Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state

NeuroImage

Volumn 86, Issue , 2014, Pages 343-353

  Carbonell, F ^a,b   Bellec, P ^c   Shmuel, A ^a,b  

^a MCGILL UNIVERSITY   (Canada)

^b MCGILL UNIVERSITY   (Canada)

^c CENTRE DE RECHERCHE   (Canada)

Author keywords

Default Mode Network; Dorsal attention network; Functional connectivity; General Linear Model; Global average signal; Resting state; Task positive network

Indexed keywords

ADULT; ARTICLE; ARTIFICIAL NEGATIVE CORRELATION INDEX; ARTIFICIAL NEURAL NETWORK; BRAIN FUNCTION; CONCEPTUAL FRAMEWORK; CONFOUNDING VARIABLE; CORRELATION COEFFICIENT; DEFAULT MODE NETWORK; DORSAL ATTENTION NETWORK; FUNCTIONAL ASSESSMENT; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GLOBAL AVERAGE SIGNAL; HEAD MOVEMENT; HUMAN; HUMAN EXPERIMENT; IGAFC INDEX; NERVOUS SYSTEM PARAMETERS; NEUROPHYSIOLOGY; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE SCANNER; POST HOC ANALYSIS; PRIORITY JOURNAL; REGRESSION ANALYSIS; RESTING STATE NETWORK; SENSITIVITY ANALYSIS; YOUNG ADULT; CORRELATION ANALYSIS; IMPACT OF THE GLOBAL AVERAGE ON FUNCTIONAL CONNECTIVITY; SIGNAL PROCESSING; STATISTICAL ANALYSIS;

DEFAULT MODE NETWORK; DORSAL ATTENTION NETWORK; FUNCTIONAL CONNECTIVITY; GENERAL LINEAR MODEL; GLOBAL AVERAGE SIGNAL; RESTING STATE; TASK POSITIVE NETWORK;

ALGORITHMS; ATTENTION; BRAIN; BRAIN MAPPING; CONFOUNDING FACTORS (EPIDEMIOLOGY); CONNECTOME; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; NERVE NET; REPRODUCIBILITY OF RESULTS; REST; SENSITIVITY AND SPECIFICITY; STATISTICS AS TOPIC; YOUNG ADULT;

EID: 84891625443     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2013.10.013     Document Type: Article

References (33)

1. Anderson J.S., Druzgal T.J., Lopez-Larson M., Jeong E.K., Desai K., Yurgelun-Todd D. Network anticorrelations, global regression, and phase-shifted soft tissue correction. Hum. Brain Mapp. 2011, 32:919-934.
2. Beall E., Lowe M. Isolating physiologic noise sources with independently determined spatial measures. NeuroImage 2007, 37(4):1286-1300.
3. Benjamini Y., Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 2001, 29(4):1165-1188.
4. Bianciardi M., Fukunaga M., van Gelderen P.O., Horovitz S.G., de Zwart J.A., Shmueli K., Duyn J.H. Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: A 7 T study. Magn. Reson. Imaging 2009, 27:1019-1029.
5. Birn R., Diamond J., Smith M., Bandettini P. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage 2006, 31(4):1536-1548.
6. Carbonell F., Bellec P., Shmuel A. Global and system-specific resting-state BOLD fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks. Brain Connect. 2011, 1(6):496-510.
7. Chai X., Castañón A.N., Öngür D., Whitfield-Gabrieli S. Anticorrelations in resting state networks without global signal regression. NeuroImage 2012, 59:1420-1428.
8. Chang C., Glover G. Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. NeuroImage 2009, 47(4):1448-1459.
9. Cohen J., Cohen P. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences 1983, Lawrence Erlbaum, Hillsdale, NJ.
10. Collins D., Neelin P., Peters T., Evans A. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 1994, 18(2):192-205.
11. Dawson D.A., Cha K., Lewis L.B., Mendola J.D., Shmuel A. Evaluation and calibration of functional network modeling methods based on known anatomical connections. NeuroImage 2013, 67:331-343.
12. Fox M., Raichle M. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 2007, 8(9):700-711.
13. Fox M., Snyder A., Vincent J., Corbetta M., Van Essen D., Raichle M. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. 2005, 102(27):9673-9678.
14. Fox M., Zhang D., Snyder A., Raichle M. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 2009, 101(6):3270-3283.
15. Frank K. Impact of a confounding variable on a regression coefficient. Sociol. Methods Res. 2000, 29(2):147-194.
16. Fransson P. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum. Brain Mapp. 2005, 26(1):15-29.
17. He H., Liu T. A geometric view of global signal confounds in resting-state functional MRI. NeuroImage 2012, 59:2339-2348.
18. Jo H., Saad Z., Simmons W., Milbury L., Cox R. Mapping sources of correlation in resting state fMRI, with artifact detection and removal. NeuroImage 2010, 52(2):571-582.
19. Kelly A., Uddin L., Biswal B., Castellanos F., Milham M. Competition between functional brain networks mediates behavioral variability. NeuroImage 2008, 39(1):527-537.
20. Lund T., Madsen K., Sidaros K., Luo W., Nichols T., Bandettini P. Non-white noise in fMRI: does modelling have an impactα. NeuroImage 2006, 29(1):54-66.
21. Murphy K., Birn R., Handwerker D., Jones T., Bandettini P. The impact of global signal regression on resting state correlations: are anti-correlated networks introducedα. NeuroImage 2009, 44(3):893-905.
22. Otsu N. A thresholding selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 1979, 9(1):62-66.
23. Perlbarg V., Bellec P., Anton J., Pelegrini-Issac M., Doyon J., Benali H. CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. Magn. Reson. Imaging 2007, 25(1):35-46.
24. Schölvinck M., Maier A., Ye F., Duyn J., Leopold D. Neural basis of global resting-state fMRI activity. Proc. Natl. Acad. Sci. 2010, 107(22):10238.
25. Shmuel A., Leopold D.A. Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: implications for functional connectivity at rest. Hum. Brain Mapp. 2008, 29:751-761.
26. Shmueli K., van Gelderen P., de Zwart J., Horovitz S., Fukunaga M., Jansma J., Duyn J. Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal. NeuroImage 2007, 38(2):306-320.
27. Uddin L.Q., Kelly A.M.C., Biswal B.B., Castellanos F.X., Milham M.P. Functional connectivity of the default model network components: correlation, anticorrelation and causality. Hum. Brain Mapp. 2009, 30:625-637.
28. Van Dijk K.R.A., Hedden T., Venkataraman A., Evans K.C., Lazar S.W., Bukner R.L. Intrinsinc functional connectivity as a tool for human connectomics: theory, properties and optimization. J. Neurophysiol. 2010, 103:297-321.
29. Weissenbacher A., Kasess C., Gerstl F., Lanzenberger R., Moser E., Windischberger C. Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. NeuroImage 2009, 47(4):1408-1416.
30. Wise R., Ide K., Poulin M., Tracey I. Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. NeuroImage 2004, 21(4):1652-1664.
31. Worsley K.J., Cao J., Paus T., Petrides M., Evans A.C. Applications of random field theory to functional connectivity. Hum. Brain Mapp. 1998, 6(5-6):364-367.
32. Worsley K.J., Liao C.H., Aston J., Petre V., Duncan G.H., Morales F., Evans A.C. A general statistical analysis for fMRI data. NeuroImage 2002, 15(1):1-15.
33. Zijdenbos A., Forghani R., Evans A. Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans. Med. Imaging 2002, 21(10):1280-1291.