get_medusa_interpolated_ts: front-end function for taking a list of windowed time series...
In UNCDEPENdLab/fmri.pipeline: Analysis of fMRI Data on HPC clusters

get_medusa_interpolated_ts

R Documentation

front-end function for taking a list of windowed time series by mask value, interpolating them onto a time grid, and (optionally) averaging across voxels/units within a value to derive the mean interpolated time series

Description

front-end function for taking a list of windowed time series by mask value, interpolating them onto a time grid, and (optionally) averaging across voxels/units within a value to derive the mean interpolated time series

Usage

get_medusa_interpolated_ts(
  fmri_obj,
  event = NULL,
  time_before = -3,
  time_after = 3,
  collide_before = NULL,
  collide_after = NULL,
  pad_before = -1.5,
  pad_after = 1.5,
  output_resolution = NULL,
  group_by = "trial",
  time_audit = FALSE
)

Arguments

`fmri_obj`	an fmri_ts object containing a single run of data with corresponding events
`event`	the event to which the fmri time series should be aligned (column in `fmri_obj$event_data`)
`time_before`	How many seconds before the `event` do we want data. Default: -3
`time_after`	How many seconds after the `event` do we want data. Default: 3
`collide_before`	An optional vector of column names in `trial_df` that set a boundary on the earliest time point used in interpolation. This effectively truncates the time series for interpolation to a smaller window than specified by `time_before`.
`collide_after`	An optional vector of column names in `trial_df` that set a boundary on the latest time point used in interpolation. This effectively truncates the time series for interpolation to a smaller window than specified by `time_after`.
`pad_before`	Number of seconds to include in the epoch time window before the event of interest. Interpolation spans the window from `time_before` to `time_after`, but padding includes data points at the boundary that can help to have sufficient data to interpolate early and late times within the epoch.
`pad_after`	Number of seconds to include in the epoch time window after the event of interest.
`output_resolution`	the sampling frequency (in seconds) of the interpolated data. Defaults to be the same as `tr`.
`group_by`	return interpolated time series for each combination of group_by variables. Default is to provide one interpolated time series per trial.
`time_audit`	If TRUE, additional columns will be added to the output showing how alignment is calculated vis-a-vis event timing