Real-Time MEG Source Localization Using Regional Clustering
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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.
KeywordsMagnetoencephalography Real-time Source localization Minimum-norm estimates K-means clustering Brain atlas
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).
Conflict of interest
The authors declare that they have no conflict of interest.
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.
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.
Supplementary material 1 (mp4 26 KB)
- Digia Plc: Qt 5.3. http://qt-project.org/ (1991–2014)
- 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 CrossRefPubMedGoogle Scholar
- 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):73–85Google Scholar
- 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 PubMedCentralCrossRefPubMedGoogle Scholar