Computational Intelligence and Neuroscience
Volume 2011 (2011), Article ID 879716, 13 pages
doi:10.1155/2011/879716
Research Article
Brainstorm: A User-Friendly Application for MEG/EEG Analysis
1Signal & Image Processing Institute, University of Southern California, Los Angeles, CA 90089, USA
2MEG Program, Departments of Neurology & Biophysics, Froedtert & Medical College of Wisconsin, Milwaukee, WI 53226, USA
3Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland, OH 44195, USA
4MEG Lab, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Received 4 October 2010; Accepted 28 January 2011
Academic Editor: Robert Oostenveld
Copyright © 2011 François Tadel et al. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Linked References
- S. Baillet, J. C. Mosher, and R. M. Leahy, “Electromagnetic brain mapping,” IEEE Signal Processing Magazine, vol. 18, no. 6, pp. 14–30, 2001. View at Publisher · View at Google Scholar · View at Scopus
- M. X. Huang, J. C. Mosher, and R. M. Leahy, “A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG,” Physics in Medicine and Biology, vol. 44, no. 2, pp. 423–440, 1999. View at Publisher · View at Google Scholar · View at Scopus
- F. Darvas, J. J. Ermer, J. C. Mosher, and R. M. Leahy, “Generic head models for atlas-based EEG source analysis,” Human Brain Mapping, vol. 27, no. 2, pp. 129–143, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- J. C. Mosher, P. S. Lewis, and R. M. Leahy, “Multiple dipole modeling and localization from spatio-temporal MEG data,” IEEE Transactions on Biomedical Engineering, vol. 39, no. 5, pp. 541–557, 1992. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- J. W. Phillips, R. M. Leahy, and J. C. Mosher, “MEG-Based imaging of focal neuronal current sources,” IEEE Transactions on Medical Imaging, vol. 16, no. 3, pp. 338–348, 1997. View at Scopus
- S. Baillet and L. Garnero, “A Bayesian approach to introducing anatomo-functional priors in the EEG/MEG inverse problem,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 5, pp. 374–385, 1997. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- D. M. Schmidt, J. S. George, and C. C. Wood, “Bayesian inference applied to the electromagnetic inverse problem,” Human Brain Mapping, vol. 7, no. 3, pp. 195–212, 1999. View at Scopus
- G. L. Barkley and C. Baumgartner, “MEG and EEG in epilepsy,” Journal of Clinical Neurophysiology, vol. 20, no. 3, pp. 163–178, 2003. View at Publisher · View at Google Scholar · View at Scopus
- A. Arieli, A. Sterkin, A. Grinvald, and A. Aertsen, “Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses,” Science, vol. 273, no. 5283, pp. 1868–1871, 1996. View at Scopus
- C. Tallon-Baudry and O. Bertrand, “Oscillatory gamma activity in humans and its role in object representation,” Trends in Cognitive Sciences, vol. 3, no. 4, pp. 151–162, 1999. View at Publisher · View at Google Scholar · View at Scopus
- G. Pfurtscheller and F. H. Lopes Da Silva, “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842–1857, 1999. View at Publisher · View at Google Scholar · View at Scopus
- P. Tass, M. G. Rosenblum, J. Weule et al., “Detection of n:m phase locking from noisy data: application to magnetoencephalography,” Physical Review Letters, vol. 81, no. 15, pp. 3291–3294, 1998. View at Scopus
- C. W. J. Granger, B. N. Huangb, and C. W. Yang, “A bivariate causality between stock prices and exchange rates: evidence from recent Asianflu,” Quarterly Review of Economics and Finance, vol. 40, no. 3, pp. 337–354, 2000. View at Scopus
- W. Hesse, E. Möller, M. Arnold, and B. Schack, “The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies,” Journal of Neuroscience Methods, vol. 124, no. 1, pp. 27–44, 2003. View at Publisher · View at Google Scholar · View at Scopus
- H. B. Hui, D. Pantazis, S. L. Bressler, and R. M. Leahy, “Identifying true cortical interactions in MEG using the nulling beamformer,” NeuroImage, vol. 49, no. 4, pp. 3161–3174, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- J. L. P. Soto, D. Pantazis, K. Jerbi, S. Baillet, and R. M. Leahy, “Canonical correlation analysis applied to functional connectivity in MEG,” in Proceedings of the 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '10), pp. 113–116, April 2010. View at Publisher · View at Google Scholar
- D. Pantazis, T. E. Nichols, S. Baillet, and R. M. Leahy, “A comparison of random field theory and permutation methods for the statistical analysis of MEG data,” NeuroImage, vol. 25, no. 2, pp. 383–394, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- S. Baillet, J. C. Mosher, and R. M. Leahy, “BrainStorm beta release: a Matlab software package for MEG signal processing and source localization and visualization,” NeuroImage, vol. 11, no. 5, p. S915, 2000. View at Scopus
- J. C. Mosher, S. Baillet, F. Darvas, et al., “Electromagnetic brain imaging using brainstorm,” vol. 7, no. 2, pp. 189–190.
- A. Tzelepi, N. Laskaris, A. Amditis, and Z. Kapoula, “Cortical activity preceding vertical saccades: a MEG study,” Brain Research, vol. 1321, pp. 105–116, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- F. Amor, S. Baillet, V. Navarro, C. Adam, J. Martinerie, and M. Le Van Quyen, “Cortical local and long-range synchronization interplay in human absence seizure initiation,” NeuroImage, vol. 45, no. 3, pp. 950–962, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- T. A. Bekinschtein, S. Dehaene, B. Rohaut, F. Tadel, L. Cohen, and L. Naccache, “Neural signature of the conscious processing of auditory regularities,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 5, pp. 1672–1677, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- F. Carota, A. Posada, S. Harquel, C. Delpuech, O. Bertrand, and A. Sirigu, “Neural dynamics of the intention to speak,” Cerebral Cortex, vol. 20, no. 8, pp. 1891–1897, 2010. View at Publisher · View at Google Scholar · View at PubMed
- M. Chaumon, D. Hasboun, M. Baulac, C. Adam, and C. Tallon-Baudry, “Unconscious contextual memory affects early responses in the anterior temporal lobe,” Brain Research, vol. 1285, pp. 77–87, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- S. Moratti and A. Keil, “Not what you expect: experience but not expectancy predicts conditioned responses in human visual and supplementary cortex,” Cerebral Cortex, vol. 19, no. 12, pp. 2803–2809, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- D. Pantazis, G. V. Simpson, D. L. Weber, C. L. Dale, T. E. Nichols, and R. M. Leahy, “A novel ANCOVA design for analysis of MEG data with application to a visual attention study,” NeuroImage, vol. 44, no. 1, pp. 164–174, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- P. Hansen, M. Kringelbach, and R. Salmelin, Eds., Meg: An Introduction to Methods, Oxford University Press, Oxford, UK, 2010.
- R. Salmelin and S. Baillet, “Electromagnetic brain imaging,” Human Brain Mapping, vol. 30, no. 6, pp. 1753–1757, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- The MoinMoin Wiki Engine, http://moinmo.in/.
- vBulleti, http://www.vbulletin.com/.
- D. W. Shattuck and R. M. Leahy, “Brainsuite: an automated cortical surface identification tool,” Medical Image Analysis, vol. 8, no. 2, pp. 129–142, 2002. View at Publisher · View at Google Scholar · View at Scopus
- Y. Cointepas, J.-F. Mangin, L. Garnero, J.-B. Poline, and H. Benali, “BrainVISA: software platform for visualization and analysis of multi-modality brain data,” Neuroimage, vol. 13, no. 6, p. S98, 2001.
- FreeSurfer, http://surfer.nmr.mgh.harvard.edu/.
- FieldTrip, Donders Institute for Brain, Cognition and Behaviour, http://fieldtrip.fcdonders.nl/.
- A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, no. 1, pp. 9–21, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- M. S. Hämäläinen, MNE software, http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php.
- D. L. Collins, A. P. Zijdenbos, V. Kollokian et al., “Design and construction of a realistic digital brain phantom,” IEEE Transactions on Medical Imaging, vol. 17, no. 3, pp. 463–468, 1998. View at Scopus
- R. M. Leahy, J. C. Mosher, M. E. Spencer, M. X. Huang, and J. D. Lewine, “A study of dipole localization accuracy for MEG and EEG using a human skull phantom,” Electroencephalography and Clinical Neurophysiology, vol. 107, no. 2, pp. 159–173, 1998. View at Publisher · View at Google Scholar · View at Scopus
- F. Darvas, D. Pantazis, E. Kucukaltun-Yildirim, and R. M. Leahy, “Mapping human brain function with MEG and EEG: methods and validation,” NeuroImage, vol. 23, no. 1, pp. S289–S299, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- P. Berg and M. Scherg, “A fast method for forward computation of multiple-shell spherical head models,” Electroencephalography and Clinical Neurophysiology, vol. 90, no. 1, pp. 58–64, 1994. View at Publisher · View at Google Scholar · View at Scopus
- A. Gramfort, T. Papadopoulo, E. Olivi, and M. Clerc, “OpenMEEG: opensource software for quasistatic bioelectromagnetics,” BioMedical Engineering Online, vol. 9, article 45, 2010. View at Publisher · View at Google Scholar · View at PubMed
- M. S. Hämäläinen and R. J. Ilmoniemi, “Interpreting magnetic fields of the brain: minimum norm estimates,” Medical and Biological Engineering and Computing, vol. 32, no. 1, pp. 35–42, 1994. View at Scopus
- A. M. Dale, A. K. Liu, B. R. Fischl et al., “Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity,” Neuron, vol. 26, no. 1, pp. 55–67, 2000. View at Scopus
- R. D. Pascual-Marqui, “Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details,” Methods and Findings in Experimental and Clinical Pharmacology, vol. 24, no. D, pp. 5–12, 2002. View at Scopus
- B. D. Van Veen and K. M. Buckley, “Beamforming: a versatile approach to spatial filtering,” IEEE ASSP Magazine, vol. 5, no. 2, pp. 4–24, 1988. View at Publisher · View at Google Scholar · View at Scopus
- R. O. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276–280, 1986, Reprint of the original 1979 paper from the RADC Spectrum Estimation Workshop.
- N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou et al., “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. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- C. Tallon-Baudry, O. Bertrand, C. Wienbruch, B. Ross, and C. Pantev, “Combined EEG and MEG recordings of visual 40 Hz responses to illusory triangles in human,” NeuroReport, vol. 8, no. 5, pp. 1103–1107, 1997. View at Scopus
- M. S. Worden, J. J. Foxe, N. Wang, and G. V. Simpson, “Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex,” The Journal of Neuroscience, vol. 20, no. 6, p. RC63, 2000. View at Scopus
- W. Klimesch, M. Doppelmayr, T. Pachinger, and B. Ripper, “Brain oscillations and human memory: EEG correlates in the upper alpha and theta band,” Neuroscience Letters, vol. 238, no. 1-2, pp. 9–12, 1997. View at Publisher · View at Google Scholar · View at Scopus
- O. Jensen and C. D. Tesche, “Frontal theta activity in humans increases with memory load in a working memory task,” European Journal of Neuroscience, vol. 15, no. 8, pp. 1395–1399, 2002. View at Publisher · View at Google Scholar · View at Scopus
- W. Klimesch, M. Doppelmayr, H. Russegger, T. Pachinger, and J. Schwaiger, “Induced alpha band power changes in the human EEG and attention,” Neuroscience Letters, vol. 244, no. 2, pp. 73–76, 1998. View at Publisher · View at Google Scholar · View at Scopus
- C. S. Herrmann, M. Grigutsch, and N. A. Busch, “EEG oscillations and wavelet analysis,” in Event-Related Potentials-A Methods Handbook, pp. 229–259, MIT Press, Cambridge, Mass, USA, 2005.
- C. E. McCulloch and S. R. Searle, Generalized, Linear, and Mixed Model, John Wiley & Sons, New York, NY, USA, 2001.
- W. D. Penny, A. P. Holmes, and K. J. Friston, “Random effects analysis,” Human Brain Function, vol. 2, pp. 843–850, 2004.
- D. Pantazis and R. M. Leahy, “Meg: an introduction to methods,” in Statistical Inference in MEG Distributed Source Imaging, chapter 10, Oxford University Press, Oxford, UK, 2010.
- J. A. Mumford and T. Nichols, “Modeling and inference of multisubject fMRI data,” IEEE Engineering in Medicine and Biology Magazine, vol. 25, no. 2, pp. 42–51, 2006. View at Publisher · View at Google Scholar · View at Scopus
- Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” Journal of the Royal Statistical Society Series B, vol. 57, no. 1, pp. 289–300, 1995.
- T. Nichols and S. Hayasaka, “Controlling the familywise error rate in functional neuroimaging: a comparative review,” Statistical Methods in Medical Research, vol. 12, no. 5, pp. 419–446, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
- D. J. Kroon, “Iterative Closest Point using finite difference optimization to register 3D point clouds affine,” http://www.mathworks.com/matlabcentral/fileexchange/24301-finite-iterative-closest-point.
- D. Shepard, “Two- dimensional interpolation function for irregularly- spaced data,” in Proceedings of the ACM National Conference, pp. 517–524, 1968. View at Scopus
- A. A. Joshi, D. W. Shattuck, P. M. Thompson, and R. M. Leahy, “Surface-constrained volumetric brain registration using harmonic mappings,” IEEE Transactions on Medical Imaging, vol. 26, no. 12, pp. 1657–1668, 2007. View at Publisher · View at Google Scholar · View at Scopus
- J. L. P. Soto, D. Pantazis, K. Jerbi, J. P. Lachaux, L. Garnero, and R. M. Leahy, “Detection of event-related modulations of oscillatory brain activity with multivariate statistical analysis of MEG data,” Human Brain Mapping, vol. 30, no. 6, pp. 1922–1934, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- J. Lefèvre and S. Baillet, “Optical flow approaches to the identification of brain dynamics,” Human Brain Mapping, vol. 30, no. 6, pp. 1887–1897, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
- SPM, http://www.fil.ion.ucl.ac.uk/spm/.