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Controlling the human connectome with spatially diffuse input signals.

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Researchers developed a new brain control model that uses spatially extended inputs, significantly reducing the energy needed for brain state transitions and requiring fewer inputs.

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • The human brain exhibits continuous dynamic activity, transitioning between various brain states.
  • Network control theory offers a framework for analyzing the energy costs of these state transitions.
  • Traditional models assume independent node inputs, ignoring the brain's spatial continuity and limited stimulation specificity.

Purpose of the Study:

  • To adapt network control models to incorporate spatially extended inputs.
  • To investigate how realistic input strategies affect the energy required for brain state transitions.
  • To identify efficient control strategies and their neurobiological correlates.

Main Methods:

  • Adapted network control models to include inputs with influence decaying exponentially with distance.
  • Analyzed the impact of spatially extended inputs on energy requirements for state transitions.
  • Identified near-optimal control strategies and mapped input site density.

Main Results:

  • Spatially extended inputs substantially reduce the energy needed for brain state transitions.
  • Near-optimal control strategies significantly decrease the number of required inputs (up to two orders of magnitude).
  • Maps of optimal input site density align with independent functional, metabolic, genetic, and neurochemical maps.

Conclusions:

  • Incorporating spatially extended inputs provides a more realistic and energy-efficient framework for brain control.
  • This approach leverages spatial dependencies in brain connectivity and activity.
  • The findings offer a neurobiologically grounded method for understanding and controlling brain dynamics.