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Localization of oscillatory neural activity by means of developing an on-line data analysis workflow

Learning how active brain areas are functionally connected is one of the biggest challenges. The most important technology to study rapid changes in the spatio-temporal dynamics of the underlying electrophysiological interaction is a state-of-the-art multichannel whole-head magnetoencephalography (MEG) device. The analysis of recordings from modern multi-channel MEG system is computationally demanding. MEG data processing usually requires filtering, artefact rejection, source localization and connectivity analysis, as shown in Fig. 1.

MEG complete workflowFig. 1
Copyright: L. Breuer, J. Dammers, T. P. L. Roberts, and N. J. Shah, “Ocular and cardiac artifact rejection for real-time analysis in MEG,” J. Neurosci. Methods, vol. 233, pp. 105–114, Jun. 2014.

To push MEG more forward towards a clinical diagnosis tool (e.g., pre-surgery planning, localisation of epileptogenic zones etc.) results must be available during or soon after the investigation. The dynamics of the brain are extremely rich and it is now clear that it is possible to see major effects by controlling, in real time, when stimuli are applied with respect to the state of the neuronal dynamic system [2]. Our goal is to accomplish advanced MEG signal pre-processing in real-time and on-line source reconstruction. On-line MEG data processing depends on the individual subject’s ongoing response and requires fast signal processing units. All analysis tools must be able to assess the true source level from transient changes in the spatio-temporal profile of the neuromagnetic recordings. The project is addressed through a scientific collaboration with research groups from the Forschungszentrum Jülich: the Institute of Neuroscience and Medicine (INM-4) and the Central Institute of Engineering, Electronics and Analytics (ZEA-2 – Electronic Systems).

References
[1] L. Breuer, J. Dammers, T. P. L. Roberts, and N. J. Shah, “Ocular and cardiac artifact rejection for real-time analysis in MEG,” J. Neurosci. Methods, vol. 233, pp. 105–114, Jun. 2014.
[2] Dale, C.L., Findlay, A.M., Adcock, R.A., Vertinski, M., Fisher, M., Genevsky, A., Aldebot, S., Subramaniam, K., Luks, T.L., Simpson, G. V, Nagarajan, S.S., Vinogradov, S., 2010. Timing is everything: neural response dynamics during syllable processing and its relation to higher-order cognition in schizophrenia and healthy comparison subjects. Int. J. Psychophysiol. 75, 183–93.


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