Structure-informed functional connectivity driven by identifiable and state-specific control regions.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Coordinated neural activity underlies complex behavior, but its relationship with brain's anatomical wiring is not fully understood.
• Observed functional connectivity (FC) patterns do not always align with direct anatomical connections.
• Dynamic adjustments in neural activity with task demands pose a challenge to static anatomical explanations.

Purpose of the Study:

• To propose a novel model explaining functional connectivity (FC) patterns based on controllable diffusion dynamics on brain architecture.
• To develop a framework for identifying brain control centers responsible for state-specific functional patterns.
• To investigate the role of controllability in brain state transitions.

Main Methods:

• Developed a 'structure-informed' functional connectivity (FC) model using controllable linear diffusion dynamics on brain anatomy.
• Introduced a principled framework to identify potential brain control centers.
• Analyzed empirical functional connectivity patterns against model predictions.

Main Results:

• Identified well-defined, sparse, and robust sets of control regions in the brain.
• Demonstrated that these control regions, with partial overlap across tasks and resting state, can generate empirical FC patterns.
• Showed that controllable diffusion dynamics can explain observed functional connectivity.

Conclusions:

• Controllable linear diffusion dynamics on brain architecture can explain functional connectivity patterns.
• Specific sets of control regions are crucial for generating state-specific functional connectivity.
• Brain controllability is a fundamental mechanism enabling transitions between different neural states.