Abstract
With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject’s reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low signal-to-noise ratio (SNR) and the limited time available for computations. Since the low SNR reduces the number of distinguishable sources, we propose an approach which downsizes the source space based on a cortical atlas and allows to discern the sources in the presence of noise. Each cortical region is represented by a small set of dipoles, which is obtained by a clustering algorithm. Using this approach, we adapted dynamic statistical parametric mapping for real-time source localization. In terms of point spread and crosstalk between regions the proposed clustering technique performs better than selecting spatially evenly distributed dipoles. We conducted real-time source localization on MEG data from an auditory experiment. The results demonstrate that the proposed real-time method localizes sources reliably in the superior temporal gyrus. We conclude that real-time source estimation based on MEG is a feasible, useful addition to the standard on-line processing methods, and enables feedback based on neural activity during the measurements.
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At this point we want to emphasis that everybody is very welcome to contribute to and to participate of the further development of this package.
References
Babadi B, Obregon-Henao G, Lamus C, Hamalainen MS, Brown EN, Purdon PL (2014) A subspace pursuit-based iterative greedy hierarchical solution to the neuromagnetic inverse problem. NeuroImage 87:427–443. doi:10.1016/j.neuroimage.2013.09.008
Besserve M, Martinerie J, Garnero L (2011) Improving quantification of functional networks with EEG inverse problem: evidence from a decoding point of view. NeuroImage 55(4):1536–1547. doi:10.1016/j.neuroimage.2011.01.056
Chowdhury RA, Lina JM, Kobayashi E, Grova C (2013) MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical Bayesian approaches. PLOS One 8(2):e55,969. doi:10.1371/journal.pone.0055969
Dale AM, Liu AK, Fischl B, 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):55–67. doi:10.1016/S0896-6273(00)81138-1
Destrieux CE, Fischl B, Dale A, Halgren E (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53(1):1–15. doi:10.1016/j.neuroimage.2010.06.010
Digia Plc: Qt 5.3. http://qt-project.org/ (1991–2014)
Dinh C, Luessi M, Sun L, Haueisen J, Hamalainen MS (2013) MNE-X: MEG/EEG real-time acquisition, real-time processing, and real-time source localization framework. Biomed Eng 58(1):4184. doi:10.1515/bmt-2013-4184
Dinh C, Strohmeier D, Haueisen J, Güllmar D (2012) Brain atlas based region of interest selection for real-time source localization using k-means lead field clustering and RAP-MUSIC. Biomed Eng 57(Suppl 1):813. doi:10.1515/bmt-2012-4316
Eichardt R, Baumgarten D, Petković B, Wiekhorst F, Trahms L, Haueisen J (2012) Adapting source grid parameters to improve the condition of the magnetostatic linear inverse problem of estimating nanoparticle distributions. Med Biol Eng Comput 50(10):1081–1089. doi:10.1007/s11517-012-0950-4
Fischl B (2012) FreeSurfer. NeuroImage 62(2):774–781. doi:10.1016/j.neuroimage.2012.01.021
Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32:675–701. doi:10.1080/01621459.1937.10503522
Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck CM, Parkkonen L, Hamalainen MS (2013) MNE software for processing MEG and EEG data. NeuroImage. doi:10.1016/j.neuroimage.2013.10.027
Hamalainen MS, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993) Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65(2):413–497. doi:10.1103/RevModPhys.65.413
Hamalainen MS, Ilmoniemi RJ (1994) Interpreting magnetic fields of the brain: minimum norm estimates. Med Biol Eng Comput 32(1):35–42. doi:10.1007/BF02512476
Jones SR, Kerr CE, Wan Q, Pritchett DL, Hamalainen MS, Moore CI (2010) Cued spatial attention drives functionally relevant modulation of the mu rhythm in primary somatosensory cortex. J Neurosci 30(41):13,760–13,765. doi:10.1523/JNEUROSCI.2969-10.2010
Lin FH, Witzel T, Ahlfors SP, Stufflebeam SM, Belliveau JW, Hämäläinen MS (2006) Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates. NeuroImage 31(1):160–71. doi:10.1016/j.neuroimage.2005.11.054
Lloyd SP (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137. doi:10.1109/TIT.1982.1056489
Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50–60. doi:10.1214/aoms/1177730491
Michel CM, Pascual-Marqui RD, Strik WK, Koenig T, Lehmann D (1995) Frequency domain source localization shows state-dependent diazepam effects in 47-channel EEG. J Neural Trans Gen Sect 99(1–3):157–171. doi:10.1007/BF01271476
Ou W, Golland P, Hamalainen MS (2007) Sources of variability in MEG. Med Image Comput Comput Assist Interv 10(Pt 2):751–759. doi:10.1007/978-3-540-75759-7-91
Papadelis C, Harini C, Ahtam B, Doshi C, Grant E, Okada Y (2013) Current and emerging potential for magnetoencephalography in pediatric epilepsy. J Pediatr Epilepsy 2(1):73–85
Soekadar S, Birbaumer N, Cohen L (2011) Brain-computer-interfaces in the rehabilitation of stroke and neurotrauma. In: Kenji K, Cohen LG (eds) Systems Neuroscience Rehabilitation. Springer, Japan, pp 3–18. doi:10.1007/978-4-431-54008-3-1
Sudre G, Parkkonen L, Bock E, Baillet S, Wang W, Weber DJ (2011) rtMEG: a real-time software interface for magnetoencephalography. Comput Intell Neurosci 2011:327,953. doi:10.1155/2011/327953
Supek S, Aine CJ (1993) Simulation studies of multiple dipole neuromagnetic source localization: model order and limits of source resolution. IEEE Trans Bio Med Eng 40(6):529–540. doi:10.1109/10.237672
Tarkiainen A, Liljeström M, Seppä M, Salmelin R (2003) The 3D topography of MEG source localization accuracy: effects of conductor model and noise. Clin Neurophys 114(10):1977–1992. doi:10.1016/S1388-2457(03)00195-0
Wehner DT, Hämäläinen MS, Mody M, Ahlfors SP (2008) Head movements of children in MEG: quantification, effects on source estimation, and compensation. NeuroImage 40(2):541–50. doi:10.1016/j.neuroimage.2007.12.026
Weisend M, Hanlon F, Montaño R, Ahlfors SP, Leuthold A, Pantazis D, Mosher JC, Georgopoulos A, Hämäläinen MS, Aine C (2007) Paving the way for cross-site pooling of magnetoencephalography (MEG) data. Int Congr Ser 1300:615–618. doi:10.1016/j.ics.2006.12.095
Wilcoxon F (1945) Individual Comparisons by ranking methods. Biom Bull 1(6):80–83
Ziegler DA, Pritchett DL, Hosseini-Varnamkhasti P, Corkin S, Hamalainen MS, Moore CI, Jones SR (2010) Transformations in oscillatory activity and evoked responses in primary somatosensory cortex in middle age: A combined computational neural modeling and MEG study. NeuroImage 52(3):897–912. doi:10.1016/j.neuroimage.2010.02.004
Acknowledgments
This work was funded by the German Research Foundation (DFG, grant Ba 4858/1-1), National Institutes of Health (NIH, grants 5R01EB009048 and 2P41EB015896), IZKF Jena (J21) and the German Academic Exchange Service (DAAD).
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The authors declare that they have no conflict of interest.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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Informed consent was obtained from all individual participants included in the study. Additional informed consent was obtained from all individual participants for whom identifying information is included in this article.
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Dinh, C., Strohmeier, D., Luessi, M. et al. Real-Time MEG Source Localization Using Regional Clustering. Brain Topogr 28, 771–784 (2015). https://doi.org/10.1007/s10548-015-0431-9
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DOI: https://doi.org/10.1007/s10548-015-0431-9