dual_reg_parc {fMRItools}

Multiple regression for parcel data

Description

Multiple regression for parcel data

Usage

dual_reg_parc(
  BOLD,
  parc,
  parc_vals,
  scale = c("local", "global", "none"),
  scale_sm_xifti = NULL,
  scale_sm_FWHM = 2,
  TR = NULL,
  hpf = 0.01,
  GSR = FALSE
)

Arguments

BOLD

Subject-level fMRI data matrix (V \times T). Rows will be centered.

parc

The parcellation as an integer vector.

parc_vals

The parcel values (keys) in desired order, e.g. sort(unique(parc)).

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.

TR

The temporal resolution of the data, i.e. the time between volumes, in seconds. TR is required for detrending with hpf.

hpf

The frequency at which to apply a highpass filter to the data during pre-processing, in Hertz. Default: 0.01 Hertz. Set to 0 to disable the highpass filter.

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.

GSR

Center BOLD across columns (each image)? This is equivalent to performing global signal regression. Default: FALSE.

Value

A list containing the subject-level independent components S (Q \times V), and subject-level mixing matrix A (TxQ).