“[Is] general anesthesia really a state of communication breakdown across the cortex? [What are] the implications for clinical monitoring?”
Anesthesiology 4 2019, Vol.130, 526-527.
One compelling reason to think that communication across frontal–parietal networks is an important substrate of general anesthetics is that functional connectivity between frontal and parietal cortices has been found to be disrupted during propofol, sevoflurane, and ketamine anesthesia,3 arguing for a common correlate or mediator of diverse anesthetic drugs. This is true in humans based on methods involving electroencephalography4 and functional magnetic resonance imaging.5–7 A recent neuroimaging study in nonhuman primates also demonstrated a suppression of functional connectivity across the prefrontal and posterior parietal cortex during propofol, sevoflurane, and ketamine anesthesia.8 Of relevance to Ma et al., this neuroimaging study of monkeys showed that connectivity patterns during anesthesia tend to converge on specific structural connections, as opposed to the broader repertoire of functional connectivity patterns observed in the waking state that might extend beyond structural highways. However, ketamine anesthesia is also associated with interrupted somatosensory information transfer between the structurally connected primary sensory cortex (in the parietal lobe) and primary motor cortex (in the frontal lobe).9 This finding has been supported by work in human electocorticography, showing disruptions of cortical coherence across similar sensorimotor regions during propofol anesthesia.10 Thus, anesthetics with distinct mechanisms do, indeed, suppress coherence and information transfer across structurally connected frontal–parietal networks. Taken in context, the study of Ma et al. demonstrates that assessment of frontal–parietal connectivity patterns depends on the specific circuit, even if there is a clear structural connection.
This is not the first study to identify increased frontal–parietal connectivity during general anesthesia. Although magnetic resonance imaging data and electroencephalogram-based analysis have been consistent across numerous studies by independent laboratories, some analytic techniques have shown increased functional connectivity or a reversed pattern of directional influence across frontal–parietal networks.11,12 The current study of Ma et al.2 raises the question of whether this discrepancy is due to the specific analytic technique in addition to the specific brain regions analyzed. Although both might contribute, evidence is strong for the latter because Ma et al. analyzed other densely connected areas and found similar results. It is also critical to note that increased functional connectivity does not necessarily imply that there is an overall increase of information exchange between the frontal and parietal cortices. Excessively high functional connectivity could also be associated with a reduction of information transfer by isolating the circuit or reducing its repertoire of responses. Recent work in general anesthesia and disorders of consciousness suggests that there is a “sweet spot” that balances cortical dynamics and functional connectivity to maintain normal levels of consciousness.13
The work of Ma et al.2 adds to the literature by demonstrating that densely connected brain regions in the frontal–parietal network do not manifest the expected reduction of functional connectivity. This study refines the frontal–parietal hypothesis by suggesting a dependence on the specific circuit analyzed and serves as a reminder that complex anesthetic mechanisms and brain dynamics cannot be trivially reduced to a single functional connectivity pattern. This prompts a careful reconsideration of the role of frontal–parietal networks in anesthetic-induced unconsciousness and highlights the need to consider circuit specificity in network-based approaches14 to understanding or monitoring general anesthesia.
References
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2. Ma, L, Liu, W, Hudson, AE . Propofol anesthesia increases long-range frontoparetal corticocortical interaction in the oculomotor circuit in macaque monkeys. Anesthesiology. 2019;130:560–71.
3. Mashour, GA . Top-down mechanisms of anesthetic-induced unconsciousness. Front Syst Neurosci. 2014;8:115.
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11. Barrett, AB, Murphy, M, Bruno, MA, Noirhomme, Q, Boly, M, Laureys, S, Seth, AK . Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia. PLoS One. 2012;7:e29072.
12. Nicolaou, N, Hourris, S, Alexandrou, P, Georgiou, J . EEG-based automatic classification of ‘awake’ versus‘anesthetized’ state in general anesthesia using Granger causality. PLoS One. 2012;7:e33869.
13. Lee, H, Golkowski, D, Jordan, D, Berger, S, Ilg, R, Lee, J, Mashour, GA, Lee, U ; ReCCognition Study Group. Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks. Neuroimage. 2018;188:228–38.
14. Lee, U, Mashour, GA . Role of network science in the study of anesthetic state transitions. Anesthesiology. 2018;129:1029–44.
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