Disambiguating Brain Functional Connectivity

Overview

Journal Neuroimage

Specialty Radiology

Date 2018 Feb 25

PMID 29476911

Citations 43

Authors

Eugene P Duff

Tamar Makin

Michiel Cottaar

Stephen M Smith

Mark W Woolrich

Affiliations

Soon will be listed here.

Abstract

Functional connectivity (FC) analyses of correlations of neural activity are used extensively in neuroimaging and electrophysiology to gain insights into neural interactions. However, analyses assessing changes in correlation fail to distinguish effects produced by sources as different as changes in neural signal amplitudes or noise levels. This ambiguity substantially diminishes the value of FC for inferring system properties and clinical states. Network modelling approaches may avoid ambiguities, but require specific assumptions. We present an enhancement to FC analysis with improved specificity of inferences, minimal assumptions and no reduction in flexibility. The Additive Signal Change (ASC) approach characterizes FC changes into certain prevalent classes of signal change that involve the input of additional signal to existing activity. With FMRI data, the approach reveals a rich diversity of signal changes underlying measured changes in FC, suggesting that it could clarify our current understanding of FC changes in many contexts. The ASC method can also be used to disambiguate other measures of dependency, such as regression and coherence, providing a flexible tool for the analysis of neural data.

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References

McKeown M, Sejnowski T . Independent component analysis of fMRI data: examining the assumptions. Hum Brain Mapp. 1998; 6(5-6):368-72. PMC: 6873375. View

Feinberg D, Moeller S, Smith S, Auerbach E, Ramanna S, Gunther M . Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One. 2010; 5(12):e15710. PMC: 3004955. DOI: 10.1371/journal.pone.0015710. View

Lindquist M, Xu Y, Nebel M, Caffo B . Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. Neuroimage. 2014; 101:531-46. PMC: 4165690. DOI: 10.1016/j.neuroimage.2014.06.052. View

Duff E, Johnston L, Xiong J, Fox P, Mareels I, Egan G . The power of spectral density analysis for mapping endogenous BOLD signal fluctuations. Hum Brain Mapp. 2008; 29(7):778-90. PMC: 5441229. DOI: 10.1002/hbm.20601. View

Smith S, Miller K, Salimi-Khorshidi G, Webster M, Beckmann C, Nichols T . Network modelling methods for FMRI. Neuroimage. 2010; 54(2):875-91. DOI: 10.1016/j.neuroimage.2010.08.063. View

Kasess C, Stephan K, Weissenbacher A, Pezawas L, Moser E, Windischberger C . Multi-subject analyses with dynamic causal modeling. Neuroimage. 2009; 49(4):3065-74. PMC: 2837922. DOI: 10.1016/j.neuroimage.2009.11.037. View

Richiardi J, Eryilmaz H, Schwartz S, Vuilleumier P, Van De Ville D . Decoding brain states from fMRI connectivity graphs. Neuroimage. 2010; 56(2):616-26. DOI: 10.1016/j.neuroimage.2010.05.081. View

Stephan K, Harrison L, Kiebel S, David O, Penny W, Friston K . Dynamic causal models of neural system dynamics:current state and future extensions. J Biosci. 2007; 32(1):129-44. PMC: 2636905. DOI: 10.1007/s12038-007-0012-5. View

Zalesky A, Fornito A, Cocchi L, Gollo L, Breakspear M . Time-resolved resting-state brain networks. Proc Natl Acad Sci U S A. 2014; 111(28):10341-6. PMC: 4104861. DOI: 10.1073/pnas.1400181111. View

10.

Marrelec G, Krainik A, Duffau H, Pelegrini-Issac M, Lehericy S, Doyon J . Partial correlation for functional brain interactivity investigation in functional MRI. Neuroimage. 2006; 32(1):228-37. DOI: 10.1016/j.neuroimage.2005.12.057. View

11.

Buchel C, Friston K . Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb Cortex. 1998; 7(8):768-78. DOI: 10.1093/cercor/7.8.768. View

12.

Fukunaga M, Horovitz S, de Zwart J, van Gelderen P, Balkin T, Braun A . Metabolic origin of BOLD signal fluctuations in the absence of stimuli. J Cereb Blood Flow Metab. 2008; 28(7):1377-87. DOI: 10.1038/jcbfm.2008.25. View

13.

van den Heuvel M, Hulshoff Pol H . Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 2010; 20(8):519-34. DOI: 10.1016/j.euroneuro.2010.03.008. View

14.

Vidaurre D, Smith S, Woolrich M . Brain network dynamics are hierarchically organized in time. Proc Natl Acad Sci U S A. 2017; 114(48):12827-12832. PMC: 5715736. DOI: 10.1073/pnas.1705120114. View

15.

Baker A, Brookes M, Rezek I, Smith S, Behrens T, Probert Smith P . Fast transient networks in spontaneous human brain activity. Elife. 2014; 3:e01867. PMC: 3965210. DOI: 10.7554/eLife.01867. View

16.

Gramfort A, Luessi M, Larson E, Engemann D, Strohmeier D, Brodbeck C . MNE software for processing MEG and EEG data. Neuroimage. 2013; 86:446-60. PMC: 3930851. DOI: 10.1016/j.neuroimage.2013.10.027. View

17.

Smith S, Nichols T, Vidaurre D, Winkler A, Behrens T, Glasser M . A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci. 2015; 18(11):1565-7. PMC: 4625579. DOI: 10.1038/nn.4125. View

18.

Zou Q, Zhu C, Yang Y, Zuo X, Long X, Cao Q . An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods. 2008; 172(1):137-41. PMC: 3902859. DOI: 10.1016/j.jneumeth.2008.04.012. View

19.

Eavani H, Satterthwaite T, Gur R, Gur R, Davatzikos C . Unsupervised learning of functional network dynamics in resting state fMRI. Inf Process Med Imaging. 2014; 23:426-37. PMC: 3974209. DOI: 10.1007/978-3-642-38868-2_36. View

20.

Colclough G, Smith S, Nichols T, Winkler A, Sotiropoulos S, Glasser M . The heritability of multi-modal connectivity in human brain activity. Elife. 2017; 6. PMC: 5621837. DOI: 10.7554/eLife.20178. View