Abstract
Functional connectivity (FC) is often analyzed assuming a condition of stationarity. While this is a useful approach to characterize global characteristics of brain activity, connectivity of the brain is intrinsically dynamic, that is, nonstationary. This chapter will summarize the typical techniques used to infer dynamic FC from resting-state functional MRI, including sliding window, cofluctuations, and phase synchrony. It reviews measures and techniques used to characterize global brain dynamics with measures, such as mean synchrony and metastability. It also discusses dimensionality reduction and clustering of temporally resolved connectivity in macrostates, the analysis of state transitions, and clinical applications and cases of use. Finally, this chapter will discuss potential limitations of the technique, and provide a list of recommendations.
| Original language | English |
|---|---|
| Title of host publication | Connectome Analysis |
| Subtitle of host publication | Characterization, Methods, and Analysis |
| Editors | Markus D. Schirmer, Tomoki Arichi, Ai Wernq Chung |
| Place of Publication | Oxford |
| Publisher | Elsevier |
| Chapter | 10 |
| Pages | 219-245 |
| Number of pages | 27 |
| Edition | 1st |
| ISBN (Electronic) | 9780323852814 |
| ISBN (Print) | 9780323852807 |
| DOIs | |
| Publication status | Published - 30 Jun 2023 |
Keywords
- Brain dynamics
- brain states
- cofluctuation
- cooscillation
- fMRI
- metastability
- phase synchrony
- time-resolved connectivity
