Bursty properties revealed in large-scale brain networks with a point-based method for dynamic functional connectivity

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Thompson, William Hedley ^a   Fransson, Peter ^a  

^a KAROLINSKA INSTITUTE   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

COVARIANCE; FUNCTIONAL CONNECTIVITY; HUMAN; INFORMATION PROCESSING; QUANTITATIVE STUDY; REST; RESTING STATE NETWORK; THEORETICAL MODEL; ANATOMY AND HISTOLOGY; BRAIN; BRAIN MAPPING; CLUSTER ANALYSIS; IMAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES;

BRAIN; BRAIN MAPPING; CLUSTER ANALYSIS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PRINCIPAL COMPONENT ANALYSIS;

EID: 85006333189     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep39156     Document Type: Article

References (47)

1. Hutchison, R. M. et al. Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage 80, 360-378 (2013).
2. Keilholz, S. D. Review Article: The Neural Basis of Time-Varying Resting State Functional Connectivity. Brain Conn. 4, 1-32 (2014).
3. Zalesky, A. & Breakspear, M. Towards a statistical test for functional connectivity dynamics. NeuroImage 114, 466-470 (2015).
4. Hindriks, R. et al. Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage 127, 242-256 (2016).
5. Thompson, W. H. & Fransson, P. On stabilizing the variance of dynamic functional brain connectivity time series. http://arxiv.org/ abs/1603.00201 (2016).
6. Kiviniemi, V. et al. A sliding time-window ICA reveals spatial variability of the default mode network in time. Brain Conn. 1, 339-47 (2011).
7. Hutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S. & Menon, R. S. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum. Brain Mapp. 34, 2154-77 (2013).
8. Keilholz, S., Magnuson, M. E., Pan, W.-J., Willis, M. & Thompson, G. Dynamic Properties of Functional Connectivity in the Rodent. Brain Conn. Connectivity 3, 121029090330004 (2012).
9. Leonardi, N. et al. Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. NeuroImage 83, 937-50 (2013).
10. Allen, E. a. et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24, 663-76 (2014).
11. Shine, J. M. et al. Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage 122, 399-407 (2015).
12. Tagliazucchi, E. et al. Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Front. Hum. Neurosci. 6, 339 (2012).
13. Liu, X. & Duyn, J. H. Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc. Natl. Acad. Sci. USA 110, 4392-4397 (2013).
14. Allan, T. W. et al. Functional Connectivity in MRI Is Driven by Spontaneous BOLD Events. Plos One 10, e0124577 (2015).
15. Smith, S. M. et al. Temporally-independent functional modes of spontaneous brain activity. Proc. Natl. Acad. Sci. USA 109, 3131-6 (2012).
16. Holme, P. & Sarameki, J. Temporal networks. Phys. Rep. 519, 97-125 (2012).
17. Nicosia, V. et al. In Temporal networks, 15-40 (Springer, 2013).
18. Barabesi, A.-L. The origin of bursts and heavy tails in human dynamics. Nature 207-211 (2005).
19. Goh, K.-I. & Barabasi, A.-L. Burstiness and Memory in Complex Systems. Europhys. Let. 81, 48002 (2006).
20. Karsai, M. et al. Small But Slow World: How Network Topology and Burstiness Slow Down Spreading. Phys. Rev. E 83, 025102 (2011).
21. Takaguchi, T., Masuda, N. & Holme, P. Bursty Communication Patterns Facilitate Spreading in a Threshold-Based Epidemic Dynamics. PloS one 8, e68629 (2013).
22. Thompson, W. H. & Fransson, P. The frequency dimension of fMRI dynamic connectivity: network connectivity, functional hubs and integration in the resting brain. NeuroImage 121, 227-242 (2015).
23. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-98 (2009).
24. Van Essen, D. C. et al. The Human Connectome Project: a data acquisition perspective. NeuroImage 62, 2222-31 (2012).
25. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142-2154 (2012).
26. Dijk, K. R. A., van, Sabuncu, M. R. & Buckner, R. L. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59, 431-438 (2012).
27. Sporns, O. Contributions and challenges for network models in cognitive neuroscience. Nat. Neurosci, doi: https://doi.org/10.1038/ nn.369010.1038/nn.3690 (2014).
28. Kopell, N. J., Gritton, H. J., Whittington, M. A. & Kramer, M. A. Beyond the connectome: The dynome. Neuron 83, 1319-1328 (2014).
29. Britz, J., Van De Ville, D. & Michel, C. M. BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage 52, 1162-70 (2010).
30. Van de Ville, D., Britz, J. & Michel, C. M. EEG microstate sequences in healthy humans at rest reveal scale-free dynamics. Proc. Natl. Acad. Sci. USA 107, 18179-84 (2010).
31. Magri, C., Schridde, U., Murayama, Y., Panzeri, S. & Logothetis, N. K. The Amplitude and Timing of the BOLD Signal Reflects the Relationship between Local Field Potential Power at Different Frequencies. J Neurosci. 32, 1395-1407 (2012).
32. Chang, C., Liu, Z., Chen, M. C., Liu, X. & Duyn, J. H. EEG correlates of time-varying BOLD functional connectivity. NeuroImage 72, 227-236 (2013).
33. Thompson, G. J. et al. Neural correlates of time-varying functional connectivity in the rat. NeuroImage 83, 826-836 (2013).
34. Thompson, G. J. et al. Phase-amplitude coupling and infraslow (<1 Hz) frequencies in the rat brain: relationship to resting state fMRI. Front Integre. Neurosci. 8, 41, 2014.
35. Freyer, F., Aquino, K., Robinson, P. a., Ritter, P. & Breakspear, M. Bistability and non-Gaussian fluctuations in spontaneous cortical activity. J. Neurosci. 29, 8512-8524 (2009)
36. Freyer, F., Roberts, J. a., Ritter, P. & Breakspear, M. A Canonical Model of Multistability and Scale-Invariance in Biological Systems. PLoS Comp. Bio. 8 (2012).
37. Roberts, J. a, Boonstra, T. W. & Breakspear, M. The heavy tail of the human brain. Curr. Npin. Neurobiol 31, 164-172 (2015).
38. Hansen, E. C. A., Battaglia, D., Spiegler, A., Deco, G. & Jirsa, V. K. Functional connectivity dynamics: Modeling the switching behavior of the resting state. NeuroImage 105, 525-535 (2015).
39. Thompson, W. H. & Fransson, P. The mean-variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI. Front. Hum. Neurosci. 9, 1-7 (2015).
40. Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. NeuroImage 80, 144-68 (2013).
41. Ugurbil, K. et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. NeuroImage 80, 80-104 (2013).
42. Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105-24 (2013).
43. Griffanti, L. et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95, 232-47 (2014).
44. Salimi-Khorshidi, G. et al. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449-68 (2014).
45. Power, J. D. et al. Functional Network Organization of the Human Brain. Neuron 72, 665-678 (2011).
46. Cole, M. W. et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16, 1348-55 (2013).
47. Leonardi, N. & Van De Ville, D. On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage 104, 430-436 (2015).