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- Title
Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States.
- Authors
Goldhacker, Markus; Tomé, Ana M.; Greenlee, Mark W.; Lang, Elmar W.
- Abstract
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 <italic>k</italic>-means. We find a scale-stability drop-off from <italic>k</italic> = 4 to <italic>k</italic> = 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.
- Subjects
BRAIN mapping; FUNCTIONAL magnetic resonance imaging; BRAIN function localization; PEARSON correlation (Statistics); ISING model
- Publication
Frontiers in Human Neuroscience, 2018, pN.PAG
- ISSN
1662-5161
- Publication type
Article
- DOI
10.3389/fnhum.2018.00253