Brain network hypersensitivity underlies pain crises in sickle cell disease.

Sickle cell disease (SCD) is a genetic disorder causing painful and unpredictable Vaso-occlusive crises (VOCs) through blood vessel blockages. In this study, we propose explosive synchronization (ES) as a novel approach to comprehend the hypersensitivity and occurrence of VOCs in the SCD brain network. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) as well as VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited lower alpha frequency than controls. SCD patients showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, stronger FDA was observed in SCD patients with a higher frequency of VOCs and EEG recording near VOC. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Our model demonstrated that a stronger FDA could be linked to increased sensitivity and frequency of VOCs. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.

Brain network hypersensitivity underlies pain crises in sickle cell disease.

Sickle cell disease (SCD) is a genetic disorder causing blood vessel blockages and painful Vaso-occlusive crises (VOCs). VOCs, characterized by severe pain due to blocked blood flow, are recurrent and unpredictable, posing challenges for preventive strategies. In this study we propose explosive synchronization (ES), a phenomenon characterized by abrupt brain network phase transitions, as a novel approach to address this challenge. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) and VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited significantly lower alpha wave frequency than controls. SCD patients under painful pressure stimulation showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, patients who had a higher frequency of VOCs in the preceding 12 months presented with stronger FDA. The timing of VOC occurrence relative to EEG recordings was significantly associated to FDA. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Stronger FDA correlated with higher responsivity and complexity in our model. Simulation under noisy environment showed that higher FDA could be linked to increased occurrence frequency of crisis. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.

Network Model With Reduced Metabolic Rate Predicts Spatial Synchrony of Neuronal Activity.

In a cerebral hypometabolic state, cortical neurons exhibit slow synchronous oscillatory activity with sparse firing. How such a synchronization spatially organizes as the cerebral metabolic rate decreases have not been systemically investigated. We developed a network model of leaky integrate-and-fire neurons with an additional dependency on ATP dynamics. Neurons were scattered in a 2D space, and their population activity patterns at varying ATP levels were simulated. The model predicted a decrease in firing activity as the ATP production rate was lowered. Under hypometabolic conditions, an oscillatory firing pattern, that is, an ON-OFF cycle arose through a failure of sustainable firing due to reduced excitatory positive feedback and rebound firing after the slow recovery of ATP concentration. The firing rate oscillation of distant neurons developed at first asynchronously that changed into burst suppression and global synchronization as ATP production further decreased. These changes resembled the experimental data obtained from anesthetized rats, as an example of a metabolically suppressed brain. Together, this study substantiates a novel biophysical mechanism of neuronal network synchronization under limited energy supply conditions.

Diversity of functional connectivity patterns is reduced in propofol-induced unconsciousness.

INTRODUCTION: Recent evidence suggests that the conscious brain is characterized by a diverse repertoire of functional connectivity patterns while the anesthetized brain shows stereotyped activity. However, classical time-averaged methods of connectivity dismiss dynamic and temporal characteristics of functional configurations. Here we demonstrate a new approach which characterizes time-varying patterns of functional connectivity at the subsecond time scale. METHODS: We introduce phase-lag entropy (PLE), a measure of the diversity of temporal patterns in the phase relationships between two signals. The proposed measure was applied to multichannel electroencephalogram (EEG), which were recorded from two distinct experimental settings: (1) propofol was administrated at a constant infusion rate for 60 min (n = 96); (2) administration of propofol by a target effect-site concentration-controlled infusion with simultaneous assessment of the level of consciousness (n = 10). RESULTS: From the first dataset, two substantial changes of the phase relationship during anesthesia was found: (1) the dynamics of the phase relationship between frontal channels became progressively less diverse and more stereotyped during unconsciousness, quantified as a reduction in PLE; and (2) the reduction in PLE was consistent across subjects. Furthermore, PLE provided better performance in the classification of states of consciousness than did phase-lag index, a classical time-averaged connectivity method. From the second dataset, PLE showed the highest agreement with the level of consciousness, compared to existing anesthetic depth indicators. CONCLUSIONS: This study suggests that a scarcity of functional configurations is closely associated with anesthetically induced unconsciousness, and shows promise as a basis for a new consciousness monitoring system during general anesthesia. Hum Brain Mapp 38:4980-4995, 2017. © 2017 Wiley Periodicals, Inc.

Suppressed neural complexity during ketamine- and propofol-induced unconsciousness.

Ketamine and propofol have distinctively different molecular mechanisms of action and neurophysiological features, although both induce loss of consciousness. Therefore, identifying a common feature of ketamine- and propofol-induced unconsciousness would provide insight into the underlying mechanism of losing consciousness. In this study we search for a common feature by applying the concept of type-II complexity, and argue that neural complexity is essential for a brain to maintain consciousness. To test this hypothesis, we show that complexity is suppressed during loss of consciousness induced by ketamine or propofol. We analyzed the randomness (type-I complexity) and complexity (type-II complexity) of electroencephalogram (EEG) signals before and after bolus injection of ketamine or propofol. For the analysis, we use Mean Information Gain (MIG) and Fluctuation Complexity (FC), which are information-theory-based measures that quantify disorder and complexity of dynamics respectively. Both ketamine and propofol reduced the complexity of the EEG signal, but ketamine increased the randomness of the signal and propofol decreased it. The finding supports our claim and suggests EEG complexity as a candidate for a consciousness indicator.