Leading Eigenvector Dynamics Analysis (Cabral et al. 2017): at each time point the instantaneous phase-coherence matrix is reduced to its leading eigenvector; these are clustered over time into a small set of recurrent connectivity states. Complements state-resolved network analysis by finding the states data-drivenly rather than from external labels.
Arguments
- x
A numeric matrix, time (rows) by channels (columns).
- sr
Sampling rate in Hz.
- freq_band
Numeric
c(low, high)band in Hz.- n_states
Number of clusters/states (default 4).
- order
Butterworth filter order (default 4).
- nstart
k-means restarts (default 10).
- seed
Optional integer seed for reproducible clustering.
Value
An object of class "leida": a list with states (per-time cluster
labels), centroids (states x channels leading-eigenvector centroids),
occupancy (fraction of time in each state), dwell (mean run length in
samples per state), and n_states.
References
Cabral J, et al. (2017). Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Sci Rep 7:5135.
Examples
set.seed(1); sr <- 100; n <- 2000; t <- seq_len(n) / sr
# two regimes: channels 1-2 coherent first half, 3-4 coherent second half
base <- sin(2 * pi * 10 * t)
X <- cbind(base + rnorm(n, sd = .3), base + rnorm(n, sd = .3),
sin(2 * pi * 10 * t + 1) + rnorm(n, sd = .3),
sin(2 * pi * 10 * t + 1) + rnorm(n, sd = .3))
res <- leidaStates(X, sr = sr, freq_band = c(8, 12), n_states = 2, seed = 1)
res$occupancy
#> [1] 0.43 0.57