Top

Published in:

01-09-2016 | Original Paper

Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps

Authors: S. Samadi, H. Soltanian-Zadeh, C. Jutten

Published in: Brain Topography | Issue 5/2016

Abstract

Integration of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is an open problem, which has motivated many researches. The most important challenge in EEG-fMRI integration is the unknown relationship between these two modalities. In this paper, we extract the same features (spatial map of neural activity) from both modality. Therefore, the proposed integration method does not need any assumption about the relationship of EEG and fMRI. We present a source localization method from scalp EEG signal using jointly fMRI analysis results as prior spatial information and source separation for providing temporal courses of sources of interest. The performance of the proposed method is evaluated quantitatively along with multiple sparse priors method and sparse Bayesian learning with the fMRI results as prior information. Localization bias and source distribution index are used to measure the performance of different localization approaches with or without a variety of fMRI-EEG mismatches on simulated realistic data. The method is also applied to experimental data of face perception of 16 subjects. Simulation results show that the proposed method is significantly stable against the noise with low localization bias. Although the existence of an extra region in the fMRI data enlarges localization bias, the proposed method outperforms the other methods. Conversely, a missed region in the fMRI data does not affect the localization bias of the common sources in the EEG-fMRI data. Results on experimental data are congruent with previous studies and produce clusters in the fusiform and occipital face areas (FFA and OFA, respectively). Moreover, it shows high stability in source localization against variations in different subjects.

Available only for authorised users

The \(\ell _0\) pseudo-norm does not satisfy the mathematical definition of a norm, However, in the following, we simply say \(\ell _0\) norm.

i.e. with data less sparse than required with algorithm based on \(\ell _1\) norm.

Please note that in the two inequalities, one of them is a strict inequality.

Babaie-Zadeh M, Jutten C (2010) On the stable recovery of the sparsest overcomplete representations in presence of noise. IEEE Trans Signal Process 58(10):5396–5400. doi:10.1109/TSP.2010.2052357 CrossRef

Babaie-Zadeh M, Mehrdad B, Giannakis GB (2012) Weighted sparse signal decomposition. In: Proceedings of ICASSP2012, Kyoto, Japan, pp 3425–3428. doi:10.1109/ICASSP.2012.6288652

Babiloni F, Mattia D, Babiloni C, Astolfi L, Salinari S, Basilisco A, Rossini PM, Marciani MG, Cincotti F (2004) Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle. Magn Reson Imaging 22:1471–1476CrossRefPubMed

Babiloni F, Cincotti F (2005) Multimodal Imaging from neuroelectromagnetic and functional magnetic resonance recordings. In: He B (ed) Modeling and imaging of bioelectrical activity, bioelectric engineering. Springer, New York. doi:10.1007/978-0-387-49963-5-8

Bai X, He B (2005) On the estimation of the number of dipole sources in EEG source localization. Clin Neurophysiol 116:2037–2043. doi:10.1016/j.clinph.2005.06.001 CrossRefPubMedPubMedCentral

Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping. Sig Process Mag IEEE 18(6):14–30. doi:10.1109/79.962275 CrossRef

Bandyopadhyay A, Tomassoni T, Omar A (2004) A numerical approach for automatic detection of the multipoles responsible for ill conditioning in generalized multipole technique (2004) IEEE MTT-S International Microwave Symposium Digest (IEEE Cat No04CH37535). doi:10.1109/MWSYM.2004.1338826

Beck R, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2:183–202CrossRef

Bolstad AK, Veen BDV, Nowak RD (2009) Space-time event sparse penalization for magneto-electroencephalography. NeuroImage 46:1066–1081CrossRefPubMedPubMedCentral

Bruce AG, Sardy S, Tseng P (1998) Block coordinate relaxation methods for nonparamatric signal denoising. doi:10.1117/12.304915, URL 10.1117/12.304915

Candès EJ, Tao T (2005) The dantzig selector: statistical estimation when p is much larger than n. Ann Stat 35:2313–2351CrossRef

Cassidy B, Solo V, Seneviratne A (2012) Grouped l0 least squares penalised magnetoencephalography. In: 2012 9th IEEE international symposium on biomedical imaging (ISBI), pp 868–871. doi:10.1109/ISBI.2012.6235686

Chen SS, Donoho DL, Michael SA (1999) Atomic decomposition by basis pursuit. SIAM J Sci Comput 20:33–61CrossRef

Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW, Lewine JD, Halgren E (2000) Dynamic statistical parametric mapping: combining fmri and meg for high-resolution imaging of cortical activity. Neuron 26(1):55–67CrossRefPubMed

Daubechies I, DeVore R, Fornasier M, Gunturk S (2008) Iteratively re-weighted least squares minimization: proof of faster than linear rate for sparse recovery. In: 42nd Annual conference on information sciences and systems (CISS)

Deb K (1999) Multi-Objective Evolutionary Algorithms: Introducing Bias Among Pareto-Optimal Solutions. Tech. rep, Kanpur Genetic Algorithms Lab (KanGal), Technical report 99002

Dezhong Yao BH (2001) A self-coherence enhancement algorithm and its application to enhancing three-dimensional source estimation from EEGs. Ann Biomed Eng 29:1019–1027CrossRef

Ding L (2009) Reconstructing cortical current density by exploring sparseness in the transform domain. Phys Med Biol 54:2683–2697CrossRefPubMed

Ding L, Ni Y, Sweeney J, He B (2011) Sparse cortical current density imaging in motor potentials induced by finger movement. J Neural Eng 8(3):36008. URL:http://stacks.iop.org/1741-2552/8/i=3/a=036008

Dong L, Wang P, Bin Y, Deng J, Li Y, Chen L, Luo C, Yao D (2015) Local multimodal serial analysis for fusing EEG-fMRI: a new method to study familial cortical myoclonic tremor and epilepsy. IEEE Trans Auton Mental Dev 7(4):311–319. doi:10.1109/TAMD.2015.2411740 CrossRef

Donoho DL, Elad M (2003) Maximal sparsity representation via \(\ell _1\) minimization. Proc Natl Acad Sci 100:1297–2202

Donoho DL (2004) For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution. Comm Pure Appl Math 59:797–829CrossRef

Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52:1289–1306CrossRef

Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32:407–451CrossRef

Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22CrossRefPubMedPubMedCentral

Friston KJ, Fletcher P, Josephs O, Holmes A, Rugg MD, Turner R (1998) Event-related fMRI: characterizing differential responses. NeuroImage 7:30–40CrossRefPubMed

Friston KJ, Harrison L, Daunizeau J, Kiebel SJ, Phillips C, Trujillo-Bareto N, Henson RNA, Flandin G, Mattout J (2008) Multiple sparse priors for the m/eeg inverse problem. NeuroImage 39:1104–1120CrossRefPubMed

Fuchs A, Kelso JAS, Murzin V (2011) Anatomically constrained minimum variance beamforming applied to EEG. Exp Brain Res 214(4):515–528. doi:10.1007/s00221-011-2850-5 CrossRefPubMed

Glover GH (1999) Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage 9:416–429CrossRefPubMed

Gorodnitsky I, George J, Rao B (1995) Neuromagnetic source imaging with focuss: a recursive weighted minimum norm algorithm. Electroencephalogr Clin Neurophysiol 58:267–288

Gramfort A, Kowalski M, Hämäläinen M (2012) Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods. Phys Med Biol 57:1937–1961CrossRefPubMedPubMedCentral

Gramfort A, Kowalski M, Hämäläinen M (2013) Time-frequency mixed-norm estimates: sparse M/EEG imaging with non-stationary source activation. NeuroImage 70:410–422CrossRefPubMedPubMedCentral

Henson R (2010) Multimodal integration: constraining meg localization with EEG and fMRI. In: Supek S, Suac A (eds) IFMBE Proceedings of the 17th international conference on biomagnetism advances in biomagnetism biomag, 2010, vol 28. Springer, Berlin, pp 97–100. doi:10.1007/978-3-642-12197-5_18

Henson RN, Mouchlianitis E, Friston KJ (2009) MEG and EEG data fusion: simultaneous localisation of face-evoked responses. Neuroimage 47:581–589CrossRefPubMedPubMedCentral

Henson RN, Wakeman DG, Litvak V, Friston KJ (2011) A parametric empirical bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration. Front Hum Neurosci 5(76):1–16

Hong D, Zhang F (2010) Weighted elastic net model for mass spectrometry imaging processing. Math Model Nat Phenom 5:115–133CrossRef

Hotelling H (1936) Relations between two sets of variates. Biometrika 28(3–4):321–377. doi:10.1093/biomet/28.3-4.321. URL:http://biomet.oxfordjournals.org/content/28/3-4/321.short; http://biomet.oxfordjournals.org/content/28/3-4/321.full.pdf+html

Inan G, Kiymik M, Akin M, Alkan A (2001) AR spectral analysis of EEG signals by using maximum likelihood estimation. Comput Biol Med 31(6):441–450CrossRef

Irimia A, Horn JDV, Halgren E (2012) Source cancellation profiles of electroencephalography and magnetoencephalography. NeuroImage 59(3):2464–2474. doi:10.1016/j.neuroimage.2011.08.104. http://www.sciencedirect.com/science/article/pii/S1053811911010378

Jorge J, van der Zwaag W, Figueiredo P (2014) EEG-fMRI integration for the study of human brain function. NeuroImage 102(1):24–34. doi:10.1016/j.neuroimage.2013.05.114.CrossRefPubMed

Jun-Tao LI, Ying-Min JIA (2010) An improved elastic net for cancer classication and gene selection. Acta Automat Sin 36:976–981

Koles ZJ, Lazar MS, Zhou SZ (1990) Spatial patterns underlying population differences in the background EEG. Brain Topogr 2(4):275–284. doi:10.1007/BF01129656 CrossRefPubMed

Krüger G, Glover G (2001) Physiological noise in oxygenation-sensitive magnetic resonance imaging. Magn Reson Med 46:631–637CrossRefPubMed

Limpiti T, Van Veen BD, Wakai RT (2006) Cortical patch basis model for spatially extended neural activity. IEEE Transn Biomed Eng 53:1740–1754. doi:10.1109/TBME.2006.873743 CrossRef

Liu AK, Belliveau JW, Dale AM (1998) Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. Proc Natl Acad Sci 95:8945–8950CrossRefPubMedPubMedCentral

Mancera L, Portilla J (2006) L0-norm-based sparse representation through alternate projections. In: ICIP

Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L (2004) EEG source imaging. Clin Neurophysiol 115:2195–2222. doi:10.1016/j.clinph.2004.06.001 CrossRefPubMed

Mohimani H, Babaie-Zadeh M, Jutten C (2009) A fast approach for overcomplete sparse decomposition based on smoothed L0 norm. IEEE Trans Signal Process 57(1):289–301. doi:10.1109/TSP.2008.2007606 CrossRef

Mosher JC, Leahy RM, Lewis PS (1999) EEG and MEG: forward solutions for inverse methods. IEEE Trans Biomed Eng 46:245–259. doi:10.1109/10.748978 CrossRefPubMed

Mosher J, Leahy R (1996) EEG and MEG source localization using recursively applied (RAP) MUSIC. In:Conference record of the thirtieth asilomar conference on signals, systems and computers. doi:10.1109/ACSSC.1996.599135

Murta T, Leite M, Carmichael DW, Figueiredo L, Lemieux L (2015) Electrophysiological correlates of the bold signal for eeg-informed fmri. Hum Brain Mapp 36:391–414CrossRefPubMed

Op de Beeck HP, Haushofer J, Kanwisher N (2008) Interpreting fMRI data: maps, modules, and dimensions. Nat Rev Neurosci 9:123–135CrossRefPubMedPubMedCentral

Pitcher D, Walsh V, Duchaine B (2011) The role of the occipital face area in the cortical face perception network. Exp Brain Res 209(4):481–493. doi:10.1007/s00221-011-2579-1 CrossRefPubMed

R Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/

Rosa MJ, Daunizeau J, Friston KJ (2010) EEG- fMRI integration: a critical review of biophysical modeling and data analysis approaches. Curr Opin Neurol 23:374–381

Samadi S, Amini L, Cosandier-Rimélé D, Soltanian-Zadeh H, Jutten C (2013) Reference-based source separation method for identification of brain regions involved in a reference state from intracerebral EEG. IEEE Trans Biomed Eng 60:1983–1992CrossRefPubMedPubMedCentral

Sameni R, Jutten C, Shamsollahi MB (2008) Multichannel electrocardiogram decomposition using periodic component analysis. IEEE Trans Biomed Eng 55(8):1935–1940. doi:10.1109/TBME.2008.919714 CrossRefPubMed

Shun Chi W, Wang PT, Swindlehurst AL, Nenadic Z (2012) Efficient dipole parameter estimation in EEG systems with near-ML performance. IEEE Trans Biomed Eng 59(5):1339–1348. doi:10.1109/TBME.2012.2187336 CrossRef

Simoncelli EP, Olshausen BA (2001) Natural images statistics and neural reperesentation. Annu Rev Neurosci 24:1193–1216CrossRefPubMed

Strother SC (2006) Evaluating fMRI preprocessing pipelines. IEEE Eng Med Biol Mag 25:27–41CrossRefPubMed

Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat 58:267–288

Tibshirani R, Saunders M, Rosset S, Zhu J, Knight K (2005) Sparsity and smoothness via the fused lasso. J R Stat Soc 67:91–108CrossRef

Tikhonov AN, Arsenin VIA (1977) Solutions of ill-posed problems. Scripta series in mathematics. Winston, Great Falls

Tipping ME (2001) Sparse bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244. doi:10.1162/15324430152748236. URL:http://www.crossref.org/deleted_DOI.html

Van Veen BD (1991) Minimum variance beamforming with soft response constraints. IEEE Trans Signal Process 39(9):1964–1972. doi:10.1109/78.134429 CrossRef

Wagner P, Röschke J, Mann K, Fell J, Hiller W, Frank C, Grözinger M (2000) Human sleep EEG under the influence of pulsed radio frequency electromagnetic fields. Results from polysomnographies using submaximal high power flux densities. Technical report 2000

Welvaert M, Durnez J, Moerkerke B, Verdoolaege G, Rosseel Y (2011) neuRosim: an R package for generating fMRI data. J Stat Softw 44(10):1–18. http://www.jstatsoft.org/v44/i10/

Wipf D, Nagarajan S (2009) A unified Bayesian framework for MEG/EEG source imaging. NeuroImage 44:947–966CrossRefPubMed

Xu D, Yan S, Tao D, Lin S, Zhang HJ (2007) Marginal fisher analysis and its variants for human gait recognition and content- based image retrieval. IEEE Transactions on Image Processing 16(11):2811–2821. URL http://dblp.uni-trier.de/db/journals/tip/tip16.html#XuYTLZ07

Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stati Soc 68(1):49–67CrossRef

Zhang J, Li X, Song Y, Liu J (2012) The fusiform face area is engaged in holistic, not parts-based, representation of faces. PLoS One 7:e40390. doi:10.1371/journal.pone.0040390 CrossRefPubMedPubMedCentral

Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc 67:301–320CrossRef

Title: Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps
Authors: S. Samadi
H. Soltanian-Zadeh
C. Jutten
Publication date: 01-09-2016
Publisher: Springer US
Published in: Brain Topography / Issue 5/2016
Print ISSN: 0896-0267
Electronic ISSN: 1573-6792
DOI: https://doi.org/10.1007/s10548-016-0506-2

At a glance: The STEP trials

Springer Medicine

Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 5/2016

Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study

Effects of Transcranial Magnetic Stimulation on Body Perception: No Evidence for Specificity of the Right Temporo-Parietal Junction

Effect of Range and Angular Velocity of Passive Movement on Somatosensory Evoked Magnetic Fields

Disturbances of Agency and Ownership in Schizophrenia: An Auditory Verbal Event Related Potentials Study

Identification of Focal Epileptogenic Networks in Generalized Epilepsy Using Brain Functional Connectivity Analysis of Bilateral Intracranial EEG Signals

Grey Matter Density Predicts the Improvement of Naming Abilities After tDCS Intervention in Agrammatic Variant of Primary Progressive Aphasia