Abstract
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.
Highlights
Neuronal activity in the brain is tightly coupled to the level of cerebral energy metabolism
In order to examine the effect of the brain metabolic rate on spatial synchronization, we developed a novel neuronal network model that incorporated a varying rate of ATP production
Our model study showed that a decrease in ATP production rate can enhance synchronization in the neuronal network, which starts with local weak synchronization, that is the fragmented slow oscillation (FSO), that gradually evolves into strong global synchronization
Summary
Neuronal activity in the brain is tightly coupled to the level of cerebral energy metabolism. In electroencephalogram (EEG) recordings, firing bursts are reflected in the appearance of slow (0.1–1 Hz) oscillation or, in more deeply suppressed states, by burst suppression, a phenomenon of transient electrocortical activity alternating with electrical silence. These phenomena are commonly observed in general anesthesia, comas, and hypothermia (Brown et al, 2010; Westover et al, 2015), all of which are associated with reduced brain metabolism. Several prior studies have attempted to computationally model the effect of varying cerebral energy metabolism on neuronal dynamics consistent with experimental observations. Burst suppression was effectively modeled with an interaction between neuronal dynamics and brain metabolism (Ching et al, 2012)
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