norm_BOLD: Normalize BOLD data

In BayesBrainMap: Estimate Brain Networks and Connectivity with Population-Derived 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("local", "global", "none"),
  scale_sm_xifti = NULL,
  scale_sm_FWHM = 2,
  scale_sm_xifti_mask = NULL,
  hpf = 0,
  TR = NULL
)

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	"local" (default), "global", or "none". Local scaling will divide each data location's time series by its estimated standard deviation. Global scaling will divide the entire data matrix by the mean image standard deviation (mean(sqrt(rowVars(BOLD)))).
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.
scale_sm_xifti_mask	For local scaling with smoothing, the data must be unmasked to be mapped back to the surface. So if the data are masked, provide the mask here.
hpf	The frequency at which to apply a highpass filter to the data during pre-processing, in Hertz. Default: 0 Hz (disabled). If the data has not already been highpass filtered, a recommended filter value is .01 Hz. The highpass filter serves to detrend the data, since low-frequency variance is associated with noise. Highpass filtering is accomplished by nuisance regression of discrete cosine transform (DCT) bases. Note the TR argument is required for highpass filtering. If TR is not provided, hpf will be ignored.
TR	The temporal resolution of the data, i.e. the time between volumes, in seconds. TR is required for detrending with hpf.

Value

Normalized BOLD data matrix (V \times T)

BayesBrainMap documentation built on Aug. 8, 2025, 7:25 p.m.