Image_1_Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States.PDF
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.
History
References
- https://doi.org//10.1103/PhysRevE.84.061918
- https://doi.org//10.1093/cercor/bhs352
- https://doi.org//10.3389/fnsys.2011.00002
- https://doi.org//10.1162/neco.1995.7.6.1129
- https://doi.org//10.1038/nrn2575
- https://doi.org//10.1002/hbm.1048
- https://doi.org//10.1016/j.neuron.2014.10.015
- https://doi.org//10.1016/j.neuroimage.2009.12.011
- https://doi.org//10.1016/j.neuroimage.2012.03.070
- https://doi.org//10.1038/nrn2961
- https://doi.org//10.1103/PhysRevLett.94.018102
- https://doi.org//10.1016/j.neuroimage.2011.11.088
- https://doi.org//10.1002/hbm.21170
- https://doi.org//10.1371/journal.pone.0015710
- https://doi.org//10.1016/j.neuroimage.2012.01.021
- https://doi.org//10.1109/LSP.2003.821662
- https://doi.org//10.1103/PhysRevE.79.061922
- https://doi.org//10.1093/biostatistics/kxm045
- https://doi.org//10.1093/cercor/bhu012
- https://doi.org//10.1016/j.neuroimage.2013.04.127
- https://doi.org//10.1073/pnas.012579499
- https://doi.org//10.1016/j.neuroimage.2014.03.034
- https://doi.org//10.1523/JNEUROSCI.0276-13.2014
- https://doi.org//10.1016/j.neuroimage.2004.03.027
- https://doi.org//10.1098/rspa.1998.0193
- https://doi.org//10.1006/nimg.2002.1132
- https://doi.org//10.1016/j.neuroimage.2011.09.015
- https://doi.org//10.1371/journal.pcbi.1000314
- https://doi.org//10.1016/j.neuron.2014.08.016
- https://doi.org//10.1002/nav.3800020109
- https://doi.org//10.1155/2012/412512
- https://doi.org//10.1002/hbm.22599
- https://doi.org//10.1016/j.neuroimage.2013.12.063
- https://doi.org//10.1109/MSP.2013.2267931
- https://doi.org//10.1002/mrm.22361
- https://doi.org//10.1038/ncomms3521
- https://doi.org//10.1137/0105003
- https://doi.org//10.1016/j.neuroimage.2013.02.035
- https://doi.org//10.1103/PhysRevLett.73.951
- https://doi.org//10.1098/rspa.2009.0502
- https://doi.org//10.1109/TSP.2011.2106779
- https://doi.org//10.1142/S1793536913500076
- https://doi.org//10.1016/j.neuroimage.2013.11.046
- https://doi.org//10.1002/mrm.23097
- https://doi.org//10.1016/j.neuroimage.2013.05.039
- https://doi.org//10.1016/j.neuroimage.2010.08.063
- https://doi.org//10.1093/cercor/bhj127
- https://doi.org//10.3389/fphys.2012.00015
- https://doi.org//10.1016/j.neuroimage.2012.02.018
- https://doi.org//10.1142/S1793536910000549
- https://doi.org//10.1142/S1793536909000047
- https://doi.org//10.1016/j.neuroimage.2015.07.002
CC BY 4.0