dynamic functional connectivity
using edge time series to study moment-to-moment features of brain network organization
This project focuses on dynamic functional connectivity through the lens of edge time series. Instead of summarizing functional connectivity as a single static matrix, I study how pairwise interactions between brain regions fluctuate over time and what those fluctuations reveal about network organization.
The broader goal is to understand which features of edge time series are meaningful for characterizing transient network states, high-amplitude co-fluctuation events, and the evolving structure of functional brain systems.
Some recurring themes in this work include:
- representing dynamic connectivity at the level of edges rather than only nodes
- quantifying event-like structure in fMRI time series
- comparing static and time-resolved descriptions of network organization
- interpreting how preprocessing and signal properties shape inferred dynamic structure
Relevant code and related resources:
- edge-ts: tools and examples for working with edge time series
- edge-centric demo: exploratory examples for edge-centric network analysis
- brain networks across the web: openly available brain network datasets useful for comparative and methodological work
This line of work connects closely to my broader interests in network neuroscience, mesoscale organization, and the interpretation of functional MRI signals across space and time.