Pharmacological perturbation of brain dynamics: effects of esketamine and midazolam on metastability

Background

Psychiatric disorders are increasingly conceptualized as disruptions in dynamic networks in which symptoms arise from altered interactions between psychological processes and neurobiological mechanisms [1]. In healthy functioning, brain activity maintains a balance between stability and flexibility, enabling coordinated interactions between brain regions while preserving functional specialization. This is referred to as metastability which can be quantified as fluctuations in synchronization across brain regions over time [2]. In psychiatric disorders, however, brain activity can become either too rigid (e.g. OCD, depression) or unstable (e.g. ADHD, mania, psychosis) [3,4]. This imbalance can manifest behaviorally for example as rumination in overly rigid networks or disorganized thinking in overly unstable ones. Neurotransmitter systems are thought to play a crucial role in these dynamics by modulating the stability of network states and transitions between them. Pharmacological perturbation in healthy participants provides a controlled approach to probe these mechanisms, as it allows transient modulation of neurotransmitter systems without the confounds of psychiatric disorders. Clinically relevant substances such as esketamine and midazolam are of particular interest for this. Esketamin, which modulates glutamatergic signaling, has been used as a pharmacological model of formal thought disorder [5], linking its effects to increased cognitive instability, whereas midazolam enhances inhibitory GABAergic signaling thereby reducing cortical excitability. Together, this provides a way to investigate how changes in these neurotransmitter systems influence metastability, providing insight into mechanisms that may underlie psychiatric disorders.

Methods

We are currently collecting resting-state fMRI data in healthy participants (target N = 90) who receive a single dose of intranasal esketamine (0.65 mg/kg), midazolam (0.05 mg/kg), or placebo in a between-subject design. The data will be preprocessed and parcellated into cortical and subcortical regions using a standard atlas (e.g. AAL3). BOLD time series in each region will be band-pass filtered, and instantaneous phase signals will be extracted using the Hilbert transform. Whole-brain phase synchronization at each time point will be quantified using the Kuramoto order parameter, resulting in a measure of global synchrony at each time point (R; 0 = complete independence, 1 = full synchronization). For each participant, global phase synchrony will be summarized by its mean and by its standard deviation across time, with the latter defining metastability. Group differences between esketamine, midazolam, and placebo will be assessed using a general linear model controlling for relevant covariates such as age, sex and movement during the scan.

Expected Results

We hypothesize that pharmacological modulation will systematically alter global metastability compared to placebo, with esketamine and midazolam exerting differential, potentially opposing, effects on network stability and flexibility.

Conclusions

These findings will provide insight into how pharmacological perturbations shift the balance between network stability and flexibility. Characterizing these effects may help underlying the clinical efficacy of esketamine and contribute to the identification of metastability as a mechanistic marker in psychiatric disorders. Insights gained from healthy participants can provide a foundation for future research in clinical populations and may ultimately inform personalized treatment approaches that take each patient’s unique network dynamics into account. Thereby bridging the gap between mechanistic insights and clinical application.

References

1. Borsboom, D. (2017). A network theory of mental disorders. World psychiatry, 16(1), 5-13.
2. Deco, G., Kringelbach, M. L., Jirsa, V. K., & Ritter, P. (2017). The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Scientific reports, 7(1), 3095.
3. Durstewitz, D., Huys, Q. J., & Koppe, G. (2021). Psychiatric illnesses as disorders of network dynamics. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 865-876.
4. Figueroa, C. A., Cabral, J., Mocking, R. J., Rapuano, K. M., Van Hartevelt, T. J., Deco, G., … & Ruhé, H. G. (2019). Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder. Human brain mapping, 40(9), 2771-2786.
5. Nagels, A., Cabanis, M., Oppel, A., Kirner-Veselinovic, A., Schales, C., & Kircher, T. (2018). S-ketamine-induced NMDA receptor blockade during natural speech production and its implications for formal thought disorder in schizophrenia: a pharmaco-fMRI study. Neuropsychopharmacology, 43(6), 1324-1333.