scholarly journals Brain State Dependent Closed-Loop Stimulation With EEG and TMS

2021 ◽  
Vol 168 ◽  
pp. S72
Author(s):  
Christoph Zrenner
2016 ◽  
Vol 9 (3) ◽  
pp. 415-424 ◽  
Author(s):  
Dominic Kraus ◽  
Georgios Naros ◽  
Robert Bauer ◽  
Fatemeh Khademi ◽  
Maria Teresa Leão ◽  
...  

2019 ◽  
Vol 12 (2) ◽  
pp. e114-e116
Author(s):  
Eliana Garcia-Cossio ◽  
Sophia Wunder ◽  
Klaus Schellhorn

2021 ◽  
Author(s):  
Ida Grano ◽  
Tuomas P. Mutanen ◽  
Aino E Tervo ◽  
Jaakko O. Nieminen ◽  
Victor Hugo Souza ◽  
...  

Background: Spontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). If not controlled for, they might increase variability in responses and mask meaningful changes in the signals of interest when studying the brain with TMS combined with electroencephalography (TMS–EEG). To address this challenge in future closed-loop stimulation paradigms, we need to understand how spontaneous oscillations affect TMS-evoked responses. Objective: To describe the effect of the pre-stimulus phase of cortical mu (8–13 Hz) and beta (13–30 Hz) oscillations on TMS-induced effective connectivity patterns. Methods: We applied TMS to the left primary motor cortex and right pre-supplementary motor area of three subjects while recording EEG. We classified trials off-line into positive- and negative-phase classes according to the mu and beta rhythms. We calculated differences in the global mean-field amplitude (GMFA) and compared the cortical spreading of the TMS-evoked activity between the two classes. Results: Phase had significant effects on the GMFA in 11 out of 12 datasets (3 subjects × 2 stimulation sites × 2 frequency bands). Seven of the datasets showed significant differences in the time range 15–50 ms, nine in 50–150 ms, and eight after 150 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. Conclusions: TMS-evoked effective connectivity appears to depend on the phase of local cortical oscillations at the stimulated site. This may be crucial for efficient design of future brain-state-dependent and closed-loop stimulation paradigms.


2016 ◽  
Vol 127 (3) ◽  
pp. e41 ◽  
Author(s):  
C. Zrenner ◽  
J. Tünnerhoff ◽  
C. Zipser ◽  
F. Müller-Dahlhaus ◽  
U. Ziemann

2021 ◽  
Vol 11 (1) ◽  
pp. 38
Author(s):  
Aqsa Shakeel ◽  
Takayuki Onojima ◽  
Toshihisa Tanaka ◽  
Keiichi Kitajo

It is a technically challenging problem to assess the instantaneous brain state using electroencephalography (EEG) in a real-time closed-loop setup because the prediction of future signals is required to define the current state, such as the instantaneous phase and amplitude. To accomplish this in real-time, a conventional Yule–Walker (YW)-based autoregressive (AR) model has been used. However, the brain state-dependent real-time implementation of a closed-loop system employing an adaptive method has not yet been explored. Our primary purpose was to investigate whether time-series forward prediction using an adaptive least mean square (LMS)-based AR model would be implementable in a real-time closed-loop system or not. EEG state-dependent triggers synchronized with the EEG peaks and troughs of alpha oscillations in both an open-eyes resting state and a visual task. For the resting and visual conditions, statistical results showed that the proposed method succeeded in giving triggers at a specific phase of EEG oscillations for all participants. These individual results showed that the LMS-based AR model was successfully implemented in a real-time closed-loop system targeting specific phases of alpha oscillations and can be used as an adaptive alternative to the conventional and machine-learning approaches with a low computational load.


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