Compute functional connectivity from parcellated BOLD timeseries

Estimates a ROI-to-ROI functional connectivity matrix from a parcellated BOLD timeseries using Pearson correlation (default) or partial correlation. Returns both the full symmetric matrix and a tidy long-format data frame.

Usage

compute_fc(parcellated, method = c("pearson", "partial"), fisher_z = TRUE)

Arguments

parcellated

A boldr_parcellated object from parcellate().

method

Character. Connectivity metric. One of:

"pearson": (Default) Pearson product-moment correlation.
"partial": Partial correlation (requires corpcor in Suggests).

fisher_z

Logical. Whether to apply Fisher r-to-z transformation before returning values. Default TRUE. Diagonal is set to NA.

Value

A list of class boldr_fc with components:

matrix: Numeric matrix (n_rois × n_rois). Symmetric FC matrix with NA on the diagonal.
long: Data frame (long format). Columns: roi_i, roi_j, roi_name_i, roi_name_j, fc.
method: Character. Connectivity method used.
fisher_z: Logical. Whether Fisher z was applied.
atlas_name: Character. Atlas identifier.

Examples

if (FALSE) { # \dontrun{
parc <- parcellate(cleaned, atlas)
fc   <- compute_fc(parc)
dim(fc$matrix)
head(fc$long)
} # }