brain-network-controllability

What problem does it solve?

Addresses the need to quantify and analyze how brain networks can be steered between states using network control theory, providing actionable metrics and insights for neuroscience research.

Core Features & Use Cases

• Compute average controllability and modal controllability for network nodes to identify key control points in brain structure.
• Evaluate minimum control energy and trajectories to understand the effort required to shift brain states in modeling experiments.
• Modular network generation and exploration to study how modular topology affects controllability and control strategies in neural systems.
• Use Case: A cognitive neuroscience study investigates how network topology influences the ability to transition from a resting state to a task-related state and uses AC, MC, and energy analyses to interpret results.

Quick Start

Compute AC, MC, and minimum energy for a given brain network matrix A using BrainNetworkControllability.