Simple models of human brain functional networks.

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• Human brain functional networks exhibit complex topological properties similar to other complex systems.
• The evolutionary pressures shaping brain network topology are not well understood.

Purpose of the Study:

• To propose and validate a generative model for human brain functional networks.
• To identify key factors driving the formation of brain network topology.

Main Methods:

• Developed a generative model incorporating distance-based connection costs and input-similarity-based clustering.
• Estimated model parameters using functional magnetic resonance imaging (fMRI) data from healthy individuals.
• Tested model performance on independent fMRI datasets and simulated networks of individuals with schizophrenia.

Main Results:

• The proposed model successfully captures key topological properties of functional brain networks.
• Model parameters derived from one dataset generalize well to independent datasets.
• The model can simulate abnormal network properties observed in schizophrenia.

Conclusions:

• A simple economical clustering rule, balancing connection costs and input similarity, can explain brain network organization.
• This model provides a parsimonious explanation for brain network topology in both health and disease states.
• The findings offer insights into the fundamental principles governing brain connectivity.