This study investigates the dynamic behavior of resting-state brain connectivity using functional MRI (fMRI) and wavelet transform coherence analysis. The researchers focused on the posterior cingulate cortex (PCC), a key node in the default-mode network, and its relationship with the "anticorrelated" network, which is often referred to as the "task-positive" or "executive-control" network. They found that coherence and phase between the PCC and the anticorrelated network were variable in both time and frequency, with significant scale-dependent temporal variability. Additionally, a sliding-window correlation procedure identified other brain regions that exhibited variable connectivity with the PCC, including areas involved in attention and salience processing. The results suggest that resting-state functional connectivity is not static and that considering measures of variability, in addition to average quantities, may be valuable for characterizing resting-state networks. The study highlights the importance of dynamic analysis methods in understanding the complex dynamics of brain connectivity during rest.This study investigates the dynamic behavior of resting-state brain connectivity using functional MRI (fMRI) and wavelet transform coherence analysis. The researchers focused on the posterior cingulate cortex (PCC), a key node in the default-mode network, and its relationship with the "anticorrelated" network, which is often referred to as the "task-positive" or "executive-control" network. They found that coherence and phase between the PCC and the anticorrelated network were variable in both time and frequency, with significant scale-dependent temporal variability. Additionally, a sliding-window correlation procedure identified other brain regions that exhibited variable connectivity with the PCC, including areas involved in attention and salience processing. The results suggest that resting-state functional connectivity is not static and that considering measures of variability, in addition to average quantities, may be valuable for characterizing resting-state networks. The study highlights the importance of dynamic analysis methods in understanding the complex dynamics of brain connectivity during rest.