Metabolic constraints shape hypersynchronous dynamics in spiking cortical microcircuit models

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Neuroenergetics

Background:

• Neuronal function relies on ATP-dependent ion homeostasis.
• Understanding how metabolic stress impacts large-scale cortical network dynamics is challenging.
• Existing seizure models lack direct links between cellular energy and network activity.

Purpose of the Study:

• To investigate if reduced intracellular energy availability can destabilize cortical activity and induce seizure-like synchronization.
• To develop a computational framework linking energy metabolism to neuronal excitability and network dynamics.
• To explore the effects of metabolic stress on cortical network stability and seizure generation.

Main Methods:

• Extended the Adaptive Exponential Integrate-and-Fire model with an energy variable.
• Incorporated explicit ATP production and consumption terms.
• Fitted layer-specific parameters to human cortical recordings and embedded the model in a laminar microcircuit.

Main Results:

• Reduced ATP production induced a shift from asynchronous activity to a burst-synchronized state with low firing rates and high-amplitude oscillations.
• Increased inhibitory conductance suppressed burst synchrony under metabolic stress but did not restore the metabolic state.
• Parameter sweeps indicated history-dependent changes in burst synchrony, particularly with inhibitory modulation.

Conclusions:

• Coupling intracellular energy availability to neuronal excitability is sufficient to destabilize cortical networks and generate seizure-like activity.
• Inhibition can suppress seizure-like activity in a low-energy state, highlighting a distinction between electrical suppression and metabolic recovery.
• The developed spiking-network model provides a framework for studying metabolic constraints on cortical stability and seizure dynamics.