bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.05.14.444193

LSD flattens the brain's energy landscape: evidence from receptor-informed network control theory

Singleton, S. P.·Luppi, A. I.·Carhart-Harris, R. L.·Cruzat, J.·Roseman, L.·Deco, G.·Kringelbach, M. L.·Stamatakis, E. A.·Kuceyeski, A.
View original source ↗Export metadata ↓

Abstract

Psychedelics like lysergic acid diethylamide (LSD) and psilocybin offer a powerful window into the function of the human brain and mind, by temporarily altering subjective experience through their neurochemical effects. A recent model postulates that serotonin 2a (5-HT2a) receptor agonism allows the brain to explore its dynamic landscape more readily, as reflected by more diverse (entropic) brain activity. We postulate that this increase in entropy may arise in part from a flattening of the brains control energy landscape, which can be observed using network control theory to quantify the energy required to transition between recurrent brain states measured using functional magnetic resonance imaging (fMRI) in individuals under LSD, psilocybin, and placebo conditions. We show that LSD and psilocybin reduce the amount of control energy required for brain state transitions, and, furthermore, that, across individuals, LSDs 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 from publicly available (non-drug) positron emission tomography (PET) maps, we demonstrate the specific role of this receptor in reducing 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, by combining receptor-informed network control theory with pharmacological modulation, our work highlights the potential of this approach in studying the impacts of targeted neuropharmacological manipulation on brain activity dynamics. Significance StatementWe present a multi-modal framework for quantifying the effects of two psychedelic drugs (LSD and psilocybin) on brain dynamics by combining functional magnetic resonance imaging (fMRI), diffusion MRI (dMRI), positron emission tomography (PET) and network control theory. Our findings provide evidence that psychedelics flatten the brains control energy landscape, allowing for more facile state transitions and more temporally diverse brain activity. We also demonstrate that the spatial distribution of serotonin 2a receptors - the main target of LSD and psilocybin - is optimized for generating these effects. This approach could be used to understand how drugs act on different receptors in the brain to influence brain activity dynamics.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Singleton, S. P., Luppi, A. I., Carhart-Harris, R. L., Cruzat, J., Roseman, L., Deco, G., Kringelbach, M. L., Stamatakis, E. A., Kuceyeski, A.. 2021-05-17. LSD flattens the brain's energy landscape: evidence from receptor-informed network control theory. https://doi.org/10.1101/2021.05.14.444193

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗
bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗
bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗