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Brain States and Transitions: Insights from Computational Neuroscience
Author(s) -
Morten L. Kringelbach,
Gustavo Deco
Publication year - 2020
Publication title -
cell reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.264
H-Index - 154
eISSN - 2639-1856
pISSN - 2211-1247
DOI - 10.1016/j.celrep.2020.108128
Subject(s) - neuroscience , computational neuroscience , computational model , computer science , cognitive science , systems neuroscience , state (computer science) , systems biology , artificial intelligence , psychology , biology , computational biology , central nervous system , algorithm , myelin , oligodendrocyte
Within the field of computational neuroscience there are great expectations of finding new ways to rebalance the complex dynamic system of the human brain through controlled pharmacological or electromagnetic perturbation. Yet many obstacles remain between the ability to accurately predict how and where best to perturb to force a transition from one brain state to another. The foremost challenge is a commonly agreed definition of a given brain state. Recent progress in computational neuroscience has made it possible to robustly define brain states and force transitions between them. Here, we review the state of the art and propose a framework for determining the functional hierarchical organization describing any given brain state. We describe the latest advances in creating sophisticated whole-brain computational models with interacting neuronal and neurotransmitter systems that can be studied fully in silico to predict and design novel pharmacological and electromagnetic interventions to rebalance them in disease.

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