An inversion method based on random sampling for real-time MEG neuroimaging

1. S. Supek and C. J. Aine, Magnetoencephalography. From signals to dynamic cortical networks. Springer, 2014.10.1007/978-3-642-33045-2Search in Google Scholar

2. G. Sudre, L. Parkkonen, E. Bock, S. Baillet, W. Wang, and D. J. Weber, rtMEG: a real-time software interface for magnetoencephalography, Computational intelligence and neuroscience, vol. 2011, p. 11, 2011.10.1155/2011/327953Search in Google Scholar

3. J. Kaipio and E. Somersalo, Statistical and computational inverse problems, vol. 160. Springer, 2005.10.1007/b138659Search in Google Scholar

4. C. Del Gratta, V. Pizzella, F. Tecchio, and G. L. Romani, Magnetoencephalography - A noninvasive brain imaging method with 1 ms time resolution, Reports on Progress in Physics, vol. 64, no. 12, p. 1759, 2001.10.1088/0034-4885/64/12/204Search in Google Scholar

5. M. Hämäläinen, R. Hari, R. J. Ilmoniemi, J. Knuutila, and O. V. Lounasmaa, Magnetoencephalography: theory, instrumentation, and applications to noninvasive studies of the working human brain, Reviews of modern Physics, vol. 65, no. 2, p. 413, 1993.10.1103/RevModPhys.65.413Search in Google Scholar

6. R. Kress, L. Kühn, and R. Potthast, Reconstruction of a current distribution from its magnetic field, Inverse Problems, vol. 18, no. 4, p. 1127, 2002.10.1088/0266-5611/18/4/312Search in Google Scholar

7. J. Cantarella, D. De Turck, and H. Gluck, The Biot-Savart operator for application to knot theory, fluid dynamics, and plasma physics, Journal of Mathematical Physics, vol. 42, pp. 876–905, 2001.10.1063/1.1329659Search in Google Scholar

8. S. Baillet, J. C. Mosher, and R. M. Leahy, Electromagnetic brain mapping, IEEE Signal processing magazine, vol. 18, no. 6, pp. 14–30, 2001.10.1109/79.962275Search in Google Scholar

9. A. Pascarella and A. Sorrentino, Statistical approaches to the inverse problem. INTECH Open Access Publisher, 2011.10.5772/27932Search in Google Scholar

10. A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, and M. S. Hämäläinen, MNE software for processing MEG and EEG data, Neuroimage, vol. 86, pp. 446–460, 2014.10.1016/j.neuroimage.2013.10.027393085124161808Search in Google Scholar

11. A. Gramfort, M. Kowalski, and M. Hämäläinen, Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods, Physics in medicine and biology, vol. 57, no. 7, p. 1937, 2012.10.1088/0031-9155/57/7/1937356642922421459Search in Google Scholar

12. M. Fornasier and F. Pitolli, Adaptive iterative thresholding algorithms for magnetoencephalography (MEG), Journal of Computational and Applied Mathematics, vol. 221, no. 2, pp. 386–395, 2008.10.1016/j.cam.2007.10.048Search in Google Scholar

13. S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte, Combining sparsity and rotational invariance in EEG/MEG source reconstruction, NeuroImage, vol. 42, no. 2, pp. 726–738, 2008.10.1016/j.neuroimage.2008.04.24618583157Search in Google Scholar

14. F. Pitolli and G. Bretti, An iterative algorithm with joint sparsity constraints for magnetic tomography, Lecture Notes in Computer Sciences, vol. 5862, pp. 316–328, 2010.10.1007/978-3-642-11620-9_21Search in Google Scholar

15. M. Sato, T. Yoshioka, S. Kajihara, K. Toyama, N. Goda, K. Doya, and M. Kawato, Hierarchical Bayesian estimation for MEG inverse problem, NeuroImage, vol. 23, no. 3, pp. 806–826, 2004.10.1016/j.neuroimage.2004.06.03715528082Search in Google Scholar

16. D. Calvetti, A. Pascarella, F. Pitolli, E. Somersalo, and B. Vantaggi, A hierarchical Krylov–Bayes iterative inverse solver for MEG with physiological preconditioning, Inverse Problems, vol. 31, no. 12, p. 125005, 2015.10.1088/0266-5611/31/12/125005Search in Google Scholar

17. J. C. Mosher and R. M. Leahy, Source localization using recursively applied and projected (RAP) MUSIC, IEEE Transactions on signal processing, vol. 47, no. 2, pp. 332–340, 1999.10.1109/78.740118Search in Google Scholar

18. A. Sorrentino, L. Parkkonen, A. Pascarella, C. Campi, and M. Piana, Dynamical MEG source modeling with multi-target Bayesian filtering, Human brain mapping, vol. 30, no. 6, pp. 1911–1921, 2009.10.1002/hbm.20786Search in Google Scholar

19. S. Sommariva and A. Sorrentino, Sequential Monte Carlo samplers for semi-linear inverse problems and application to magnetoencephalography, Inverse Problems, vol. 30, no. 11, p. 114020, 2014.10.1088/0266-5611/30/11/114020Search in Google Scholar

20. D. L. Donoho, Superresolution via sparsity constraints, SIAM Journal on Mathematical Analysis, vol. 23, no. 5, pp. 1309–1331, 1992.10.1137/0523074Search in Google Scholar

21. V. Bruni, F. Pitolli, and C. Pocci, A comparison of iterative thresholding algorithms for the MEG inverse problem, IMACS Series in Computational and Applied Mathematics, vol. 19, pp. 21–30, 2016.Search in Google Scholar

22. F. Pitolli and C. Pocci, Neuroelectric source localization by random spatial sampling, Journal of Computational and Applied Mathematics, vol. 296, pp. 237–246, 2016.10.1016/j.cam.2015.09.028Search in Google Scholar

23. B. D. Van Veen, W. Van Drongelen, M. Yuchtman, and A. Suzuki, Localization of brain electrical activity via linearly constrained minimum variance spatial filtering, IEEE Transactions on biomedical engineering, vol. 44, no. 9, pp. 867–880, 1997.10.1109/10.6230569282479Search in Google Scholar

24. K. Sekihara and S. S. Nagarajan, Adaptive spatial filters for electromagnetic brain imaging. Springer Science & Business Media, 2008.Search in Google Scholar

25. M. A. Quraan, Characterization of brain dynamics using beamformer techniques: advantages and limitations. INTECH Open Access Publisher, 2011.Search in Google Scholar

26. D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, and A. C. Evans, Design and construction of a realistic digital brain phantom, Medical Imaging, IEEE Transactions on, vol. 17, no. 3, pp. 463–468, 1998.10.1109/42.7121359735909Search in Google Scholar

27. R. Oostenveld, P. Fries, E. Maris, and J.-M. Schoffelen, FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data, Computational intelligence and neuroscience, vol. 2011, 2010.10.1155/2011/156869302184021253357Search in Google Scholar

28. V. Pizzella, S. Della Penna, C. Del Gratta, and G. L. Romani, SQUID systems for biomagnetic imaging, Superconductor Science and Technology, vol. 14, no. 7, p. R79, 2001.10.1088/0953-2048/14/7/201Search in Google Scholar

29. G. Nolte, The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors, Physics in medicine and biology, vol. 48, no. 22, p. 3637, 2003.10.1088/0031-9155/48/22/00214680264Search in Google Scholar

30. N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard, N. Delcroix, B. Mazoyer, and M. Joliot, Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, Neuroimage, vol. 15, no. 1, pp. 273–289, 2002.10.1006/nimg.2001.097811771995Search in Google Scholar

31. D. Calvetti, B. Wodlinger, D. M. Durand, and E. Somersalo, Hierarchical beamformer and cross-talk reduction in electroneurography, Journal of Neural Engineering, vol. 8, no. 5, p. 056002, 2011.10.1088/1741-2560/8/5/056002Search in Google Scholar