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Computational neuroscience advances brain state definitions and control. New models predict interventions to rebalance brain dynamics for treating neurological disorders.

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Area of Science:

  • Computational Neuroscience
  • Neuroscience
  • Systems Neuroscience

Background:

  • The human brain is a complex dynamic system with potential for therapeutic rebalancing via pharmacological or electromagnetic perturbation.
  • Significant challenges exist in predicting effective brain perturbation strategies due to difficulties in defining brain states.

Purpose of the Study:

  • To review the state of the art in defining brain states and controlling transitions.
  • To propose a framework for understanding the functional hierarchical organization of brain states.
  • To highlight advances in whole-brain computational models for predicting therapeutic interventions.

Main Methods:

  • Reviewing current computational neuroscience literature on brain state definition and control.
  • Developing a framework for functional hierarchical organization of brain states.
  • Utilizing sophisticated whole-brain computational models with interacting neuronal and neurotransmitter systems.

Main Results:

  • Progress has been made in robustly defining brain states and inducing transitions.
  • A framework for describing the functional hierarchical organization of brain states is proposed.
  • Advanced computational models allow for in silico prediction of interventions.

Conclusions:

  • Defining brain states is crucial for developing targeted interventions.
  • Computational models offer a powerful platform for designing pharmacological and electromagnetic therapies.
  • This work facilitates the design of novel interventions to rebalance brain dynamics in disease.