Edge-centric cofluctuation analysis

Compute edge time series and root-sum-square (RSS) cofluctuations from z-standardised BOLD signal. Implements the edge-centric FC framework of Esfahlani et al. (2020) and Faskowitz et al. (2020).

Usage

cofluct(timeseries, k = 1)

Arguments

timeseries

Numeric matrix [N × Tmax]. BOLD signal with N parcels as rows and Tmax timepoints as columns.

k

Integer. Upper-triangle offset. k = 1 (default) excludes the diagonal; k = 0 includes it.

Value

A list with:

edge_ts

Numeric matrix [n_edges × Tmax]. Edge time series, one row per unique parcel pair.

rss

Numeric vector [Tmax]. Root-sum-square cofluctuation at each timepoint.

References

Esfahlani, F. Z. et al. (2020). High-amplitude cofluctuations in cortical activity drive functional connectivity. PNAS, 117(45), 28393–28401. doi:10.1073/pnas.2005531117

Faskowitz, J. et al. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature Neuroscience, 23(12), 1644–1654. doi:10.1038/s41593-020-00719-y

Examples

set.seed(1)
ts <- matrix(rnorm(10 * 200), nrow = 10, ncol = 200)
res <- cofluct(ts)
dim(res$edge_ts)  # n_edges x 200
#> [1]  45 200