Integrative Models of Brain Structure and Dynamics: Concepts, Challenges, and Methods.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Brain function arises from complex interactions between regions, constrained by anatomical architecture.
• Neural plasticity at the microscopic level enables coordinated dynamics at mesoscopic and macroscopic scales.
• The interplay between neural network structure and intrinsic dynamics across multiple timescales remains poorly understood.

Purpose of the Study:

• To review theoretical and empirical studies on the coupling between brain structure and dynamics.
• To elucidate the mechanisms governing the reciprocal relationship between neural network structure and intrinsic dynamics.
• To discuss clinically relevant dimensions of brain connectivity, such as topological features and neural synchronization.

Main Methods:

• Review of neuroimaging studies (dMRI, EEG, MEG, fMRI) investigating macroscopic structural and functional connectivity.
• Analysis of large-scale oscillatory dynamics models conditioned on network structure.
• Discussion of theoretical frameworks for understanding brain structure-dynamics interplay.

Main Results:

• Empirical studies report statistical associations between macroscopic structural and functional connectivity.
• Models of large-scale dynamics offer insights into structure-dynamics relationships at the mesoscopic level.
• Progress has been made in understanding connectivity dimensions like topology and synchronization.

Conclusions:

• Understanding the structure-dynamics relationship is crucial for deciphering brain function.
• Multi-modal neuroimaging analyses are promising for future research.
• Challenges remain in integrating findings across different spatial and temporal scales.