Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain's control energy landscape.
S Parker SingletonAndrea I LuppiRobin L Carhart-HarrisJosephine CruzatLeor RosemanDavid John NuttGustavo DecoMorten L KringelbachEmmanuel Andreas StamatakisAmy F KuceyeskiPublished in: Nature communications (2022)
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.
Keyphrases
- resting state
- positron emission tomography
- magnetic resonance imaging
- white matter
- computed tomography
- functional connectivity
- multiple sclerosis
- cerebral ischemia
- magnetic resonance
- depressive symptoms
- machine learning
- gold nanoparticles
- deep learning
- quantum dots
- artificial intelligence
- study protocol
- sleep quality