Introduction
In recent years, neuroscience has made significant progress in unraveling how neural networks generate different states of consciousness. We now know that consciousness does not depend on a single brain region, but rather emerges from the dynamic and coordinated interaction of multiple functional networks distributed throughout the brain (Mashour & Hudetz, 2018; Brown et al., 2011). This integration is essential for maintaining awareness, attention, and the ability to respond to our environment.
But what really happens when the brain is exposed to anesthetic agents? How do these functional networks, usually integrated and synchronized, become fragmented, leading to loss of consciousness? And even more intriguingly, how do they restore their connectivity and dynamics to allow recovery of consciousness? Exploring these mechanisms is not only fundamental for anesthetic safety, but also brings us closer to understanding the very nature of consciousness and its vulnerability to pharmacological intervention.
Current evidence indicates that anesthetic agents induce unconsciousness primarily by disrupting functional connectivity among large-scale brain networks, particularly affecting communication between the frontal and parietal cortices, as well as thalamocortical coupling (Brown et al., 2011; Mashour, 2014). This functional fragmentation results in the brain’s inability to integrate information, leading to a reversible loss of consciousness.
In this article, we will review:
- The main neural networks involved in consciousness (default mode network, central executive network, and salience network)
- How anesthetic agents alter the connectivity and dynamics of these networks
- The difference between connected and disconnected consciousness under anesthesia
- The clinical relevance of these findings for advanced monitoring and patient safety
The Neural Networks of Consciousness
Consciousness emerges from the dynamic interaction between several brain networks, primarily:
- Default Mode Network (DMN): Active during rest, introspection, and self-referential thought. Essential for autobiographical memory and internal thought processes.
- Central Executive Network (CEN): Involved in directed attention, working memory, and problem-solving. Enables adaptive responses to the environment.
- Salience Network (SN): Detects relevant stimuli and facilitates transitions between the DMN and CEN, regulating the balance between internal and external processes.
These networks are part of the so-called human connectome, the functional map of brain connections that enables the integration and processing of internal and external perceptions.
Functional Integration and Consciousness
Consciousness requires effective functional connectivity among these networks. The salience network acts as a mediator, determining when the brain should switch from an introspective state (DMN) to an active attention state (CEN), depending on the relevance of stimuli. The thalamus and thalamocortical connections are essential for synchronizing the activity of these networks and sustaining the conscious state.
Ultimately, conscious experience depends on the functional coordination of multiple neural networks. The action of anesthetic agents disrupts this coordination, causing a transient disconnection that manifests as loss of consciousness, without permanently altering brain architecture.
What Happens When We Administer Anesthetic Agents?
“General anesthesia is not a state of global brain suppression, but rather a state of altered connectivity among specific brain networks, particularly a breakdown in frontoparietal and thalamocortical connectivity.”
— Brown EN, Purdon PL, Van Dort CJ. (2011)
Connected and Disconnected Consciousness
States of consciousness can be classified as:
- Connected consciousness: Functional integration among networks, with conscious perception of the environment.
- Disconnected consciousness: Loss of connectivity, with absence of response to the environment, although internal experiences (such as dreams) may persist.
During anesthesia, the goal is to achieve a stable disconnected consciousness: no explicit memory, no pain, and autonomic stability. The EEG shows coherent frontal alpha-delta oscillations without spectral collapse, reflecting the stability of this state.
Effects of Anesthetic Agents on Neural Networks
| Anesthetic Agent | Salience Network (SN) | Default Mode Network (DMN) | Central Executive Network (CEN) |
|---|---|---|---|
| Propofol | Blocked | Fragmented | Silenced |
| Sevoflurane | Blocked | Fragmented | Silenced |
| Ketamine | Disruptive | Hyperconnected/Disorganized | Partially Suppressed |
| Dexmedetomidine | Partially Active | Partially Inhibited, preserved in light sedation | Suppressed |
Source: Adapted from Dr. Frederico conference, Barcelona 2025.
Feedforward, Feedback, and Consciousness
- Feedforward (ascending): Processing of information from sensory areas to frontal regions.
- Feedback (descending): Integration and prediction processing from frontal to sensory areas.
Anesthetic agents, especially propofol, preferentially block feedback (frontal ? parietal), leading to loss of integration and disconnection from external consciousness. Internal consciousness (DMN) may be partially preserved at low doses but becomes decoupled and silenced at surgical doses.
“Anesthetics preferentially disrupt top-down (feedback) connectivity, which is thought to be essential for conscious perception and integration.”
— Mashour GA. (2014)
Advanced Monitoring: EEG and DSA
Interpretation of EEG and the density spectral array (DSA) allows identification of specific patterns according to the anesthetic agent and state of consciousness. However, EEG-derived indices have limitations: they may omit the phenotypic richness of the EEG, do not anticipate rapid changes in consciousness, and do not account for individual variables such as age or frailty.
“The EEG signatures of general anesthesia reflect the underlying disruption of communication between brain regions, with specific patterns corresponding to different anesthetic agents and depths of anesthesia.”
— Purdon PL, Sampson A, Pavone KJ, Brown EN. (2015)
Clinical Implications
- Optimize monitoring of anesthetic depth.
- Anticipate and prevent cognitive complications, especially in vulnerable patients.
- Improve safety and quality of anesthetic care.
Conclusion
Understanding how anesthesia affects the brain is not just a scientific curiosity for me—it’s a responsibility. By clarifying these complex concepts, I hope to help my colleagues provide safer and more thoughtful care, because when we truly understand how consciousness is formed and how it can be disrupted, we are better able to safeguard our patients’ brains and improve their outcomes every day in the operating room.
References
- Brown EN, Purdon PL, Van Dort CJ. General anesthesia and altered states of arousal: a systems neuroscience analysis. Annu Rev Neurosci. 2011;34:601-628. doi:10.1146/annurev-neuro-060909-153200.
- Mashour GA, Hudetz AG. Neural Correlates of Unconsciousness in Large-Scale Brain Networks. Trends Neurosci. 2018;41(3):150-160. doi:10.1016/j.tins.2017.12.003.
- Mashour GA. Top-down mechanisms of anesthetic-induced unconsciousness. Front Syst Neurosci. 2014;8:115. doi:10.3389/fnsys.2014.00115.
- Purdon PL, Sampson A, Pavone KJ, Brown EN. Clinical Electroencephalography for Anesthesiologists: Part I: Background and Basic Signatures. Anesthesiology. 2015;123(4):937–960. doi:10.1097/ALN.0000000000000841.
- Mashour GA. Integrating the Science of Consciousness and Anesthesia. Anesth Analg. 2019;128(4):783-789. doi:10.1213/ANE.0000000000004061.