Poster No:
1401
Submission Type:
Abstract Submission
Authors:
Lion Deger1, Jennifer Them1, Leon Martin1, Leon Stefanovski1, Halgurd Taher1, Petra Ritter1
Institutions:
1Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Berlin, Germany
First Author:
Lion Deger
Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin
Berlin, Germany
Co-Author(s):
Jennifer Them
Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin
Berlin, Germany
Leon Martin
Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin
Berlin, Germany
Leon Stefanovski
Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin
Berlin, Germany
Halgurd Taher
Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin
Berlin, Germany
Petra Ritter
Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin
Berlin, Germany
Introduction:
Intravenous anesthetics are routinely used in clinical practice for surgeries, interventions, and in emergency and intensive care medicine. Their different classes induce a loss of consciousness through distinct effects on the receptor-level. Propofol and ketamine, although targeting different transmitter systems, both lead to a disruption of frontoparietal functional networks (Lee et al., 2013). Differential effects are reported in the spectral domain (Lee et al., 2013), as well as in complexity measures (Sarasso et al., 2015). The thalamus is a highly connected brain region modulating cortical dynamics. Anesthetics are known to affect thalamocortical connectivity (e.g.: Liu et al., 2013). Here, we propose a brain network model (BNM) to investigate the link between hypothesized molecular mechanisms of propofol and ketamine, their effect on thalamocortical and corticocortical interactions, and observed network-level phenomena linked to the anesthetic-induced loss of consciousness.
Methods:
We construct a BNM based on an empirically derived structural connectome combining a cortical parcellation with a detailed parcellation of the thalamus (Iglesias et al., 2018). Each node in the BNM is represented by a so-called neural mass model, describing the local interaction between one excitatory and one inhibitory population through biologically meaningful parameters in a mean-field approximated brain region (Breakspear et al., 2003). Each region receives input from all other regions relative to the connection strength defined by the structural connectome. Propofol's GABAergic mechanism of action is introduced through an increase of inhibitory to excitatory local coupling while ketamine's dose dependent NMDAergic mechanism is modeled via the offset reduction of the local excitatory coupling parameters between each node's two regional subnetworks.
Results:
The behavior of the BNM is systematically investigated in a parameter space exploration. Next, the model is optimized to reproduce characteristics of empirical resting-state fMRI data. Molecular effects of propofol and ketamine can be observed through alterations, including regional heterogeneity, of the BNM's coupling parameters. We explore the role of differential anesthetic effects on thalamocortical projections of specific vs. non-specific thalamic nuclei. Anesthetic modulations of thalamocortical and corticocortical interactions are analyzed regarding their effect on thalamocortical functional connectivity and cortical functional networks as well as on spectral properties. Furthermore, we investigate the change of perturbational and spontaneous complexity across the different conditions.
Conclusions:
To our knowledge, this is the first attempt to capture molecular effects of propofol and ketamine within a unified framework. The empirical thalamocortical connectome allows the simulation of distinct anesthetic effects on thalamocortical and corticocortical interactions. This approach enables the systematic exploration of hypothesized mechanisms underlying anesthetic-induced unconsciousness and provides novel insights into the link between molecular actions and network-level phenomena. The framework's adaptability to individualized patient data highlights its clinical potential, paving the way for precision anesthesia and the development of "virtual patient" simulations for anesthetic optimization.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Physiology, Metabolism and Neurotransmission:
Pharmacology and Neurotransmission 2
Keywords:
Computational Neuroscience
Consciousness
Cortex
GABA
Glutamate
Modeling
Thalamus
1|2Indicates the priority used for review
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Provide references using APA citation style.
1. Breakspear, M., Terry, J. R., & Friston, K. J. (2003). Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics. Network: Computation in Neural Systems, 14(4), 703–732. https://doi.org/10.1088/0954-898X_14_4_305
2. Iglesias, J. E., Insausti, R., Lerma-Usabiaga, G., Bocchetta, M., Van Leemput, K., Greve, D. N., Van Der Kouwe, A., Fischl, B., Caballero-Gaudes, C., & Paz-Alonso, P. M. (2018). A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. NeuroImage, 183, 314–326. https://doi.org/10.1016/j.neuroimage.2018.08.012
3. Lee, U., Ku, S., Noh, G., Baek, S., Choi, B., & Mashour, G. A. (2013). Disruption of Frontal–Parietal Communication by Ketamine, Propofol, and Sevoflurane. Anesthesiology, 118(6), 1264–1275. https://doi.org/10.1097/ALN.0b013e31829103f5
4. Liu, X., Lauer, K. K., Ward, B. D., Li, S.-J., & Hudetz, A. G. (2013). Differential Effects of Deep Sedation with Propofol on the Specific and Nonspecific Thalamocortical Systems. Anesthesiology, 118(1), 59–69. https://doi.org/10.1097/ALN.0b013e318277a801
5. Sarasso, S., Boly, M., Napolitani, M., Gosseries, O., Charland-Verville, V., Casarotto, S., Rosanova, M., Casali, A. G., Brichant, J.-F., Boveroux, P., Rex, S., Tononi, G., Laureys, S., & Massimini, M. (2015). Consciousness and Complexity during Unresponsiveness Induced by Propofol, Xenon, and Ketamine. Current Biology, 25(23), 3099–3105. https://doi.org/10.1016/j.cub.2015.10.014
No