scholarly journals Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Author(s):  
Maria-Ioanna Stefanou ◽  
David Baur ◽  
Paolo Belardinelli ◽  
Til Ole Bergmann ◽  
Corinna Blum ◽  
...  
Author(s):  
Forough Habibollahi Saatlou ◽  
Nigel C. Rogasch ◽  
Nicolas A. McNair ◽  
Mana Biabani ◽  
Steven D. Pillen ◽  
...  

The capacity to externally control transcranial magnetic stimulation (TMS) devices is becoming increasingly important in brain stimulation research. Here we introduce MAGIC (MAGnetic stimulator Interface Controller), an open-source MATLAB toolbox for controlling Magstim and MagVenture stimulators. MAGIC includes a series of MATLAB functions which allow the user to arm/disarm the stimulator, send triggers, change stimulator settings such as amplitude, interpulse intervals, and frequency, and receive stimulator setting information via a serial port connection between a computer and the stimulator. By providing external control capability, MAGIC enables greater flexibility in designing research protocols which require trial-by-trial changes of device settings to realize a priori trial randomization or interactive ad hoc adjustment of parameters during an ongoing experiment. MAGIC thus helps to prevent experimental confounds related to the block-wise variation of parameters and facilitates the integration of TMS with cognitive/sensory tasks, and the development of more adaptive brain state-dependent brain stimulation protocols.


2019 ◽  
Author(s):  
Sina Shirinpour ◽  
Ivan Alekseichuk ◽  
Kathleen Mantell ◽  
Alexander Opitz

ABSTRACTBrain oscillations reflect system-level neural dynamics and capture the current brain state. These brain rhythms can be measured noninvasively in humans with electroencephalography (EEG). Up and down states of brain oscillations capture local changes in neuronal excitability. This makes them a promising target for non-invasive brain stimulation methods such as Transcranial Magnetic Stimulation (TMS). Real-time EEG-TMS systems record ongoing brain signals, process the data, and deliver TMS stimuli at a specific brain state. Despite their promise to increase the temporal specificity of stimulation, best practices and technical solutions are still under development. Here, we implement and compare state-of-the-art methods (Fourier based, Autoregressive Prediction) for real-time EEG-TMS and evaluate their performance both in silico and experimentally. We further propose a new robust algorithm for delivering real-time EEG phase-specific stimulation based on short prerecorded EEG training data (Educated Temporal Prediction). We found that Educated Temporal Prediction performs at the same level or better than Fourier-based or Autoregressive methods both in silico and in vivo, while being computationally more efficient. Further, we document a dependency of EEG signal-to-noise ratio (SNR) on algorithm accuracy across all algorithms. In conclusion, our results can give important insights for real-time TMS-EEG technical development as well as experimental design.


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

2021 ◽  
Vol 15 ◽  
Author(s):  
Shanice E. W. Janssens ◽  
Alexander T. Sack

Transcranial magnetic stimulation (TMS) can cause measurable effects on neural activity and behavioral performance in healthy volunteers. In addition, TMS is increasingly used in clinical practice for treating various neuropsychiatric disorders. Unfortunately, TMS-induced effects show large intra- and inter-subject variability, hindering its reliability, and efficacy. One possible source of this variability may be the spontaneous fluctuations of neuronal oscillations. We present recent studies using multimodal TMS including TMS-EMG (electromyography), TMS-tACS (transcranial alternating current stimulation), and concurrent TMS-EEG-fMRI (electroencephalography, functional magnetic resonance imaging), to evaluate how individual oscillatory brain state affects TMS signal propagation within targeted networks. We demonstrate how the spontaneous oscillatory state at the time of TMS influences both immediate and longer-lasting TMS effects. These findings indicate that at least part of the variability in TMS efficacy may be attributable to the current practice of ignoring (spontaneous) oscillatory fluctuations during TMS. Ignoring this state-dependent spread of activity may cause great individual variability which so far is poorly understood and has proven impossible to control. We therefore also compare two technical solutions to directly account for oscillatory state during TMS, namely, to use (a) tACS to externally control these oscillatory states and then apply TMS at the optimal (controlled) brain state, or (b) oscillatory state-triggered TMS (closed-loop TMS). The described multimodal TMS approaches are paramount for establishing more robust TMS effects, and to allow enhanced control over the individual outcome of TMS interventions aimed at modulating information flow in the brain to achieve desirable changes in cognition, mood, and behavior.


2021 ◽  
Vol 286 ◽  
pp. 78-79
Author(s):  
Sara Borgomaneri ◽  
Simone Battaglia ◽  
Alessio Avenanti ◽  
Giuseppe di Pellegrino

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