norm_BOLD: Normalize BOLD data

In templateICAr: Estimate Brain Networks and Connectivity with ICA and Empirical Priors

Normalize BOLD data

Description

Center the data across space and/or time, detrend, and scale, in that order. For dual regression, row centering is required and column centering is not recommended. Scaling and detrending depend on the user preference.

Usage

norm_BOLD(
  BOLD,
  center_rows = TRUE,
  center_cols = FALSE,
  scale = c("global", "local", "none"),
  scale_sm_xifti = NULL,
  scale_sm_FWHM = 2,
  detrend_DCT = 0
)

Arguments

BOLD	fMRI numeric data matrix (V \times T)
center_rows, center_cols	Center BOLD data across rows (each data location's time series) or columns (each time point's image)? Default: TRUE for row centering, and FALSE for column centering.
scale	"global" (default), "local", or "none". Global scaling will divide the entire data matrix by the mean image standard deviation (mean(sqrt(rowVars(BOLD)))). Local scaling will divide each data location's time series by its estimated standard deviation.
scale_sm_xifti, scale_sm_FWHM	Only applies if scale=="local" and BOLD represents CIFTI-format data. To smooth the standard deviation estimates used for local scaling, provide a "xifti" object with data locations in alignment with BOLD, as well as the smoothing FWHM (default: 2). If no "xifti" object is provided (default), do not smooth.
detrend_DCT	Detrend the data? This is an integer number of DCT bases to use for detrending. If 0 (default), do not detrend.

Value

Normalized BOLD data matrix (V \times T)

templateICAr documentation built on Feb. 16, 2023, 8:14 p.m.