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.
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
)Normalized BOLD data matrix (\(V \times T\))
fMRI numeric data matrix (\(V \times T\))
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.
"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)))).
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.
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.
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.
The temporal resolution of the data, i.e. the time between volumes,
in seconds. TR is required for detrending with hpf.