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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().

Details

The phase-locking computation is accelerated by a compiled C++ backend (dyn_phase_lock_cpp()): only the upper triangle is evaluated with cos(phi_i - phi_j) and mirrored to the lower, halving trigonometric operations relative to a naive double loop.

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