Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity.

Area of Science:

• Neuroscience
• Computational Biology
• Systems Biology

Background:

• Nervous system connectivity is shaped by evolutionary pressures balancing wiring costs and functional needs.
• Stochastic models have partially reproduced human connectome topology from diffusion MRI, based on connection cost-value trade-offs.

Purpose of the Study:

• To develop a new formalism for modeling brain connectivity that better balances wiring cost minimization and topological complexity.
• To investigate the impact of developmental changes and genetic factors on macroscale brain network organization.

Main Methods:

• Derived a novel mathematical formalism to model competing pressures in neural wiring.
• Incorporated developmental changes in brain geometry and wiring costs into models.
• Utilized interregional transcriptional and microstructural similarity as alternative wiring rules.
• Compared model performance against empirical human connectome data from diffusion MRI.

Main Results:

• The new formalism more accurately captured competing pressures than previous models.
• Accounting for developmental geometry and genetic similarity improved model performance.
• Models successfully reproduced topological properties but failed to capture topographical (spatial) network features.
• Stochastic models were found to provide an incomplete explanation for connectome organization.

Conclusions:

• Genetics plays a significant role in shaping macroscale brain connectivity.
• Current stochastic models do not fully account for the spatial organization of the human connectome.
• Future models may need to integrate developmental, genetic, and spatial constraints more effectively.