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PLOS ONE  2011 

Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

DOI: 10.1371/journal.pone.0024642

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Abstract:

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.

References

[1]  Friston KJ, Frith CD, Fletcher P, Liddle PF, Frackowiak RS (1996) Functional topography: multidimensional scaling and functional connectivity in the brain. Cerebral Cortex 6: 156–164.
[2]  Buchel C, Friston KJ (1997) Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cerebral Cortex 7: 768–778.
[3]  Valdes-Sosa PA, Sanchez-Bornot JM, Lage-Castellanos A, Vega-Hernandez M, Bosch-Bayard J, et al. (2005) Estimating brain functional connectivity with sparse multivariate autoregression. Philos Trans R Soc Lond B Biol Sci 360: 969–981.
[4]  Valdes-Sosa PA, Sanchez-Bornot JM, Sotero RC, Iturria-Medina Y, Aleman-Gomez Y, et al. (2009) Model driven EEG/fMRI fusion of brain oscillations. Hum Brain Mapp 30: 2701–2721.
[5]  Calhoun VD, Adali T (2006) Unmixing fMRI with independent component analysis. IEEE Eng Med Biol Mag 25: 79–90.
[6]  McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, et al. (1998) Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp 6: 160–188.
[7]  Jafri MJ, Pearlson GD, Stevens M, Calhoun VD (2008) A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage 39: 1666–1681.
[8]  Demirci O, Stevens MC, Andreasen NC, Michael A, Liu J, et al. (2009) Investigation of relationships between fMRI brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls. Neuroimage 46: 419–431.
[9]  Babiloni F, Cincotti F, Babiloni C, Carducci F, Mattia D, et al. (2005) Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function. Neuroimage 24: 118–131.
[10]  Trujillo-Barreto N, Martinez-Montes E, Melie-Garcia L, Valdes-Sosa P (2001) A symmetrical Bayesian model for fMRI and EEG/MEG neuroimage fusion. Int J of Bioelectromag 3: 1.
[11]  Eichele T, Calhoun VD, Debener S (2009) Mining EEG-fMRI using independent component analysis. Int J Psychophysiol 73: 53–61.
[12]  Goldman RI, Stern JM, Engel J Jr, Cohen MS (2002) Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport 13: 2487–2492.
[13]  Moosmann M, Ritter P, Krastel I, Brink A, Thees S, et al. (2003) Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. Neuroimage 20: 145–158.
[14]  Martinez-Montes E, Valdes-Sosa PA, Miwakeichi F, Goldman RI, Cohen MS (2004) Concurrent EEG/fMRI analysis by multiway Partial Least Squares. Neuroimage 22: 1023–1034.
[15]  Debener S, Ullsperger M, Siegel M, Fiehler K, von Cramon DY, et al. (2005) Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring. Journal of Neuroscience 25: 11730–11737.
[16]  Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A 104: 13170–13175.
[17]  Lei X, Qiu C, Xu P, Yao D (2010) A parallel framework for simultaneous EEG/fMRI analysis: Methodology and simulation. Neuroimage 52: 1123–1134.
[18]  Lei X, Xu P, Luo C, Zhao J, Zhou D, et al. (2011) fMRI Functional Networks for EEG Source Imaging. Human Brain Mapping 32: 1141–1160.
[19]  Comon P (1994) Independent component analysis, a new concept? Signal Processing 36: 287–314.
[20]  Makeig S, Westerfield M, Jung TP, Enghoff S, Townsend J, et al. (2002) Dynamic brain sources of visual evoked responses. Science 295: 690–694.
[21]  Granger C (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society 37: 424–438.
[22]  Ding M, Chen Y, Bressler SL (2006) Granger Causality: Basic Theory and Application to Neuroscience; Schelter B, Winterhalder M, Timmer J, editors. Berlin: Wiley-VCH.
[23]  Haykin S (2002) Adaptive filter theory: Upper Saddle River. NJ: Prentice Hall.
[24]  Geweke J (1984) Measures of conditional linear dependence and feedback between time series. Journal of the American Statistical Association 79: 907–915.
[25]  Phillips C, Mattout J, Rugg MD, Maquet P, Friston KJ (2005) An empirical Bayesian solution to the source reconstruction problem in EEG. Neuroimage 24: 997–1011.
[26]  Friston K, Harrison L, Daunizeau J, Kiebel S, Phillips C, et al. (2008) Multiple sparse priors for the M/EEG inverse problem. Neuroimage 39: 1104–1120.
[27]  Friston K, Mattout J, Trujillo-Barreto N, Ashburner J, Penny W (2007) Variational free energy and the Laplace approximation. Neuroimage 34: 220–234.
[28]  Seth AK (2005) Causal connectivity of evolved neural networks during behavior. Network: Computation in Neural Systems 16: 35–54.
[29]  Yao D (2000) Electric potential produced by a dipole in a homogeneous conducting sphere. IEEE Trans Biomed Eng 47: 964–966.
[30]  Yao D (2001) A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas 22: 693–711.
[31]  Roebroeck A, Formisano E, Goebel R (2005) Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 25: 230–242.
[32]  Boynton GM, Engel SA, Glover GH, Heeger DJ (1996) Linear systems analysis of functional magnetic resonance imaging in human V1. Journal of Neuroscience 16: 4207–4221.
[33]  Ostwald D, Porcaro C, Bagshaw AP (2010) An information theoretic approach to EEG-fMRI integration of visually evoked responses. Neuroimage 49: 498–516.
[34]  Niazy RK, Beckmann CF, Iannetti GD, Brady JM, Smith SM (2005) Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage 28: 720–737.
[35]  Grill-Spector K, Malach R (2004) The human visual cortex. Annu Rev Neurosci 27: 649–677.
[36]  Sackett DL (2001) Why randomized controlled trials fail but needn't: 2. Failure to employ physiological statistics, or the only formula a clinician-trialist is ever likely to need (or understand!). CMAJ 165: 1226–1237.
[37]  Deshpande G, Sathian K, Hu X (2010) Effect of hemodynamic variability on Granger causality analysis of fMRI. Neuroimage 52: 884–896.
[38]  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.
[39]  Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, et al. (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98: 676–682.
[40]  Scheeringa R, Fries P, Petersson K-M, Oostenveld R, Grothe I, et al. (2011) Neuronal Dynamics Underlying High- and Low-Frequency EEG Oscillations Contribute Independently to the Human BOLD Signal. Neuron 69: 572–583.
[41]  Beisteiner R, Erdler M, Teichtmeister C, Diemling M, Moser E, et al. (1997) Magnetoencephalography May Help to Improve Functional MRI Brain Mapping. European Journal of Neuroscience 9: 1072–1077.
[42]  Friston KJ, Frith CD (1995) Schizophrenia: a disconnection syndrome? Clin Neurosci 3: 89–97.
[43]  Daunizeau J, Grova C, Marrelec G, Mattout J, Jbabdi S, et al. (2007) Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework. Neuroimage 36: 69–87.
[44]  Luessi M, Babacan SD, Molina R, Booth JR, Katsaggelos AK (2011) Bayesian symmetrical EEG/fMRI fusion with spatially adaptive priors. Neuroimage 55: 113–132.
[45]  Ou W, Nummenmaa A, Ahveninen J, Belliveau JW, Hamalainen MS, et al. (2010) Multimodal Functional Imaging Using fMRI-Informed Regional EEG/MEG Source Estimation. Neuroimage.
[46]  Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Hum Brain Mapp 13: 43–53.
[47]  Valdes-Sosa PA, Vega-Hernandez M, Sanchez-Bornot JM, Martinez-Montes E, Bobes MA (2009) EEG source imaging with spatio-temporal tomographic nonnegative independent component analysis. Hum Brain Mapp 30: 1898–1910.
[48]  Esposito F, Scarabino T, Hyvarinen A, Himberg J, Formisano E, et al. (2005) Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage 25: 193–205.
[49]  Li YO, Adali T, Calhoun VD (2007) Estimating the number of independent components for functional magnetic resonance imaging data. Hum Brain Mapp 28: 1251–1266.
[50]  Marinazzo D, Liao W, Chen H, Stramaglia S (2010) Nonlinear connectivity by Granger causality. Neuroimage.
[51]  Chen AC, Feng W, Zhao H, Yin Y, Wang P (2008) EEG default mode network in the human brain: spectral regional field powers. Neuroimage 41: 561–574.
[52]  Qin Y, Xu P, Yao D (2010) A comparative study of different references for EEG default mode network: The use of the infinity reference. Clinical Neurophysiology 121: 1981–1991.

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