TY - JOUR AU - Goldhacker, Markus AU - Tomé, Ana M. AU - Greenlee, Mark W. AU - Lang, Elmar W. PY - 2018 M3 - Methods TI - Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States JO - Frontiers in Human Neuroscience UR - https://www.frontiersin.org/articles/10.3389/fnhum.2018.00253 VL - 12 SN - 1662-5161 N2 - Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided. ER -