A new dynamic property of human consciousness

As pointed out by William James, "the consciousness is a dynamic process, not a thing" , during which short term integration is succeeded by another differentiated neural state through the continual interplay between the environment, the body, and the brain itself. Thus, the dynamic structure underlying successive states of the brain is important for understanding human consciousness as a process. In order to investigate the dynamic property of human consciousness, we developed a new method to reconstruct a state space from electroencephalogram(EEG), in which a trajectory, reflecting states of consciousness, is constructed based on the global information integration of the brain. EEGs were obtained from 14 subjects received an intravenous bolus of propopol. Here we show that the degree of human consciousness is directly associated with the information integration capacity of gamma wave, which is significantly higher in the conscious state than in the unconscious state. And we found a new time evolutional property of human consciousness. The conscious state showed a lower dimensional dynamic process which changed to a random-like process after loss of consciousness. This characteristic dynamic property, appeared only in the gamma band, might be used as an indicator to distinguish the conscious and unconscious states and also considered as an important fact for the human consciousness model

Propofol Induction Reduces the Capacity for Neural Information Integration: Implications for the Mechanism of Consciousness and General Anesthesia

The cognitive unbinding paradigm suggests that the synthesis of cognitive information is attenuated by general anesthesia. Here, we investigated the functional organization of brain activities in the conscious and anesthetized states, based on characteristic functional segregation and integration of electroencephalography (EEG). EEG recordings were obtained from 14 subjects undergoing induction of general anesthesia with propofol. We quantified changes in mean information integration capacity in each band of the EEG. After induction with propofol, mean information integration capacity was reduced most prominently in the gamma band of the EEG (p=0.0001). Furthermore, we demonstrate that loss of consciousness is reflected by the breakdown of the spatiotemporal organization of gamma waves. Induction of general anesthesia with propofol reduces the capacity for information integration in the brain. These data directly support the information integration theory of consciousness and the cognitive unbinding paradigm of general anesthesia

Relationship of topology, multiscale phase synchronization, and state transitions in human brain networks

How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram(EEG) properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI) of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa) were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are ��progressive and earlier�� or ��abrupt but delayed�� account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious state. This theoretical approach also offers a principled explanation of how the brain reconstitutes consciousness and cognitive functions after physiologic (sleep), pharmacologic (anesthesia), and pathologic (coma) perturbations. ? 2017 Kim, Kim, Mashour and Lee.115sciescopu

Classification of epilepsy types through global network analysis of scalp electroencephalograms

Epilepsy is a dynamic disease in which self-organization and emergent structures occur dynamically at multiple levels of neuronal integration. Therefore, the transient relationship within multichannel electroencephalograms (EEGs) is crucial for understanding epileptic processes. In this paper, we show that the global relationship within multichannel EEGs provides us with more useful information in classifying two different epilepsy types than pairwise relationships such as cross correlation. To demonstrate this, we determine the global network structure within channels of the scalp EEG based on the minimum spanning tree method. The topological dissimilarity of the network structures from different types of temporal lobe epilepsy is described in the form of the divergence rate and is computed for 11 patients with left (LTLE) and right temporal lobe epilepsy (RTLE). We find that patients with LTLE and RTLE exhibit different large scale network structures, which emerge at the epoch immediately before the seizure onset, not in the preceding epochs. Our results suggest that patients with the two different epilepsy types display distinct large scale dynamical networks with characteristic epileptic network structures.OAIID:oai:osos.snu.ac.kr:snu2006-01/102/2014017262/4SEQ:4PERF_CD:SNU2006-01EVAL_ITEM_CD:102USER_ID:2014017262ADJUST_YN:YEMP_ID:A079623DEPT_CD:801CITE_RATE:2.438DEPT_NM:의학과CONFIRM:

Preferential Inhibition of Frontal-to-Parietal Feedback Connectivity Is a Neurophysiologic Correlate of General Anesthesia in Surgical Patients

BACKGROUND: The precise mechanism and optimal measure of anesthetic-induced unconsciousness has yet to be elucidated. Preferential inhibition of feedback connectivity from frontal to parietal brain networks is one potential neurophysiologic correlate, but has only been demonstrated in animals or under limited conditions in healthy volunteers. METHODS AND FINDINGS: We recruited eighteen patients presenting for surgery under general anesthesia; electroencephalography of the frontal and parietal regions was acquired during (i) baseline consciousness, (ii) anesthetic induction with propofol or sevoflurane, (iii) general anesthesia, (iv) recovery of consciousness, and (v) post-recovery states. We used two measures of effective connectivity, evolutional map approach and symbolic transfer entropy, to analyze causal interactions of the frontal and parietal regions. The dominant feedback connectivity of the baseline conscious state was inhibited after anesthetic induction and during general anesthesia, resulting in reduced asymmetry of feedback and feedforward connections in the frontoparietal network. Dominant feedback connectivity returned when patients recovered from anesthesia. Both analytic techniques and both classes of anesthetics demonstrated similar results in this heterogeneous population of surgical patients. CONCLUSIONS: The disruption of dominant feedback connectivity in the frontoparietal network is a common neurophysiologic correlate of general anesthesia across two anesthetic classes and two analytic measures. This study represents a key translational step from the underlying cognitive neuroscience of consciousness to more sophisticated monitoring of anesthetic effects in human surgical patients

Brain Networks Maintain a Scale-free Organization across Consciousness, Anesthesia, and Recovery: Evidence for Adaptive Reconfiguration

Background: Loss of consciousness is an essential feature of general anesthesia. Although alterations of neural networks during anesthesia have been identified in the spatial domain, there has been relatively little study of temporal organization. Methods: Ten healthy male volunteers were anesthetized with an induction dose of propofol on two separate occasions. The duration of network connections in the brain was analyzed by multichannel electroencephalography and the minimum spanning tree method. Entropy of the connections was calculated based on Shannon entropy. The global temporal configuration of networks was investigated by constructing the cumulative distribution function of connection times in different frequency bands and different states of consciousness. Results: General anesthesia was associated with a significant reduction in the number of network connections, as well as significant alterations of their duration. These changes were most prominent in the 5 bandwidth and were also associated with a significant reduction in entropy of the connection matrix. Despite these and other changes, a global "scale-free" organization was consistently preserved across multiple subjects, anesthetic exposures, states of consciousness, and electroencephalogram frequencies. Conclusions: Our data suggest a fundamental principle of temporal organization of network connectivity that is maintained during consciousness and anesthesia, despite local changes. These findings are consistent with a process of adaptive reconfiguration during general anesthesia.11Ysciescopu

Functional Network Configurations of the Human Brain Lead to Distinct Synchronization Patterns that Model the Progressive and Abrupt Recovery of Consciousness after General Anesthesia

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Topographic Reconfiguration of Local and Shared Information in Anesthetic-Induced Unconsciousness

Theoretical consideration predicts that the alteration of local and shared information in the brain is a key element in the mechanism of anesthetic-induced unconsciousness. Ordinal pattern analysis, such as permutation entropy (PE) and symbolic mutual information (SMI), have been successful in quantifying local and shared information in neurophysiological data; however, they have been rarely applied to altered states of consciousness, especially to data obtained with functional magnetic resonance imaging (fMRI). PE and SMI analysis, together with the superb spatial resolution of fMRI recording, enables us to explore the local information of specific brain areas, the shared information between the areas, and the relationship between the two. Given the spatially divergent action of anesthetics on regional brain activity, we hypothesized that anesthesia would differentially influence entropy (PE) and shared information (SMI) across various brain areas, which may represent fundamental, mechanistic indicators of loss of consciousness. FMRI data were collected from 15 healthy participants during four states: wakefulness (W), light (conscious) sedation (L), deep (unconscious) sedation (D), and recovery (R). Sedation was produced by the common, clinically used anesthetic, propofol. Firstly, we found that that global PE decreased from W to D, and increased from D to R. The PE was differentially affected across the brain areas; specifically, the PE in the subcortical network was reduced more than in the cortical networks. Secondly, SMI was also differentially affected in different areas, as revealed by the reconfiguration of its spatial pattern (topographic structure). The topographic structures of SMI in the conscious states W, L, and R were distinctively different from that of the unconscious state D. Thirdly, PE and SMI were positively correlated in W, L, and R, whereas this correlation was disrupted in D. And lastly, PE changes occurred preferentially in highly connected hub regions. These findings advance our understanding of brain dynamics and information exchange, emphasizing the importance of topographic structure and the relationship of local and shared information in anesthetic-induced unconsciousness