Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape

S. Parker Singleton (), Andrea I. Luppi, Robin L. Carhart-Harris, Josephine Cruzat, Leor Roseman, David J. Nutt, Gustavo Deco, Morten L. Kringelbach, Emmanuel A. Stamatakis and Amy Kuceyeski
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S. Parker Singleton: Cornell University
Andrea I. Luppi: University of Cambridge
Robin L. Carhart-Harris: Imperial College London
Josephine Cruzat: Universidad Adolfo Ibanez
Leor Roseman: Imperial College London
David J. Nutt: Imperial College London
Gustavo Deco: Universitat Pompeu Fabra
Morten L. Kringelbach: University of Oxford
Emmanuel A. Stamatakis: University of Cambridge
Amy Kuceyeski: Cornell University

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Psychedelics including lysergic acid diethylamide (LSD) and psilocybin temporarily alter subjective experience through their neurochemical effects. Serotonin 2a (5-HT2a) receptor agonism by these compounds is associated with more diverse (entropic) brain activity. We postulate that this increase in entropy may arise in part from a flattening of the brain’s control energy landscape, which can be observed using network control theory to quantify the energy required to transition between recurrent brain states. Using brain states derived from existing functional magnetic resonance imaging (fMRI) datasets, we show that LSD and psilocybin reduce control energy required for brain state transitions compared to placebo. Furthermore, across individuals, reduction in control energy correlates with more frequent state transitions and increased entropy of brain state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors (obtained from publicly available positron emission tomography (PET) data under non-drug conditions), we demonstrate an association between the 5-HT2a receptor and reduced control energy. Our findings provide evidence that 5-HT2a receptor agonist compounds allow for more facile state transitions and more temporally diverse brain activity. More broadly, we demonstrate that receptor-informed network control theory can model the impact of neuropharmacological manipulation on brain activity dynamics.

Date: 2022
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DOI: 10.1038/s41467-022-33578-1

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