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Dynamic phase-locking matrix (dPL)

Source: R/dyn_phase_lock.R
dyn_phase_lock.Rd

Compute instantaneous phase-locking matrices from parcel-level phase time series and extract leading eigenvectors via get_leida(). The first and last 10 timepoints are discarded to avoid edge effects from the Hilbert transform.

Usage

dyn_phase_lock(phases)

Arguments

phases

Numeric matrix [N × Tmax]. Instantaneous phases in radians, as returned by hilbert_phases().

Value

A list with:

sync_conn

Array [N, N, Tmax-20]. Instantaneous phase-locking matrices, one per (trimmed) timepoint.

leida

Matrix [Tmax-20, N]. Leading eigenvectors from get_leida().

References

Cabral, J. et al. (2017). Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Scientific Reports, 7(1), 5135. doi:10.1038/s41598-017-05425-7

Lord, L.-D. et al. (2019). Dynamical exploration of the repertoire of brain networks at rest is modulated by psilocybin. NeuroImage, 199, 127–142. doi:10.1016/j.neuroimage.2019.05.060

Examples

set.seed(1)
ts <- matrix(rnorm(10 * 200), nrow = 10, ncol = 200)
phases <- hilbert_phases(ts)
res <- dyn_phase_lock(phases)
dim(res$sync_conn)  # 10 x 10 x 180
#> [1]  10  10 180
dim(res$leida)      # 180 x 10
#> [1] 180  10