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Analysis of complex-valued functional magnetic resonance imaging data: are we just going through a "phase"?

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EN
Abstrakty
EN
Functional magnetic resonance imaging (fMRI) data are acquired as a natively complex data set, however for various reasons the phase data is typically discarded. Over the past few years, interest in incorporating the phase information into the analyses has been growing and new methods for modeling and processing the data have been developed. In this paper, we provide an overview of approaches to understand the complex nature of fMRI data and to work with the utilizing the full information, both the magnitude and the phase. We discuss the challenges inherent in trying to utilize the phase data, and provide a selective review with emphasis on work in our group for developing biophysical models, preprocessing methods, and statistical analysis of the fully-complex data. Of special emphasis are the use of data-driven approaches, which are particularly useful as they enable us to identify interesting patterns in the complex-valued data without making strong assumptions about how these changes evolve over time, something which is challenging for magnitude data and even more so for the complex data. Finally, we provide our view of the current state of the art in this area and make suggestions for what is needed to make efficient use of the fully-complex fMRI data.
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371--418
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Bibliogr. 85 poz., rys., tab.
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autor
autor
  • The Mind Research Network, Albuquerque, New Mexico 87106, USA / Department of ECE, University of New Mexico, Albuquerque, New Mexico 87131, USA
Bibliografia
  • [1] S. Ogawa, D.W. Tank, R. Menon, J.M. Ellermann, S.G. Kim, H. Merkle , and K. Ugurbil, “Intrinsic signal changes accompanying sensory stimulation, functional brain mapping with magnetic resonance imaging”, Proc. Natl. Acad. Sci. 89 (13), 5951-5955 (1992).
  • [2] N. Petridou, A. Schafer. P. Gowland, and R. Bowtell, “Phase vs. magnitude information in functional magnetic resonance imaging time series, toward understanding the noise”, Magn.Reson. Imaging 27 (8), 1046-57 (2009).
  • [3] P. Rodriguez, V.D. Calhoun, and T. Adali, “Phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data, Pattern Recognition 45 (6), 2050- 2063 (2012).
  • [4] P. Rodriguez, N. Correa, T. Adali, T. Eichele, and V.D. Calhoun, “Quality map thresholding for de-noising of complexvalued fmri data and its application to ICA of fMRI”, J. SignalProcessing Systems 1, 1-16 (2009).
  • [5] T. Adali and V.D. Calhoun, “Complex ICA of brain imaging data”, IEEE Signal Proc. Magazine 24 (5), 136-139 (2007).
  • [6] S. Arja, Z. Feng, Z. Chen, A. Caprihan, K.A. Kiehl, T. Adali, and V.D. Calhoun, “Changes in fMRI magnitude data and phase data observed in block-design and event-related tasks”, NeuroImage 49 (4), 3149-3160 (2010).
  • [7] V.D. Calhoun and T. Adali, “Complex ICA for fMRI analysis, performance of several approaches”, Proc. ICASSP 1, CDROM (2003).
  • [8] V.D. Calhoun, T. Adali, G.D. Pearlson, P.C. van Zijl and J.J. Pekar, “Independent component analysis of fMRI data in the complex domain”, Magn. Reson. Med. 48 (1), 180-192 (2002).
  • [9] H. Li, T. Adali, N. Correa, P. Rodriguez, and V.D. Calhoun, “Flexible complex ICA of fMRI data”, Proc. ICASSP 1, CDROM (2010).
  • [10] H. Li, N. Correa, V.D. Calhoun, and T. Adali, “Application of independent component analysis with adaptive density model to complex-valued fMRI data”, IEEE Trans. Biomed. Eng. I 58 (10), 2794-2803 (2011).
  • [11] F. G. Hoogenraad, P.J. Pouwels, M.B. Hofman, J.R. Reichenbach, M. Sprenger, and E.M. Haacke, “Quantitative differentiation between BOLD models in fMRI”, Magn. Reson. Med. 45 (2), 233-246 (2001).
  • [12] F.G Hoogenraad, J.R. Reichenbach, E.M. Haacke, S. Lai, K. Kuppusamy, and M. Sprenger, “In vivo measurement of changes in venous blood-oxygenation with high resolution functional MRI at 0.95 tesla by measuring changes in susceptibility and velocity”, Magn. Res. Med. 39 (1), 97-107 (1998).
  • [13] R. Menon, “Postacquisition suppression of large-vessel BOLD signals in high-resolution fMRI”, Magn. Res. Med. 47 (1), 1-9 (2002).
  • [14] D.B. Rowe, “Parameter estimation in the magnitude-only and complex-valued fMRI data models”, Neuroimage 25 (4), 1124- 32 (2005b).
  • [15] D.B. Rowe, “Modeling both the magnitude and phase of complex-valued fMRI data”, Neuroimage 25(4), 1310-24 (2005a).
  • [16] D.B. Rowe and B.R. Logan, “A complex way to compute fMRI activation”, Neuroimage 23 (3), 1078-92 (2004).
  • [17] D.B. Rowe and B.R. Logan, “Complex fMRI analysis with unrestricted phase is equivalent to a magnitude-only model”, Neuroimage 24 (2), 603-6 (2005).
  • [18] A.S. Nencka and D.B. Rowe, “Reducing the unwanted draining vein BOLD contribution in fMRI with statistical postprocessing methods”, Neuroimage 37 (1), 177-88 (2007).
  • [19] D.G. Tomasi and E.C. Caparelli, “Macrovascular contribution in activation patterns of working memory”, J. Cereb. BloodFlow Metab. 27 (1), 33-42 (2007).
  • [20] F. Zhao, T. Jin, P. Wang, X. Hu, and S.G. Kim, “Sources of phase changes in BOLD and CBV-weighted fMRI”, Magn. Reson.Med. 57 (3), 520-7 (2007).
  • [21] Z. Feng, A. Caprihan, K. Blagoev and V.D. Calhoun, “Biophysical modeling of phase changes in BOLD fMRI”, NeuroImage 47, 540-548 (2009).
  • [22] T. Adali and S. Haykin, Adaptive Signal Processing Next GenerationSolutions, Wiley-IEEE Press, New York, 2010.
  • [23] T. Adali, P.J. Schreier, and L.L. Scharf, “Complex-valued signal processing, The proper way to deal with impropriety”, IEEE Trans. Signal Processing 59 (11), 5101-5123 (2011).
  • [24] B. Picinbono and P. Chevalier, “Widely linear-estimation with complex data”, IEEE Trans. on Signal Processing 43 (8), 2030- 2033 (1995).
  • [25] P. Schreier and L.L. Scharf, “Second-order analysis of improper complex random vectors and processes”, IEEE Trans.on Signal Processing 51 (3), 714-725 (2003).
  • [26] P. Schreier and L.L. Scharf, “Statistical signal processing of complex-valued data”, The Theory of Improper and NoncircularSignals 1, CD-ROM (2010).
  • [27] K. Kreutz-Delgago, The Complex Gradient Operator and theCR-Calculus, University of California, San Diego, 2007.
  • [28] W. Wirtinger, Zur formalen theorie der funktionen von mehr complexen ver¨a¨anderlichen, Math. Ann. 97, 357-375 (1927).
  • [29] B. Picinbono, “On circularity”, IEEE Trans. Signal Processing 42, 3473-3482 (1994).
  • [30] P. Chevalier and F. Pipon, “New insights into optimal widely linear array receivers for the demodulation of BPSK, MSK, and GMSK signals corrupted by noncircular interferences - application to SAIC”, IEEE Trans. Signal Processing 54 (3), 870-883 (2006).
  • [31] F. Roemer and M. Haardt, “Efficient 1-D and 2-D DOA estimation for non-circular sources with hexagonal shaped espar arrays”, Proc. IEEE Int. Conf. Acoust. Speech, Signal Processing(ICASSP) 1, 881-884 (2007).
  • [32] E. Hardy, D. Hoferer, D. Mertens, and G. Kasper, “Automated phase correction via maximization of the real signal”, Mag.Res. Imag. 27, 393-400 (2009).
  • [33] A. Macovski, “Noise in MRI”, Magn. Res. Med. 36 (3), 494- 497 (1996).
  • [34] D.H. Brandwood, “A complex gradient operator and its application in adaptive array theory”, Proc. Inst. Elect. Eng. 1, 11-16 (1983).
  • [35] K.B Petersen and M.S. Pedersen, The Matrix Cookbook, Technical University of Denmark, Copenhagen, 2008.
  • [36] H. Li, T. Adali, “Complex-valued adaptive signal processing using nonlinear functions”, J. Advances in Signal Processing B, 1-9 (2008).
  • [37] A. Van den Bos, “Complex gradient and Hessian”, IEE Proc.Vision, Image, and Signal Processing 1, 380-382 (1994).
  • [38] T. Adali, T. Kim, and V.D. Calhoun, “Independent Component analysis by complex nonlinearities”, Proc. ICASSP 1, 525-528 (2004).
  • [39] T. Adali and H. Li, Complex-Valued Adaptive Signal Processing,Adaptive Signal Processing, Next Generation Solutions, Wiley, New York, 2009.
  • [40] T. Adali, M. Novey, and J.F. Cardoso, “Complex ICA using nonlinear functions”, IEEE Trans. Signal Processing 59 (9), 4356-4544 (2008).
  • [41] H. Li and T. Adali, “A class of complex ICA algorithms based on the kurtosis cost function”, IEEE Trans. Neural Netw. 19 (3), 408-420 (2008).
  • [42] X. Li and T. Adali, “Independent component analysis by entropy bound minimization”, IEEE Trans. on Signal Processing 58 (10), 5151-5164 (2010).
  • [43] M. Novey and T. Adali, “Complex ICA by negentropy maximization”, IEEE Trans. Neural Networks 19 (4), 596-609 (2008).
  • [44] M. Novey and T. Adali, “On extending the complex FastICA algorithm to noncircular sources”, IEEE Trans. Signal Processing 56 (5), 2148-2154 (2008).
  • [45] J. Martindale, A.J. Kennerley, D. Johnston, Y. Zheng, and J.E. Mayhew, “Theory and generalization of Monte Carlo models of the BOLD signal source”, Magnetic Resonance in Medicine 59 (3), 607-618 (2008).
  • [46] J.P. Marques and R.W. Bowtell, “Using forward calculations of the magnetic field perturbation due to a realistic vascular model to explore the BOLD effect”, NMR Biomed. 21 (6), 553-65 (2008).
  • [47] Z. Chen, A. Caprihan, and V.D. Calhoun, “Effect of surrounding vasculature on intravoxel BOLD signal”, Med.Phys. 37 (4), 1778-1787 (2010).
  • [48] K.M. Koch, X. Papademetris, D.L. Rothman, and R.A. de Graaf, “Rapid calculations of susceptibility-induced magnetostatic field perturbations for in vivo magnetic resonance”, Physics in Medicine and Biology 51 (24), 6381-6402 (2006).
  • [49] D.A. Yablonskiy and E.M. Haacke, “Theory of NMR signal behavior in magnetically inhomogeneous tissues, the static dephasing regime”, Magn. Reson. Med. 32 (6), 749-63 (1994).
  • [50] W.M. Spees, D.A. Yablonskiy, M.C. Oswood, and J.J. Ackerman, “Water proton MR properties of human blood at 1.5 Tesla, magnetic susceptibility, T(1), T(2), T*(2), and non- Lorentzian signal behavior”, Magn. Reson. Med. 45 (4), 533- 42 (2001).
  • [51] A.C. Guyton and J.E. Hall, Textbook of Medical Physiology, W.B. Saunders Company, Philadelphia, 1996.
  • [52] L. Freire, A. Roche, and J.F. Mangin, “What is the best similarity measure for motion correction in fMRI time series?”, IEEE Trans. Med. Imaging 21 (5), 470-484 (2002).
  • [53] E.M. Haacke, N.Y. Cheng, M.J. House, Q. Liu, J. Neelavalli, R.J. Ogg, A. Khan, M. Ayaz, W. Kirsch, and A. Obenaus, “Imaging iron stores in the brain using magnetic resonance imaging”, Magn. Reson. Imaging 23 (1), 1-25 (2005).
  • [54] S. Khullar, A. Michael, N. Correa, T. Adali, S. Baum, and V.D. Calhoun, “Wavelet-based fMRI analysis, 3-D denoising, signal separate, and validation metrics”, NeuroImage 54 (4), 2867-2884 (2011).
  • [55] G. Gilboa, N. Sochen, and Y.Y. Zeevi, “Image enhancement and denoising by complex diffusion processes”, IEEE Trans.Pattern Anal. Mach. Intell 26 (8), 1020-36 (2004).
  • [56] T. Adali and H. Li, “A practical formulation for computation of complex gradients and its application to maximum likelihood”, Proc. ICASSP 1, CD-ROM (2007).
  • [57] D.B. Rowe, C.P. Meller, and R.G. Hoffmann, “Characterizing phase-only fMRI data with an angular regression model”, J.Neurosci. Methods 161 (2), 331-41 (2007).
  • [58] A.J. Bell and T.J. Sejnowski, “An information maximisation approach to blind separation and blind deconvolution”, NeuralComputing 7 (6), 1129-1159 (1995).
  • [59] P. Comon, “Independent component analysis - a new concept?”, Signal Proc. 36, 287-314 (1994).
  • [60] A. Hyvarinen, “One-unit contrast functions for independent component analysis. A statistical analysis”, Proc. NNSP 1, 388-397 (1997).
  • [61] A. Hyvarinen, J. Karhunen, and E. Oja, Independent ComponentAnalysis, Johns Wiley & Sons, New York, 2001.
  • [62] Z. Koldovsky, P. Tichavski, and E. Oja, “Efficient variant of algorithm FastICA for independent component analysis attaining the Cramer-Rao lower bound”, IEEE Trans. Neural Netw. 17 (5), 1265-77 (2006).
  • [63] E.G. Learned-Miller, J.W. Fisher III, and T.W. Lee, “ICA using spacings estimates of entropy”, J. Machine Learning Research 4, 1271-1295, (2003).
  • [64] J.F. Cardoso, “Blind signal separation, statistical principles”, Proc. IEEE 9 (10), 2009-2025 (1998).
  • [65] J.A. Palmer, S. Makeig, K. Kreutz-Delgago, and B.D. Rao, “Newton method for the ICA mixture model”, Proc. IEEEInt. Conf. Acoust. Speech, Signal Processing 1, CD-ROM (2008).
  • [66] D.T. Pham and P. Garat, Blind separation of mixture of independent sources through a quasi-maximum likelihood approach, IEEE Trans. Signal Proc. 45 (7), 1712-1725 (1997).
  • [67] T.W. Lee, M. Girolami, and T.J. Sejnowski, “Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources, NeuralComput. 11, 417-441 (1999).
  • [68] D. Erdogmus, K.E. Hild, Y. Rao, and J.C. Principe, “Minimax mutual information approach for independent component analysis”, Neural Comput. 16 (1235), 1252 (2004).
  • [69] A. Hyvarinen, “New approximations of differential entropy for independent component analysis and projection pursuit”, Advancesin Neural Inf. Proc. Sys. 10, 273-279 (1998).
  • [70] X. Li and T. Adali, “Complex independent component analysis by entropy bound minimization”, IEEE Trans. Circuits andSystems I 57 (7), 1417-1430 (2010).
  • [71] W. Lu W and J.C. Rajapakse, “Constrained independent component analysis”, in Adv. Neural Inf. Proc. Sys. pp. 570-576, MIT Press, Cambridge, 2000.
  • [72] W. Lu and J.C. Rajapakse, “ICA with reference”, Proc. Int.Conf. on ICA and BSS 1, 120-125 (2001).
  • [73] L. De Lathauwer and B. De Moor, “On the blind separation of non-circular sources”, Proc. Eur. Signal Process. Conf. (EUSIPCO) 1, CD-ROM (2002).
  • [74] J. Eriksson and V. Koivunen, “Complex random vectors and ICA models, Identifiability, uniqueness and separability”, IEEETrans. Info. Theory 52 (3), 1017-1029 (2006).
  • [75] J.F. Cardoso and A. Souloumiac, “Blind beamforming for non Gaussian signals”, IEE-Proc. F 140 (6), 362-370 (1993).
  • [76] T. Trainini, X.-L. Li, E. Moreau, and T. Adali, “A relative gradient algorithm for joint decompositions of complex matrices”, Proc. Eur. Signal Process. Conf. (EUSIPCO) 1, CD-ROM (2010).
  • [77] J.T.J. Annemueller, T.J. Sejnowski, and S. Makeig, “Complex Independent component analysis of frequency-domain electroencephalographic data”, Neural Networks 16, 1311-1323 (2003).
  • [78] P. Smaragdis, “Blind separation of convolved mixtures in the frequency domain”, Neurocomputing 22 (1-3), 21-34 (1998).
  • [79] E. Bingham and A. Hyvarinen, “A fast fixed-point algorithm for independent component analysis of complex-valued signals”, Int. J. Neural Syst. 10 (1), 1-8 (2000).
  • [80] V.P. Zarzoso and P. Comon, “Robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size”, IEEE Trans. Neural Netw. 21 (2), 248-61 (2010).
  • [81] M. Novey and T. Adali, “A complex generalized Gaussian distribution-characterization, generation, and estimation”, IEEE Trans. on Signal Processing 58 (3), 1427-1433 (2010).
  • [82] M.J. McKeown, S. Makeig, G.G. Brown, T.P. Jung, S.S. Kindermann, A.J. Bell, and T.J. Sejnowski, “Analysis of fMRI data by blind separation into independent spatial components”, HumanBrain Mapping 6, 160-188 (1998).
  • [83] A.C. Rencher, Methods of Multivariate Analysis, John Wiley & Sons, New York, 1995.
  • [84] J.A.Mumford and T. Nichols, “Modeling and inference of multisubject fMRI data”, IEEE Eng Med. Biol. Mag. 25 (2), 42-51 (2006).
  • [85] J. Karvanen, J. Eriksson, and V. Koivunen, “Pearson system based method for blind separation”, Proc. Second Int. Workshopon ICA 1, CD-ROM (2000)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG8-0096-0001
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