass filtering to apply ('cosine', 'poly')

posterior_args

Named list of graphical parameters for posterior predictive lines.

legend_pos

Position of the legend. Default: "topleft".

layout

Panel layout matrix for multiple modulation groups. NULL leaves current layout

n_cores

Number of cores to calculate FIR across subjects with.

...

Additional graphical parameters passed to plotting functions (e.g., col, lwd, lty).

Examples

ts <- data.frame(
  subjects = rep(1, 100),
  run      = rep(1, 100),
  time     = seq(0, 99),
  ROI      = rnorm(100)
)
events <- data.frame(
  subjects   = rep(1, 5),
  run        = rep(1, 5),
  onset      = c(10, 30, 50, 70, 90),
  event_type = rep("A", 5),
  modulation = rep(1, 5),
  duration   = rep(0.5, 5)
)
plot_fmri(ts, events = events, event_type = "A")