P124 Real-time source-level EEG network activity state triggered TMS with millisecond resolution. Issue 3 (March 2017)

• Record Type: Journal Article
• Authors: Belardinelli, P.; Desideri, D.; Zrenner, C.; Ziemann, U.
• Is Part Of: Clinical neurophysiology. Volume 128:Issue 3(2017:Mar.)
• Page Start: e76
• Page End: e77
• Publication Date: 2017-03
• Subjects: Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2016.10.246
• Languages: English
• ISSNs: 1388-2457

Abstract

Abstract : Question: Closed-loop methods that trigger the TMS pulse based on the EEG signal are increasingly available, however, current systems rely on the data of a small number of channels and the signal at sensor level cannot be localized to a specific brain area. The challenge is to analyze a sufficient number of EEG channels in real-time to enable spatially localized estimation of individual brain network activity. Methods: EEG data is acquired using a high-density TMS compatible recording system and streamed online to a real-time digital processing system based on Simulink. A sliding window of data is used to estimate the sources that give rise to the EEG signal using a spatial filter computation executed on the processor as the signal is acquired. The forward model is computed based on the MRI data using a FEM model to solve the forward problem. The leadfields are calculated employing the neuronavigated electrode positions and then employed to compute the real-time LCMV beamforming. Instantaneous phase is estimated at multiple source-level location simultaneously using parallel sliding windows of band-pass filtered data preceding the current time, each extended into the future in real-time using autoregressive model, with instantaneous phase estimated using a Hilbert transform. Results: A latency of <1 ms is achieved in estimating the phase at source level with custom real-time software. A validation study with healthy individuals shows that the instantaneous phase of three distinctly localised brain areas within the motor network both intra- and inter-hemispherically can be estimated in real-time with reòiable accuracy. Conclusions: A novel technique enabling TMS triggered by the instantaneous phase-state of the individual network is presented. This is a significant extension of the current state-of-the-art and could enable the development of more effective personalized closed-loop neuromodulatory stimulation protocols.