Signature of consciousness in the dynamics of resting-state brain activity

Barttfeld, Pablo; Uhrig, Lynn; Sitt, Jacobo; Sigman, Mariano; Jarraya, Béchir; Dehaene, Stanislas

doi:10.1073/pnas.1418031112

Cited by 573 publications

(777 citation statements)

References 54 publications

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“…Recent work on anaesthetized primates [49] found that transient patterns of functional connectivity strongly resembling the anatomical constraints were more frequent during loss of consciousness relative to wakefulness, which appears to contradict our results. However, two important differences must be taken into account.…”

Section: Discussioncontrasting

confidence: 99%

Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics

Tagliazucchi

Chialvo

Siniatchkin

et al. 2016

J. R. Soc. Interface.

170

184

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Loss of cortical integration and changes in the dynamics of electrophysiological brain signals characterize the transition from wakefulness towards unconsciousness. In this study, we arrive at a basic model explaining these observations based on the theory of phase transitions in complex systems. We studied the link between spatial and temporal correlations of large-scale brain activity recorded with functional magnetic resonance imaging during wakefulness, propofol-induced sedation and loss of consciousness and during the subsequent recovery. We observed that during unconsciousness activity in frontothalamic regions exhibited a reduction of long-range temporal correlations and a departure of functional connectivity from anatomical constraints. A model of a system exhibiting a phase transition reproduced our findings, as well as the diminished sensitivity of the cortex to external perturbations during unconsciousness. This framework unifies different observations about brain activity during unconsciousness and predicts that the principles we identified are universal and independent from its causes.

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Section: Discussioncontrasting

confidence: 99%

Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics

Tagliazucchi

Chialvo

Siniatchkin

et al. 2016

J. R. Soc. Interface.

170

184

View full text Add to dashboard Cite

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“…It has been proposed that characterizing the temporalevolution properties of the brain network during different cognitive states is crucial to understanding a large variety of complex processes, from cognitive process dynamics to brain disease. 1,13,14 Despite growing interest in these dynamic networks as a new modality for understanding information processing, development, and disease, their temporal characteristics have not been well characterized. The characterization of dynamic brain network properties has received great interest for the study of brain neural mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Spectral properties of the temporal evolution of brain network structure

Wang

Zhang

et al. 2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems. The temporal evolution of brain network structure is crucial to revealing brain functions at different brain states, such as learning, memory, and visual stimulation. The sliding window method is effective for the construction of a dynamic brain network, and complex network analysis has been widely used to characterize these networks. Recently, the spectral property analysis of complex networks has attracted increasing attention based on its ability to measure more intrinsic properties of networks than complex network analysis. In particular, the dynamic brain network constructed from human electroencephalogram (EEG) data has been successfully studied by applying random matrix theory (RMT), which aims to identify the spectral fluctuation properties of networks. However, little is known about the spectral properties of the dynamic brain network based on human functional magnetic resonance imaging (fMRI) data at different brain states, and how spectral properties reflect changes in the dynamic brain network must be explained in greater detail. Here, we addressed these issues and investigated the spectral properties of a dynamic brain network constructed from human fMRI data. We show that the global and local properties of the eigenvalue of the brain network are effective for evaluating the changes in the dynamic brain network caused by visual stimulation. However, the differences between global properties...

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“…Furthermore, in vivo recordings in animal models (21) reveal that, similar to humans, USFs persist under general anesthesia, yet with a lower amplitude (22,23). Under anesthesia, the dominating functional configurations have low information capacity and lack negative correlations (24). Conversely, in the awake state a dynamic exploration of a rich, flexible repertoire of functional configurations takes place, including ignition, in the course of conscious access.…”

mentioning

confidence: 99%

Nicotinic receptors in mouse prefrontal cortex modulate ultraslow fluctuations related to conscious processing

Koukouli

Rooy

Changeux

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The prefrontal cortex (PFC) plays an important role in cognitive processes, including access to consciousness. The PFC receives significant cholinergic innervation and nicotinic acetylcholine receptors (nAChRs) contribute greatly to the effects of acetylcholine signaling. Using in vivo two-photon imaging of both awake and anesthetized mice, we recorded spontaneous, ongoing neuronal activity in layer II/III in the PFC of WT mice and mice deleted for different nAChR subunits. As in humans, this activity is characterized by synchronous ultraslow fluctuations and neuronal synchronicity is disrupted by light general anesthesia. Both the α7 and β2 nAChR subunits play an important role in the generation of ultraslow fluctuations that occur to a different extent during quiet wakefulness and light general anesthesia. The β2 subunit is specifically required for synchronized activity patterns. Furthermore, chronic application of mecamylamine, an antagonist of nAChRs, disrupts the generation of ultraslow fluctuations. Our findings provide new insight into the ongoing spontaneous activity in the awake and anesthetized state, and the role of cholinergic neurotransmission in the orchestration of cognitive functions.

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Signature of consciousness in the dynamics of resting-state brain activity

Cited by 573 publications

References 54 publications

Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics

Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics

Spectral properties of the temporal evolution of brain network structure

Nicotinic receptors in mouse prefrontal cortex modulate ultraslow fluctuations related to conscious processing

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