reverse-engineering-brain-control-nodes

What problem does it solve?

Identifies sparse brain control nodes and their inputs to reconstruct neural dynamics from task-based fMRI data, enabling targeted insights into how specific regions drive cognitive and motor tasks.

Core Features & Use Cases

• Linear state-space model: X(t+1) = A X(t) + B u(t) to describe neural dynamics.
• Sparse input optimization: jointly identifies a minimal set of control nodes (B) and inputs (u) that explain observed activity.
• Brain mapping utility: links identified control nodes to known functional systems for interpretation.
• Use cases include task-based fMRI analysis, neuromodulation targeting, cognitive process modeling, and network-control applications.

Quick Start

Apply the framework to task-based fMRI data to identify sparse control nodes that reconstruct neural dynamics.