Structural Modularity Tunes Mesoscale Criticality in Biological Neuronal Networks

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Biological neuronal networks are suggested to self-organize towards a critical state for stable recruitment dynamics.
• Neuronal avalanches, where individual neurons activate one further neuron, characterize this critical state.
• Explosive recruitment in local circuits (neocortical minicolumns, neuronal clusters) appears supercritical, creating a paradox with mesoscale criticality.

Purpose of the Study:

• To investigate how neuronal networks reconcile supercritical local dynamics with mesoscale criticality.
• To provide experimental support for theoretical models proposing modular networks resolve this inconsistency.
• To explore the role of structural self-organization in tuning network dynamics.

Main Methods:

• Cultured rat cortical neurons (either sex) were used to manipulate structural self-organization.
• The effect of increasing network clustering on neuronal avalanche size distributions was analyzed.
• Avalanche size distributions were measured to assess transitions from supercritical to subcritical activity dynamics.

Main Results:

• Increasing network clustering correlated with a transition from supercritical to subcritical avalanche size distributions.
• Moderately clustered networks exhibited avalanche size distributions approximating a power law, indicating overall critical recruitment.
• This suggests that activity-dependent self-organization tunes inherently supercritical networks toward mesoscale criticality via modular structures.

Conclusions:

• Modularity in neuronal networks plays a key role in tuning critical recruitment dynamics at the mesoscale.
• This reconciles findings of supercritical local circuit dynamics with mesoscopic criticality.
• Altered mesoscale organization is relevant to neuropathological diseases studied within the criticality framework.