Controlling the human connectome with spatially diffuse input signals.

Area of Science:

• Neuroscience
• Network Science
• Control Theory

Background:

• The human brain exhibits continuous dynamic transitions between whole-brain activity states.
• Network control theory provides a framework for understanding the energetic costs of these state transitions.
• Current models often treat control inputs as localized, neglecting the spatial spread of signals in the brain.

Purpose of the Study:

• To adapt network control models to incorporate spatially extended inputs that decay exponentially from their origin.
• To investigate if this more realistic input strategy can reduce the energy required for brain state transitions.
• To explore near-optimal control strategies and their neurobiological plausibility.

Main Methods:

• Modified network control theory to include spatially decaying input signals.
• Analyzed the energy efficiency of state transitions using these extended inputs.
• Compared derived input site density maps with existing neurobiological data.

Main Results:

• Spatially extended inputs significantly reduce the energy (effort) needed for brain state transitions.
• Near-optimal control strategies require substantially fewer input signals, sometimes by two orders of magnitude.
• Derived input site density maps show close correspondence with functional, metabolic, genetic, and neurochemical brain maps.

Conclusions:

• Proposes a more efficient and neurobiologically realistic framework for network control of brain states.
• Highlights the importance of spatial spread and connectivity in optimizing brain control strategies.
• Suggests neurobiologically grounded mechanisms for achieving optimal control in the brain.