Coarse graining and criticality in the human connectome

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• The human brain's complexity necessitates network analysis for simplification.
• Current computational methods struggle with large-scale brain networks.
• Severe coarse-graining may impact dynamical behavior and critical phenomena.

Purpose of the Study:

• To investigate the preservation of dynamical behavior and critical phenomena after severe network coarse-graining.
• To explore the feasibility of analyzing simplified brain networks for complex computational models.

Main Methods:

• Applied a modularity-based approach to further coarse-grain the human connectome into a smaller number of modules.
• Compared the dynamical behavior of original and coarse-grained networks.
• Formulated and verified a hypothesis connecting Wilson-Cowan model transitions with Ising model critical temperatures in both network types.

Main Results:

• Qualitative dynamical behavior was preserved in coarse-grained networks, though to a lesser extent.
• A hypothesis linking criticality measures across models was successfully verified on both original and simplified networks.
• Severe coarse-graining allows for the analysis of brain network dynamics and critical phenomena in smaller network models.

Conclusions:

• Severe coarse-graining of the human connectome preserves essential dynamical and critical properties.
• This simplification enables computationally intractable analyses, such as those in Integrated Information Theory and quantum brain models.
• Analyzing smaller, coarse-grained brain networks offers a viable pathway to understanding complex brain functions.