The global dynamical complexity of the human brain network.

Area of Science:

• Computational neuroscience
• Network science
• Information theory

Background:

• Quantifying integrated information in large-scale brain networks is computationally challenging.
• Existing measures of dynamical complexity face normalization and computational feasibility issues for complex networks.

Purpose of the Study:

• To develop a computationally feasible method for quantifying integrated information in large brain networks.
• To couple network information integration with brain function using information-theoretic measures.

Main Methods:

• Formulated network integrated information using Kullback-Leibler divergence between network and factorized distributions.
• Utilized maximum information partition to optimize computations for large networks.
• Applied the formalism to linear stochastic dynamics and attractor/non-stationary states.

Main Results:

• Developed a computationally efficient approach for calculating network integrated information.
• Demonstrated suitability for large networks with linear stochastic dynamics.
• Computed integrated information for human brain connectome's attractor and non-stationary states.

Conclusions:

• The human brain's specific topology generates greater information complexity compared to random networks.
• The proposed method offers a feasible way to assess information integration in complex brain networks.
• This approach can link network dynamics to brain function through quantifiable information measures.