LEiDA pipeline

Convenience wrapper running the full phase-based dynamic FC pipeline: optional Butterworth bandpass filter -> Hilbert transform -> dynamic phase-locking matrices + LEiDA eigenvectors -> Kuramoto order parameter. Returns a structured dynR_leida object with print() and plot() methods.

Usage

leida_pipeline(
  timeseries,
  flp = NULL,
  fhi = NULL,
  delt = NULL,
  order = 2L,
  filter = TRUE
)

Arguments

timeseries

Numeric matrix [N x Tmax]. Rows = channels/parcels, columns = timepoints.

flp

Numeric. Low-pass cutoff frequency (Hz). No default — must be set explicitly when filter = TRUE.

fhi

Numeric. High-pass cutoff frequency (Hz). No default — must be set explicitly when filter = TRUE.

delt

Numeric. Sampling interval in seconds (1 / sampling_rate). No default — must be set explicitly when filter = TRUE.

order

Integer. Butterworth filter order. Default 2L.

filter

Logical. Apply bandpass filter before phase extraction? Default TRUE. Set to FALSE if the timeseries is already filtered.

Value

An object of class dynR_leida (a named list) with elements:

leida

Matrix [Tmax-20, N]. LEiDA eigenvectors.

sync_conn

Array [N, N, Tmax-20]. Instantaneous phase-locking matrices.

phases

Matrix [N, Tmax]. Instantaneous phases in radians.

synchrony

Numeric vector [Tmax-20]. Kuramoto R(t).

metastability

Numeric. Standard deviation of synchrony.

entropy

Numeric. Shannon entropy of synchrony series (bits).

N

Integer. Number of channels/parcels.

Tmax

Integer. Number of timepoints.

Details

dynR is modality-agnostic. flp, fhi, and delt have no defaults and must be supplied when filter = TRUE. Set them for your recording modality (see examples). Pass filter = FALSE if the timeseries is already band-limited.

See also

sw_pipeline(), dyn_phase_lock(), kuramoto(), plot.dynR_leida()

Examples

set.seed(1)
ts <- matrix(rnorm(10 * 200), nrow = 10)

# filter = FALSE: timeseries already band-limited
res <- leida_pipeline(ts, filter = FALSE)
res
#> <dynR_leida>
#>   Parcels:       10 
#>   Timepoints:    200 
#>   LEiDA frames:  180 
#>   Metastability: 0.1427 
#>   Entropy:       6.4623 bits

# fMRI BOLD (TR = 2 s)
if (FALSE) { # \dontrun{
res <- leida_pipeline(ts, flp = 0.01, fhi = 0.1, delt = 2)
} # }

# EEG alpha band (250 Hz)
if (FALSE) { # \dontrun{
res <- leida_pipeline(ts, flp = 8, fhi = 12, delt = 1 / 250)
} # }