LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics
In MEG data from 16 healthy participants given intravenous LSD, the study shows LSD reduces the asymmetry of directed functional connectivity, flattening the hierarchy of senders and receivers across the brain. A hierarchy-based metric also discriminates LSD from placebo with higher accuracy using machine learning than traditional functional connectivity measures.
Authors
- Suresh Muthukumaraswamy
- Robin Carhart-Harris
- David Nutt
Published
Abstract
Psychedelics are serotonergic drugs that profoundly alter consciousness, yet their neural mechanisms are not fully understood. A popular theory, RElaxed Beliefs Under pSychedelics (REBUS), posits that psychedelics flatten the hierarchy of information flow in the brain. Here, we investigate hierarchy based on the imbalance between sending and receiving brain signals, as determined by directed functional connectivity. We measure directed functional hierarchy in a magnetoencephalography (MEG) dataset of 16 healthy human participants who were administered a psychedelic dose (75 micrograms, intravenous) of lysergic acid diethylamide (LSD) under four different conditions. LSD diminishes the asymmetry of directed connectivity when averaged across time. Additionally, we demonstrate that machine learning classifiers distinguish between LSD and placebo more accurately when trained on one of our hierarchy metrics than when trained on traditional measures of functional connectivity. Taken together, these results indicate that LSD weakens the hierarchy of directed connectivity in the brain by increasing the balance between senders and receivers of neural signals.
Research Summary of 'LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics'
Introduction
The brain's resting-state dynamics are characterised by a hierarchical architecture in which directed information flow — the asymmetric propagation of neural activity across cortical gradients from sensory to association regions — encodes the structured organisation of cognition and perception. Entropy production, quantifiable as the degree of temporal irreversibility in neural signals, provides an index of the metabolic cost and directionality of this information processing. Classical psychedelic drugs such as LSD have been proposed, under the REBUS (Relaxed Beliefs Under Psychedelics) hypothesis, to flatten this cortical hierarchy by reducing top-down predictive constraints and increasing the relative influence of bottom-up sensory signals. This study aimed to directly test the REBUS hypothesis by measuring changes in directed information flow and hierarchical coherence under LSD using magnetoencephalography (MEG) and an INSIDEOUT irreversibility framework, in which temporal asymmetry in neural dynamics serves as a proxy for directed, entropy-producing information processing across the whole brain.
Expert Research Summaries
Go Pro to access AI-powered section-by-section summaries, editorial takes, and the full research toolkit.
Full Text PDF
Full Paper PDF
Create a free account to open full-text PDFs.
Study Details
- Study Typeindividual
- Journal
- Compound
- Topic
- Authors
- APA Citation
Shinozuka, K., Tewarie, P. K., Luppi, A., Lynn, C., Roseman, L., Muthukumaraswamy, S., Nutt, D. J., Carhart-Harris, R., Deco, G., & Kringelbach, M. L. (2024). LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics. https://doi.org/10.1101/2024.04.25.591130
References (47)
Papers cited by this study that are also in Blossom
Alamia, A., Timmermann, C., Carhart-Harris, R. L. · eLife (2020)
Alonso, J. N., Romero, S., Mañanas, M. A. et al. · International Journal of Neuropsychopharmacology (2015)
Atasoy, S., Leor, R., Kaelen, M. et al. · Scientific Reports (2017)
Barnett, L., Muthukumaraswamy, S., Carhart-Harris, R. L. · NeuroImage (2020)
Barrett, F. S., Doss, M. K., Sepeda, N. D. et al. · Scientific Reports (2020)
Barrett, F. S., Krimmel, S. R., Griffiths, R. R. et al. · NeuroImage (2020)
Bedford, P., Hauke, D. J., Wang, Z. et al. · Neuropsychopharmacology (2022)
Carhart-Harris, R. L., Erritzoe, D., Williams, T. et al. · PNAS (2012)
Carhart-Harris, R. L., Friston, K. J. · Pharmacological Reviews (2019)
Carhart-Harris, R. L., Muthukumaraswamy, S., Roseman, L. et al. · PNAS (2016)
Show all 47 referencesShow fewer
Carhart-Harris, R. L., Roseman, L., Bolstridge, M. et al. · Scientific Reports (2017)
Carhart-Harris, R. L., Leech, R., Shanahan, M. et al. · Frontiers in Human Neuroscience (2014)
Cavarra, M., Falzone, A., Ramaekers, J. G. et al. · Frontiers in Psychology (2022)
Dai, R., Larkin, T. E., Huang, Z. et al. · NeuroImage (2023)
De Araujo, D. B., Ribeiro, S., Cecchi, G. A. et al. · Human Brain Mapping (2011)
Doss, M. K., Považan, M., Rosenberg, M. D. et al. · Translational Psychiatry (2021)
Girn, M., Roseman, L., Bernhardt, B. et al. · NeuroImage (2022)
Grimm, O., Kraehenmann, R., Preller, K. H. et al. · European Neuropsychopharmacology (2018)
Jobst, B. M., Atasoy, S., Ponce-Alvarez, A. et al. · NeuroImage (2021)
Kaelen, M., Lorenz, R., Barrett, F. S. et al. · Biorxiv (2017)
Kaelen, M., Roseman, L., Kahan, J. et al. · European Neuropsychopharmacology (2016)
Kraehenmann, R., Schmidt, A., Friston, K. et al. · NeuroImage (2015)
Lebedev, A. V., Kaelen, M., L€ Ovd En, M. et al. · Human Brain Mapping (2016)
Lebedev, A. V., L€ Ovd En, M., Rosenthal, G. et al. · Human Brain Mapping (2015)
López-Giménez, J. F., González-Maeso, J. · Current Topics in Behavioral Neurosciences (2017)
Luppi, A. I., Carhart-Harris, R. L., Roseman, L. et al. · NeuroImage (2021)
Madsen, M. K., Stenbaek, D. S., Arvidsson, A. et al. · European Neuropsychopharmacology (2021)
Mason, N. L., Kuypers, K. P. C., Reckweg, J. et al. · Neuropsychopharmacology (2020)
McCulloch, D. E-W., Olsen, A. S., Ozenne, B. et al. · MedRvix (2023)
Mediano, P. A. M., Rosas, F. E., Timmermann, C. et al. · ACS Chemical Neuroscience (2024)
Nichols, D. E. · Pharmacological Reviews (2016)
Palhano-Fontes, F., Andrade, K. C., Tófoli, L.F. et al. · PLOS ONE (2015)
Pallavicini, C., Vilas, M. G., Villarreal, M. et al. · NeuroImage (2019)
Preller, K. H., Razi, A., Zeidman, P. et al. · PNAS (2019)
Roseman, L., Leech, R., Feilding, A. et al. · Frontiers in Human Neuroscience (2014)
Roseman, L., Sereno, M. I., Leech, R. et al. · Human Brain Mapping (2016)
Ruffini, G., Damiani, G., Lozano-Soldevilla, D. et al. · PLOS ONE (2023)
Schartner, M., Carhart-Harris, R. L., Barrett, A. B. et al. · Scientific Reports (2017)
Singleton, S. P., Luppi, A. I., Carhart-Harris, R. L. et al. · Nature Communications (2022)
Tagliazucchi, E., Roseman, L., Kaelen, M. et al. · Current Biology (2016)
Timmermann, C., Roseman, L., Haridas, S. et al. · PNAS (2023)
Timmermann, C., Roseman, L., Schartner, M. et al. · Scientific Reports (2019)
Timmermann, C., Spriggs, M. J., Kaelen, M. et al. · Neuropharmacology (2018)
Varley, T. F., Carhart-Harris, R., Roseman, L. et al. · NeuroImage (2020)
Viol, A., Palhano-Fontes, F., Onias, H. et al. · Scientific Reports (2017)
Viol, A., Viswanathan, G. M., Soldatkina, O. et al. · Journal of Physiology (2023)
Vollenweider, F. X., Vollenweider-Scherpenhuyzen, M. F. I., Bäbler, A. et al. · NeuroReport (1998)
Cited By (1)
Papers in Blossom that reference this study
Coleman, J. A., Shinozuka, K., Tromm, R. et al. · Human Brain Mapping (2025)
Your Personal Research Library
Go Pro to save papers, add notes, rate studies, and organize your research into custom shelves.