Social stress contributes to major societal health burdens, such as anxiety disorders and nervousness. Nx4 has been found to modulate stress responses. We investigated whether dampening of such responses is associated with neuronal correlates in brain regions involved in stress and anxiety. In a randomized, placebo-controlled, double-blind, cross-over trial, 39 healthy males took a single dose (three tablets) of either placebo or Nx4, 40 to 60 minutes before an fMRI scan session. We here report on drug effects on amygdala responses during a face-matching task, which was performed during a complex test battery further including resting-state brain connectivity and a social stress experiment. The first of the Primary Outcomes, defined in a hierarchical order, concerned reduced amygdala effects after intake of verum compared to placebo. We found a statistically significant reduction in differential activations in the left amygdala for the contrast negative faces versus forms during verum versus placebo condition. Our results indicate that effects of Nx4 can be monitored in the brain. Previously noted effects on stress responses may thus be modulated by affective brain regions including the amygdala. More than half of the population in Germany suffered from stress in the past five years, and almost 25% suffered frequently 1. But what is stress? Hans Selye influenced the definition of the term "stress", describing it as "the non-specific response of the body to any demand for change" 2. The reaction of the body to any unexpected positive or negative stimuli from external and internal cues for survival was defined to be stress. Nowadays, the term "stress" usually refers to a state of tension. Depending on the cognitive appraisal of the physiological outcome and the stress-inducing stimulus, a highly recurrent and long-lasting tension state may lead to a maladaptive reaction to the stressor and an increase in physio-and psychopathology 3-5. Chronic stress is assumed to be a major risk factor for several physical and psychological disorders leading to increased heart rate and blood pressure, raised cholesterol, headache, restlessness, inability to concentrate, fatigue, state of anxiety, and feeling of depression 6 .

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Electroencephalogram (EEG) microstates that represent quasi-stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non-Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long-short-term-memory (LSTM) units to capture the complex temporal dependencies.Using an LSTM-based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time scales (200-2,000 ms) aiming to capture stably recurring microstate patterns within and across subjects. We show that RNNs can learn underlying microstate patterns with high accuracy and that the microstate trajectories are subject invariant at shorter time scales (≤400 ms) and reproducible across sessions. Significant drop in the reconstruction accuracy was observed for longer sequence lengths of 2,000 ms. These findings indirectly corroborate earlier studies which indicated that EEG microstate sequences exhibit long-range dependencies with finite memory content. Furthermore, we find that the latent representations learned by the RNNs are sensitive to external stimulation such as stress while the conventional univariate microstate measures (e.g., occurrence, mean duration, etc.) fail to capture such changes in brain dynamics. While RNNs cannot be configured to identify the specific discriminating patterns, they have the potential for learning the underlying temporal dynamics and are sensitive to sequence aberrations characterized by changes in metal processes. Empowered with the macroscopic understanding of the temporal dynamics that extends beyond short-term interactions, RNNs offer a reliable alternative for exploring system level brain dynamics using EEG microstate sequences. K E Y W O R D SEEG, microstates, recurrent neural networks, stress