A novel closed-loop EEG-tDCS approach to promote responsiveness of patients in minimally conscious state: A study protocol.

Transcranial direct current stimulation (tDCS) applied over the prefrontal cortex has been shown to improve behavioral responsiveness in patients with disorders of consciousness following severe brain injury, especially those in minimally conscious state (MCS). However, one potential barrier of clinical response to tDCS is the timing of stimulation with regard to the fluctuations of vigilance that characterize this population. Indeed, a previous study showed that the vigilance of MCS patients has periodic average cycles of 70 min (range 57-80 min), potentially preventing them to be in an optimal neural state to benefit from tDCS when applied randomly. To tackle this issue, we propose a new protocol to optimize the application of tDCS by selectively stimulating at high and low vigilance states. Electroencephalography (EEG) real-time spectral entropy will be used as a marker of vigilance and to trigger tDCS, in a closed-loop fashion. We will conduct a randomized controlled crossover clinical trial on 16 patients in prolonged MCS who will undergo three EEG-tDCS sessions 5 days apart (1. tDCS applied at high vigilance; 2. tDCS applied at low vigilance; 3. tDCS applied at a random moment). Behavioral effects will be assessed using the Coma Recovery Scale-Revised at baseline and right after the stimulations. EEG will be recorded throughout the session and for 30 min after the end of the stimulation. This unique and novel approach will provide patients' tailored treatment options, currently lacking in the field of disorders of consciousness.

Intermittent tACS during a visual task impacts neural oscillations and LZW complexity.

Little is known about how transcranial alternating current stimulation (tACS) interacts with brain activity. Here, we investigate the effects of tACS using an intermittent tACS-EEG protocol and use, in addition to classical metrics, Lempel-Ziv-Welch complexity (LZW) to characterize the interactions between task, endogenous and exogenous oscillations. In a cross-over study, EEG was recorded from thirty participants engaged in a change-of-speed detection task while receiving multichannel tACS over the visual cortex at 10 Hz, 70 Hz and a control condition. In each session, tACS was applied intermittently during 5 s events interleaved with EEG recordings over multiple trials. We found that, with respect to control, stimulation at 10 Hz ([Formula: see text]) enhanced both [Formula: see text] and [Formula: see text] power, [Formula: see text]-LZW complexity and [Formula: see text] but not [Formula: see text] phase locking value with respect to tACS onset ([Formula: see text]-PLV, [Formula: see text]-PLV), and increased reaction time (RT). [Formula: see text] increased RT with little impact on other metrics. As trials associated with larger [Formula: see text]-power (and lower [Formula: see text]-LZW) predicted shorter RT, we argue that [Formula: see text] produces a disruption of functionally relevant fast oscillations through an increase in [Formula: see text]-band power, slowing behavioural responses and increasing the complexity of gamma oscillations. Our study highlights the complex interaction between tACS and endogenous brain dynamics, and suggests the use of algorithmic complexity inspired metrics to characterize cortical dynamics in a behaviorally relevant timescale.

Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks.

State Visual Evoked Potentials (SSVEPs) arise from a resonance phenomenon in the visual cortex that is produced by a repetitive visual stimulus. SSVEPs have long been considered a steady-state response resulting from purely oscillatory components phase locked with the stimulation source, matching the stimulation frequency and its harmonics. Here we explore the dynamical character of the SSVEP response by proposing a novel non-stationary methodology for SSVEP detection based on an ensemble of Echo State Networks (ESN). The performance of this dynamical approach is compared to stationary canonical correlation analysis (CCA) for the detection of 6 visual stimulation frequencies ranging from 12 to 22 Hz. ESN-based methodology outperformed CCA, achieving an average information transfer rate of 47 bits/minute when simulating a BCI system of 6 degrees of freedom. However, for some subjects and stimulation frequencies the detection accuracy of CCA exceeds that of ESN. The comparison suggests that each methodology captures different features of the SSVEP response: while CCA captures purely stationary patterns, the ESN-based approach presented here is capable of detecting the non-stationary nature of the SSVEP.