Goldhacker, Markus Tomé, Ana M. W. Greenlee, Mark W. Lang, Elmar Image_1_Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States.PDF <p>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.</p> dynamic functional connectivity;multivariate;empirical mode decomposition;filter-bank;multiscale;fMRI;resting-state;scale-invariance 2018-06-26
    https://frontiersin.figshare.com/articles/figure/Image_1_Frequency-Resolved_Dynamic_Functional_Connectivity_Reveals_Scale-Stable_Features_of_Connectivity-States_PDF/6680711
10.3389/fnhum.2018.00253.s001