Paths to Oblivion: Common Neural Mechanisms of Anaesthesia and Disorders of Consciousness

Abstract

The human brain generates a rich repertoire of spatiotemporal dynamics during normal wakefulness, supporting a wide variety of conscious experiences and cognitive functions. However, neural dynamics are reconfigured, in comparable ways, when consciousness is lost either due to anaesthesia or disorders of consciousness (DOC). Here, leveraging a neurobiologically realistic whole-brain computational model informed by functional MRI, diffusion MRI, and PET, we sought to identify the neurobiological mechanisms that explain the common reconfiguration of neural dynamics observed both for transient pharmacological intervention and chronic neuroanatomical injury. Our results show that, by incorporating local inhibitory action through a PET-based GABA receptor density map, our model can reproduce the brain dynamics of subjects undergoing propofol anaesthesia, and that this effect depends specifically on the spatial distribution of GABA receptors across cortical regions. Additionally, using a structural connectome obtained from DOC patients, we demonstrate how the dynamics that characterise loss of consciousness can emerge from changes in neuroanatomical connectivity. Crucially, we find that each of these two interventions generalises across datasets: a model with increased GABA-mediated inhibition can reproduce the dynamics of DOC patients' brains, and a model with a DOC connectome is also compatible with brain dynamics observed during propofol anaesthesia. These results demonstrate how increased inhibition and connectome randomisation represent different neurobiological paths towards the characteristic dynamics of the unconscious brain. Overall, the present findings begin to disentangle the neurobiological mechanisms by which highly dissimilar perturbations of the brain's neurodynamics can lead to unconsciousness.

LSD and psilocybin flatten the brain’s energy landscape: insights from receptor-informed network control theory

“…Finally, our approach is based on network control theory, which differs from other recent computational investigations using e.g. whole-brain simulation through dynamic mean-field modelling of brain activity [6][7][8]23 . These latter approaches employ a neurobiologically realistic model of brain activity based on mean-field reduction of spiking neurons into excitatory and inhibitory populations, and have been used to account for non-linear effects of 5HT2a neuromodulation induced by LSD and other psychedelics.…”

Mechanisms Underlying Disorders of Consciousness: Bridging Gaps to Move Toward an Integrated Translational Science

“…In this vein, the increasing richness of empirical data can be leveraged to develop increasingly realistic computational models of DOC. In silico whole-brain models offer an especially promising avenue, with their potential to combine macroscale information about brain structure and function with considerations of microscale neurobiology [62][63][64] (Box 2) as well as related empirical data and theoretical perspectives at multiple levels (e.g., how systems-level accounts of DOC interact with information perspectives). Indeed, successful models of anesthetic states have already been put forth [149][150][151].…”

“…Whole-brain computational models represent a powerful set of tools to study macroscale mechanistic questions in neuroscience [62][63][64]. Such models typically combine two fundamental ingredients (Fig.…”

“…The complex interactions of these two key components can give rise to rich and biologically realistic functional dynamics analogous to those observed from fMRI and EEG [65,66]. Although the required level of neurobiological detail will vary according to the specific question under investigation, the more biologically inspired models (e.g., dynamic mean-field) can also be enriched with further information, such as regional myelination or the regional distribution of specific receptors obtained from positron-emission tomography (PET) [64,[67][68][69][70] Importantly, in silico computational models offer several advantages: their parameters are fully available to inspection and manipulation by the researcher, and they can be perturbed in ways that would not be possible in either humans or animals [67][68][69][70]. Computational modeling allows formulation and testing of specific mechanisms, a key feature not provided by other techniques (e.g., neuroimaging).…”

Changes in dynamic transitions between integrated and segregated states underlie visual hallucinations in Parkinson’s disease

“…Nevertheless, future work may seek to leverage insights from non-linear models of brain dynamics, e.g. through neurobiologically detailed dynamic mean-field models that have already been successfully applied to the study of altered states of consciousness(69,92). Finally, data on neurotransmitter density and gene expression were not derived from our participants but from separate cohorts of healthy volunteers and post-mortem human brains respectively; therefore results relating to neurotransmitter receptors should be interpreted with caution.…”