An R port of the Python dynfc library for computing dynamic connectivity (dynFC) representations from multivariate neurophysiological timeseries — BOLD fMRI, EEG, LFP, and related signals.
📖 What is dynR?
dynR computes dynFC representations from preprocessed multivariate timeseries, providing the upstream computation layer for dynamic connectivity analysis. It is a full R port of the Python dynfc library, motivated by reproducibility: R + renv provides a more stable long-term environment than Python dependency chains for research pipelines.
Although the bundled example data and several vignettes use BOLD fMRI, all methods are applicable to any band-limited neurophysiological signal where phase relationships or pairwise correlations carry meaningful information — including EEG, LFP, and MEG.
The outputs of dynR feed directly into stateR for brain state quantification (fractional occupancy, dwell time, Markov transitions).
🔁 Pipeline position
Multivariate timeseries [N × Tmax]
│
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dynR ← this package
(dynFC representations)
│
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stateR
(brain state metrics: FO, dwell time, Markov transitions)✨ Features
Phase-based methods
| Function | Description |
|---|---|
hilbert_phases() |
Instantaneous phase extraction via the analytic signal |
dyn_phase_lock() |
Dynamic phase-locking matrices (dPL) + LEiDA eigenvectors |
get_leida() |
Leading eigenvector decomposition (LEiDA) |
kuramoto() |
Kuramoto order parameter, metastability, Shannon entropy |
Correlation-based methods
| Function | Description |
|---|---|
corr_slide() |
Sliding-window Pearson correlation matrices |
cofluct() |
Edge-centric cofluctuation time series + RSS |
corr_corr() |
Correlation-of-correlations (FC recurrence) matrix |
Utilities
| Function | Description |
|---|---|
bandpass_filter() |
Zero-phase Butterworth bandpass filter |
shannon_entropy() |
Shannon entropy with optional bit-depth discretisation |
do_euclid() |
Euclidean distance between consecutive trajectory points |
📦 Quick example
library(dynR)
# Simulated timeseries: 80 channels, 300 timepoints
set.seed(42)
ts <- matrix(rnorm(80 * 300), nrow = 80, ncol = 300)
# 1. Bandpass filter (e.g. fMRI: TR = 2 s, 0.01–0.1 Hz)
ts_filt <- apply(ts, 1, bandpass_filter, flp = 0.01, fhi = 0.1, delt = 2)
ts_filt <- t(ts_filt)
# 2. Phase-based: LEiDA + Kuramoto
phases <- hilbert_phases(ts_filt)
dpl <- dyn_phase_lock(phases) # dpl$leida: [280 × 80] LEiDA eigenvectors
kop <- kuramoto(phases) # kop$metastability, kop$entropy
# 3. Cluster LEiDA vectors → feed into stateR
km <- kmeans(dpl$leida, centers = 5, nstart = 100)
# 4. Correlation-based: sliding-window FC + cofluctuations
sw <- corr_slide(ts_filt, window = 30, step = 5)
ec <- cofluct(ts_filt) # ec$edge_ts, ec$rss📐 Data conventions
All timeseries inputs follow:
rows = channels/parcels (N), columns = timepoints (Tmax)
This matches the [N, Tmax] convention of the original Python dynfc package.
👥 Authors
| Role | Name | Affiliation |
|---|---|---|
| Author, maintainer | Lucas G. S. França | Northumbria University / Circadia Lab |
| Author | Mario Leocadio-Miguel | Northumbria University / Circadia Lab |
| Author | Dafnis Batallé | King’s College London / CoDe-Neuro Lab |
🤝 Related tools
Circadia Lab ecosystem: - 🕐 zeitR — wrist actigraphy analysis - 😴 hypnoR — hypnogram handling and sleep architecture - 🔗 syncR — ecosystem integrator
CoDe-Neuro ecosystem: - 🧪 stateR — brain state metrics (FO, dwell time, Markov) — consumes dynR output - 🔬 ptestR — permutation-based significance testing