scrub_time_series: "Scrubs" Bad Time Points from Time Series Matrix

In abnormally-distributed/rsfcNet: Analyze resting state functional connectivity networks

Description

This function takes a list of time series files and a list of confound matrices and returns the scrubbed time series files. The default method is "censor". Censoring takes the preceding time point and the following two time points of each bad time point in the confound matrices and deletes them as described in Power et al, 2012. If there are relatively few bad time points in the subjects method="interpolate" may be tried.

Usage

scrub_time_series(
  ts_file_bulk,
  confound_file_bulk,
  method = "censor",
  n = NULL,
  n.nodes = NULL
)

Arguments

ts_file_bulk	list of time series matrices.
confound_file_bulk	list of confound files for each subject
method	method="censor" uses censoring/scrubbing while interpolation fills in missing time points column-wise with smooth polynomials.
n	The The number of subjects in the list.
n.nodes	The The number of nodes in the network.

Value

The time series matrix for each subject.

Author(s)

Brandon Vaughan

References

Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3), 2142–2154. http://doi.org/10.1016/j.neuroimage.2011.10.018

Moritz S, Bartz-Beielstein T (2017). “imputeTS: Time Series Missing Value Imputation in R.” The R Journal, 9(1), 207–218. https://journal.r-project.org/archive/2017/RJ-2017-009/index.html

Examples

scrubbed_ts = scrub_time_series(ts, confounds)

abnormally-distributed/rsfcNet documentation built on March 8, 2020, 5:32 p.m.