A measure for brain complexity: relating functional segregation and integration in the nervous system

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Higher vertebrate brains exhibit a dichotomy between local functional segregation and global integration.
• Understanding the interplay between segregation and integration is key to deciphering brain organization and function.

Purpose of the Study:

• Introduce a novel quantitative measure, neural complexity (CN), to capture the balance between functional segregation and integration in neural systems.
• Investigate how neuroanatomical organization principles influence brain dynamics using the developed complexity measure.

Main Methods:

• Defined neural complexity (CN) based on statistical independence of neural system subsets of varying sizes.
• Estimated CN by averaging deviations from statistical independence across increasing subset sizes.
• Utilized computer simulations of cortical areas to model neuroanatomical connectivity patterns.

Main Results:

• Neural complexity (CN) is high when functional segregation and integration coexist, and low when systems are purely segregated or integrated.
• Simulations demonstrated that specific cortical connectivity patterns (high density, local clustering, patchiness, reciprocal connections) are associated with high CN values.
• The findings suggest a link between structural neuroanatomy and dynamic brain states.

Conclusions:

• Neural complexity (CN) provides a valuable metric for quantifying the balance between segregation and integration in neural systems.
• Cortical connectivity patterns are critical determinants of neural complexity.
• The developed approach may be applicable to analyzing complexity in other biological systems, such as gene regulation and embryogenesis.