Spontaneous physiological variability modulates dynamic functional connectivity in resting-state functional magnetic resonance imaging

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Volumn 374, Issue 2067, 2016, Pages

  Nikolaou, F ^a   Orphanidou, C ^a   Papakyriakou, P ^a   Murphy, K ^b   Wise, R G ^b   Mitsis, G D ^a,c  

^a UNIVERSITY OF CYPRUS   (Cyprus)

^b CARDIFF UNIVERSITY   (United Kingdom)

^c MCGILL UNIVERSITY   (Canada)

Author keywords

Blood oxygen level dependent signal; End tidal CO2; Heart rate variability; Wavelets

Indexed keywords

BLOOD; BRAIN; CARBON DIOXIDE; FREQUENCY BANDS; HEART; MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; SIGNAL SYSTEMS; WAVELET DECOMPOSITION;

BLOOD OXYGEN LEVEL DEPENDENTS; DISCRETE WAVELET DECOMPOSITION; END TIDAL; FUNCTIONAL CONNECTIVITY PATTERNS; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HEART RATE VARIABILITY; RESTING-STATE FUNCTIONAL MAGNETIC RESONANCE IMAGING; WAVELETS;

FUNCTIONAL NEUROIMAGING;

MAGNETIC RESONANCE ANGIOGRAPHY;

MAGNETIC RESONANCE ANGIOGRAPHY;

EID: 84962643921     PISSN: 1364503X     EISSN: None     Source Type: Journal    
DOI: 10.1098/rsta.2015.0183     Document Type: Article

References (43)

1. Rogers BP, Morgan VL, Newton AT, Gore JC. 2007 Assessing functional connectivity in the human brain by fMRI. Magn. Reson. Imaging 25, 1347-1357. (doi: 10.1016/j.mri.2007.03.007)
2. Friston KJ. 2011 Functional and effective connectivity: A review. Brain Connect. 1, 13-36. (doi: 10.1089/brain.2011.0008)
3. Fox MD, Raichle ME. 2007 Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700-711. (doi: 10.1038/nrn2201)
4. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995 Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537-541. (doi: 10.1002/mrm.1910340409)
5. Damoiseaux JS, Rombouts SARB, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF. 2006 Consistent resting-state networks across healthy subjects. Proc. Natl Acad. Sci. USA 103, 13 848-13 853. (doi: 10.1073/pnas.0601417103)
6. De Luca M, Beckmann CF, De Stefano N, Matthews PM, Smith SM. 2006 fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage 29, 1359-1367. (doi: 10.1016/j.neuroimage.2005.08.035)
7. Smit DJA, Stam CJ, Posthuma D, Boomsma DI, de Geus EJC. 2008 Heritability of 'small-world' networks in the brain: A graph theoretical analysis of resting-state EEG functional connectivity. Hum. Brain Mapp. 29, 1368-1378. (doi: 10.1002/hbm.20468)
8. de Pasquale F et al. 2010 Temporal dynamics of spontaneous MEG activity in brain networks. Proc. Natl Acad. Sci. USA 107, 6040-6045. (doi: 10.1073/pnas.0913863107)
9. Greicius MD, Krasnow B, Reiss AL, Menon V. 2003 Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proc. Natl Acad. Sci. USA 100, 253-258. (doi: 10.1073/pnas.0135058100)
10. Honey CJ et al. 2009 Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl Acad. Sci. USA 106, 2035-2040. (doi: 10.1073/pnas.0811168106)
11. Chang C, Glover GH. 2010 Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50, 81-98. (doi: 10.1016/j.neuroimage.2009.12.011)
12. Hutchison RM et al. 2013 Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage 80, 360-378. (doi: 10.1016/j.neuroimage.2013.05.079)
13. Chang C, Liu Z, Chen MC, Liu X, Duyn JH. 2013 EEG correlates of time-varying BOLD functional connectivity. Neuroimage 72, 227-236. (doi: 10.1016/j.neuroimage.2013.01.049)
14. Kiviniemi V, Vire T, Remes J, Elseoud AA, Starck T, Tervonen O, Nikkinen J. 2011 A sliding time-window ICA reveals spatial variability of the default mode network in time. Brain Connect. 1, 339-347. (doi: 10.1089/brain.2011.0036)
15. Zalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M. 2014 Time-resolved resting-state brain networks. Proc. Natl Acad. Sci. USA 111, 10 341-10 346. (doi: 10.1073/pnas.1400181111)
16. Buxton RB. 2012 Dynamic models of BOLD contrast. Neuroimage 62, 953-961. (doi: 10.1016/j.neuroimage.2012.01.012)
17. Wise RG, Ide K, Poulin MJ, Tracey I. 2004 Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. Neuroimage 21, 1652-1664. (doi: 10.1016/j.neuroimage.2003.11.025)
18. Napadow V, Dhond R, Conti G, Makris N, Brown EN, Barbieri R. 2008 Brain correlates of autonomic modulation: Combining heart rate variability with fMRI. Neuroimage 42, 169-177. (doi: 10.1016/j.neuroimage.2008.04.238)
19. Mitsis GD, Poulin MJ, Robbins PA, Marmarelis VZ. 2004 Nonlinear modeling of the dynamic effects of arterial pressure and CO2 variations on cerebral blood flow in healthy humans. IEEE Trans. Biomed. Eng. 51, 1932-1943. (doi: 10.1109/TBME.2004.834272)
20. Pattinson KTS, Mitsis GD, Harvey AK, Jbabdi S, Dirckx S, Mayhew SD, Rogers R, Tracey I, Wise RG. 2009 Determination of the human brainstem respiratory control network and its cortical connections in vivo using functional and structural imaging. Neuroimage 44, 295-305. (doi: 10.1016/j.neuroimage.2008.09.007)
21. Birn RM, Diamond JB, Smith MA, Bandettini PA. 2006 Separating respiratory-variationrelated fluctuations from neuronal-activity-related fluctuations in fMRI. Neuroimage 31, 1536-1548. (doi: 10.1016/j.neuroimage.2006.02.048)
22. Murphy K, Birn RM, Bandettini PA. 2013 Resting-state fMRI confounds and cleanup. Neuroimage 80, 349-359. (doi: 10.1016/j.neuroimage.2013.04.001)
23. Birn RM. 2012 The role of physiological noise in resting-state functional connectivity. Neuroimage 62, 864-870. (doi: 10.1016/j.neuroimage.2012.01.016)
24. Orphanidou C, Bonnici T, Charlton P, Clifton D, Vallance D, Tarassenko L. 2015 Signal-quality indices for the electrocardiogram and photoplethysmogram: Derivation and applications to wireless monitoring. IEEE J. Biomed. Heal. Informatics 19, 832-838.
25. Hamilton PS, Tompkins WJ. 1986 Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. 33, 1157-1165. (doi: 10.1109/TBME. 1986.325695)
26. Smith SM et al. 2004 Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl. 1), S208-S219. (doi: 10.1016/j.neuroimage. 2004.07.051)
27. Oppenheim AV, Schafer RW. 2009 Discrete-time signal processing, 3rd edn. Prentice-Hall Signal Processing Series. Englewood Cliffs, NJ: Prentice-Hall.
28. Rubinov M, Sporns O. 2010 Complex network measures of brain connectivity: Uses and interpretations. Neuroimage 52, 1059-1069. (doi: 10.1016/j.neuroimage.2009.10.003)
29. Ville DE, Blu T, UnserM. 2006 Surfing the brain: An overview of wavelet-based techniques for fMRI data analysis. IEEE Engi. Med. Biol. Mag. 25, 65-78.
30. Daubechies I. 1992 Ten lectures on wavelets. In CBMS-NSF regional conference series in applied mathematics, vol. 62. Philadelphia, PA: SIAM.
31. Hommel G. 1988 A stagewise rejective multiple test procedure based on amodified bonferroni test. Biometrika 75, 383-386. (doi: 10.1093/biomet/75.2.383)
32. Liang PJ, Pandit J, Robbins PA. 1996 Statistical properties of breath-to-breath variations in ventilation at constant PETCO2 and PETO2 in humans. J. Appl. Physiol. 81, 2274-2286.
33. Jones DT et al. 2012 Non-stationarity in the 'resting brain's' modular architecture. PLoS ONE 7, e39731. (doi: 10.1371/journal.pone.0039731)
34. Van Dijk KRA, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL. 2010 Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization. J. Neurophysiol. 103, 297-321. (doi: 10.1152/jn.00783.2009)
35. Hutchison RM, Womelsdorf T, Gati JS, Everling S, Menon RS. 2013 Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum. Brain Mapp. 34, 2154-2177. (doi: 10.1002/hbm.22058)
36. Theiler J. Testing for nonlinearity in time series: The method of surrogate data. Physica D 58, 85-77. (doi: 10.1016/0167-2789(92)90102-s)
37. Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF, Nichols TE, Ramsey JD, Woolrich MW. 2011 Network Modelling Methods for FMRI. Neuroimage 54, 875-891. (doi: 10.1016/j.neuroimage.2010.08.063)
38. Wu G-R, Liao W, Stramaglia S, Ding J-R, Chen H, Marinazzo D. 2013 A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med. Image Anal. 17, 365-374. (doi: 10.1016/j.media.2013.01.003)
39. Deshpande G, Sathian K, Hu X. 2010 Effect of hemodynamic variability on Granger causality analysis of fMRI. Neuroimage 52, 884-896. (doi: 10.1016/j.neuroimage.2009.11.060)
40. Mitsis GD, Iannetti GD, Smart TS, Tracey I, Wise RG. 2008 Regions of interest analysis in pharmacological fMRI: How do the definition criteria influence the inferred result? Neuroimage 40, 121-132. (doi: 10.1016/j.neuroimage.2007.11.026)
41. Glover GH, Li TQ, Ress D. 2000 Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162-167. (doi: 10. 1002/1522-2594(200007)44: 13.0.CO;2-E)
42. Billman GE. 2011 Heart rate variability-A historical perspective. Front. Physiol. 2, 1-13. (doi: 10.3389/fphys.2011.00086)
43. Barbieri R, Matten EC, Alabi A, Brown EN. 2005 A point-process model of human heartbeat intervals: New definitions of heart rate and heart rate variability. Am. J. Physiol. Heart Circ. Physiol. 288, H424-H435. (doi: 10.1152/ajpheart.00482.2003)