Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions

Overview

Journal Brain Connect

Specialty Neurology

Date 2017 Sep 7

PMID 28874061

Citations 47

Authors

Shella Keilholz

Cesar Caballero-Gaudes

Peter Bandettini

Gustavo Deco

Vince Calhoun

Affiliations

Soon will be listed here.

Abstract

Time-resolved analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data allows researchers to extract more information about brain function than traditional functional connectivity analysis, yet a number of challenges in data analysis and interpretation remain. This article briefly summarizes common methods for time-resolved analysis and presents some of the pressing issues and opportunities in the field. From there, the discussion moves to interpretation of the network dynamics observed with rs-fMRI and the role that rs-fMRI can play in elucidating the large-scale organization of brain activity.

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References

Hansen E, Battaglia D, Spiegler A, Deco G, Jirsa V . Functional connectivity dynamics: modeling the switching behavior of the resting state. Neuroimage. 2014; 105:525-35. DOI: 10.1016/j.neuroimage.2014.11.001. View

Chen J, Glover G, Greicius M, Chang C . Dissociated patterns of anti-correlations with dorsal and ventral default-mode networks at rest. Hum Brain Mapp. 2017; 38(5):2454-2465. PMC: 5385153. DOI: 10.1002/hbm.23532. View

Chang C, Liu Z, Chen M, Liu X, Duyn J . EEG correlates of time-varying BOLD functional connectivity. Neuroimage. 2013; 72:227-36. PMC: 3602157. DOI: 10.1016/j.neuroimage.2013.01.049. View

Yuan H, Zotev V, Phillips R, Drevets W, Bodurka J . Spatiotemporal dynamics of the brain at rest--exploring EEG microstates as electrophysiological signatures of BOLD resting state networks. Neuroimage. 2012; 60(4):2062-72. DOI: 10.1016/j.neuroimage.2012.02.031. View

Leonardi N, Van De Ville D . On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage. 2014; 104:430-6. DOI: 10.1016/j.neuroimage.2014.09.007. View

Betzel R, Fukushima M, He Y, Zuo X, Sporns O . Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks. Neuroimage. 2015; 127:287-297. PMC: 4755785. DOI: 10.1016/j.neuroimage.2015.12.001. View

Choe A, Nebel M, Barber A, Cohen J, Xu Y, Pekar J . Comparing test-retest reliability of dynamic functional connectivity methods. Neuroimage. 2017; 158:155-175. PMC: 5614828. DOI: 10.1016/j.neuroimage.2017.07.005. View

Miller R, Yaesoubi M, Calhoun V . Cross-Frequency rs-fMRI Network Connectivity Patterns Manifest Differently for Schizophrenia Patients and Healthy Controls. IEEE Signal Process Lett. 2016; 23(8):1076-1080. PMC: 5175483. DOI: 10.1109/LSP.2016.2585182. View

Kringelbach M, McIntosh A, Ritter P, Jirsa V, Deco G . The Rediscovery of Slowness: Exploring the Timing of Cognition. Trends Cogn Sci. 2015; 19(10):616-628. DOI: 10.1016/j.tics.2015.07.011. View

10.

Preti M, Bolton T, Van De Ville D . The dynamic functional connectome: State-of-the-art and perspectives. Neuroimage. 2016; 160:41-54. DOI: 10.1016/j.neuroimage.2016.12.061. View

11.

Chow H, Horovitz S, Carr W, Picchioni D, Coddington N, Fukunaga M . Rhythmic alternating patterns of brain activity distinguish rapid eye movement sleep from other states of consciousness. Proc Natl Acad Sci U S A. 2013; 110(25):10300-5. PMC: 3690889. DOI: 10.1073/pnas.1217691110. View

12.

Majeed W, Magnuson M, Hasenkamp W, Schwarb H, Schumacher E, Barsalou L . Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. Neuroimage. 2010; 54(2):1140-50. PMC: 2997178. DOI: 10.1016/j.neuroimage.2010.08.030. View

13.

Patel R, Van De Ville D, Bowman F . Determining significant connectivity by 4D spatiotemporal wavelet packet resampling of functional neuroimaging data. Neuroimage. 2006; 31(3):1142-55. DOI: 10.1016/j.neuroimage.2006.01.012. View

14.

Falahpour M, Thompson W, Abbott A, Jahedi A, Mulvey M, Datko M . Underconnected, But Not Broken? Dynamic Functional Connectivity MRI Shows Underconnectivity in Autism Is Linked to Increased Intra-Individual Variability Across Time. Brain Connect. 2016; 6(5):403-14. PMC: 4913487. DOI: 10.1089/brain.2015.0389. View

15.

Kiviniemi V, Vire T, Remes J, Abou Elseoud A, Starck T, Tervonen O . A sliding time-window ICA reveals spatial variability of the default mode network in time. Brain Connect. 2012; 1(4):339-47. DOI: 10.1089/brain.2011.0036. View

16.

Hindriks R, Adhikari M, Murayama Y, Ganzetti M, Mantini D, Logothetis N . Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?. Neuroimage. 2015; 127:242-256. PMC: 4758830. DOI: 10.1016/j.neuroimage.2015.11.055. View

17.

Tagliazucchi E, Balenzuela P, Fraiman D, Chialvo D . Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis. Front Physiol. 2012; 3:15. PMC: 3274757. DOI: 10.3389/fphys.2012.00015. View

18.

Ou J, Lian Z, Xie L, Li X, Wang P, Hao Y . Atomic dynamic functional interaction patterns for characterization of ADHD. Hum Brain Mapp. 2014; 35(10):5262-78. PMC: 6869011. DOI: 10.1002/hbm.22548. View

19.

Simony E, Honey C, Chen J, Lositsky O, Yeshurun Y, Wiesel A . Dynamic reconfiguration of the default mode network during narrative comprehension. Nat Commun. 2016; 7:12141. PMC: 4960303. DOI: 10.1038/ncomms12141. View

20.

Handwerker D, Roopchansingh V, Gonzalez-Castillo J, Bandettini P . Periodic changes in fMRI connectivity. Neuroimage. 2012; 63(3):1712-9. PMC: 4180175. DOI: 10.1016/j.neuroimage.2012.06.078. View