Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Psilocybin-enhanced fear extinction linked to bidirectional modulation of cortical ensembles

Nature Neuroscience volume 28, pages 1311–1326 (2025)Cite this article

Subjects

Abstract

The psychedelic drug psilocybin demonstrates rapid and long-lasting efficacy across neuropsychiatric disorders that are characterized by behavioral inflexibility. However, its impact on the neural activity underlying sustained changes in behavioral flexibility has not been characterized. To test whether psilocybin enhances behavioral flexibility by altering activity in cortical neural ensembles, we performed longitudinal single-cell calcium imaging in the mouse retrosplenial cortex across a 5-day trace fear learning and extinction assay. We found that a single dose of psilocybin altered cortical ensemble turnover and oppositely modulated fear- and extinction-active neurons. Suppression of fear-active neurons and recruitment of extinction-active neurons predicted psilocybin-enhanced fear extinction. In a computational model of this microcircuit, inhibition of simulated fear-active units modulated recruitment of extinction-active units and behavioral variability in freezing, aligning with experimental results. These results suggest that psilocybin enhances behavioral flexibility by recruiting new neuronal populations and suppressing fear-active populations in the retrosplenial cortex.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Psilocybin enhances TFC extinction in a subgroup of low-freezing mice.
Fig. 2: Psilocybin alters dynamics and encoding of freezing behavior.
Fig. 3: TCA captures evolution of RSC through different task-relevant states over learning.
Fig. 4: Turnover in the dominant neural ensembles driving RSC dynamics predicts fear extinction.
Fig. 5: Psilocybin bidirectionally modulates neural ensembles driving RSC dynamics during TFC in responders.
Fig. 6: Psilocybin induces long-term suppression of Acq-dominant neurons and strong post-acute recruitment of Ext3-dominant neurons in responders.
Fig. 7: A computational model of a two-ensemble RSC microcircuit explains psilocybin’s effects.
Fig. 8: Psilocybin acutely suppresses Acq-dominant neurons, unveiling persistent recruitment of the Ext3-dominant ensemble and enhancing fear extinction.

Similar content being viewed by others

Data availability

The original videos and datasets generated during and/or analyzed during the current study comprise a 5TB dataset and are available from the corresponding authors. Processed data are available on GitHub (https://github.com/sarogers9/Rogers_et_al_2024). Source data are provided with this paper.

Code availability

Custom code generated for this paper is available on GitHub (https://github.com/sarogers9/Rogers_et_al_2024).

References

  1. Castaldelli-Maia, J. M. & Bhugra, D. Analysis of global prevalence of mental and substance use disorders within countries: focus on sociodemographic characteristics and income levels. Int. Rev. Psychiatry 34, 6–15 (2022).

    Article  PubMed  Google Scholar 

  2. Nutt, D. & Carhart-Harris, R. The current status of psychedelics in psychiatry. JAMA Psychiatry 78, 121–122 (2021).

    Article  PubMed  Google Scholar 

  3. Griffiths, R. R. et al. Psilocybin occasioned mystical-type experiences: immediate and persisting dose-related effects. Psychopharmacology 218, 649–665 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Agin-Liebes, G. I. et al. Long-term follow-up of psilocybin-assisted psychotherapy for psychiatric and existential distress in patients with life-threatening cancer. J. Psychopharmacol. 34, 155–166 (2020).

    Article  PubMed  Google Scholar 

  5. Aday, J. S., Mitzkovitz, C. M., Bloesch, E. K., Davoli, C. C. & Davis, A. K. Long-term effects of psychedelic drugs: a systematic review. Neurosci. Biobehav. Rev. 113, 179–189 (2020).

    Article  CAS  PubMed  Google Scholar 

  6. Gukasyan, N. et al. Efficacy and safety of psilocybin-assisted treatment for major depressive disorder: prospective 12-month follow-up. J. Psychopharmacol. 36, 151–158 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hesselgrave, N., Troppoli, T. A., Wulff, A. B., Cole, A. B. & Thompson, S. M. Harnessing psilocybin: antidepressant-like behavioral and synaptic actions of psilocybin are independent of 5-HT2R activation in mice. Proc. Natl. Acad. Sci. USA 118, e2022489118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Cameron, L. P. et al. 5-HT2ARs mediate therapeutic behavioral effects of psychedelic tryptamines. ACS Chem. Neurosci. 14, 351–358 (2023).

    Article  CAS  PubMed  Google Scholar 

  9. Hibicke, M., Kramer, H. M. & Nichols, C. D. A single administration of psilocybin persistently rescues cognitive deficits caused by adolescent chronic restraint stress without long-term changes in synaptic protein gene expression in a rat experimental system with translational relevance to depression. Psychedelic Med. 1, 54–67 (2023).

    Article  CAS  Google Scholar 

  10. Hernandez-Leon, A. et al. Antidepressant- and anxiolytic-like activities and acute toxicity evaluation of the Psilocybe cubensis mushroom in experimental models in mice. J. Ethnopharmacol. 320, 117415 (2024).

    Article  CAS  PubMed  Google Scholar 

  11. Jones, N. T. et al. Delayed anxiolytic-like effects of psilocybin in male mice are supported by acute glucocorticoid release. Preprint at bioRxiv https://doi.org/10.1101/2020.08.12.248229 (2020).

  12. Jones, N. T. et al. Transient elevation of plasma glucocorticoids supports psilocybin-induced anxiolysis in mice. ACS Pharmacol. Transl. Sci. 6, 1221–1231 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Takaba, R., Ibi, D. & Yoshida, K. et al. Ethopharmacological evaluation of antidepressant-like effect of serotonergic psychedelics in C57BL/6J male mice. Naunyn-Schmiedebergs Arch. Pharmacol. 397, 3019–3035 (2024).

    Article  CAS  PubMed  Google Scholar 

  14. Odland, A. U., Kristensen, J. L. & Andreasen, J. T. Investigating the role of 5-HT2A and 5-HT2C receptor activation in the effects of psilocybin, DOI, and citalopram on marble burying in mice. Behav. Brain Res. 401, 113093 (2021).

    Article  CAS  PubMed  Google Scholar 

  15. Wulff, A. B., Nichols, C. D. & Thompson, S. M. Preclinical perspectives on the mechanisms underlying the therapeutic actions of psilocybin in psychiatric disorders. Neuropharmacology 231, 109504 (2023).

    Article  CAS  PubMed  Google Scholar 

  16. Brownstien, M. et al. Striking long-term beneficial effects of single dose psilocybin and psychedelic mushroom extract in the SAPAP3 rodent model of OCD-like excessive self-grooming. Mol. Psychiatry 30, 1172–1183 (2025).

    Article  CAS  PubMed  Google Scholar 

  17. Passie, T., Seifert, J., Schneider, U. & Emrich, H. M. The pharmacology of psilocybin. Addict. Biol. 7, 357–364 (2002).

    Article  CAS  PubMed  Google Scholar 

  18. Lowe, H. et al. The therapeutic potential of psilocybin. Molecules 26, 2948 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Garcia-Romeu, A., Griffiths, R. R. & Johnson, M. W. Psilocybin-occasioned mystical experiences in the treatment of tobacco addiction. Curr. Drug Abuse Rev. 7, 157–164 (2015).

    Article  PubMed Central  Google Scholar 

  20. Bremler, R., Katati, N., Shergill, P., Erritzoe, D. & Carhart-Harris, R. L. Case analysis of long-term negative psychological responses to psychedelics. Sci. Rep. 13, 15998 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Simonsson, O., Hendricks, P. S., Chambers, R., Osika, W. & Goldberg, S. B. Prevalence and associations of challenging, difficult or distressing experiences using classic psychedelics. J. Affect. Disord. 326, 105–110 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Breeksema, J. J. et al. Adverse events in clinical treatments with serotonergic psychedelics and MDMA: a mixed-methods systematic review. J. Psychopharmacol. 36, 1100–1117 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lewis, C. R., Preller, K. H., Braden, B. B., Riecken, C. & Vollenweider, F. X. Rostral anterior cingulate thickness predicts the emotional psilocybin experience. Biomedicines 8, 34 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Halim, H. J., Burk, B. G., Fargason, R. E. & Birur, B. Manic episode following psilocybin use in a man with bipolar II disorder: a case report. Front. Psychiatry 14, 1221131 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Davis, A. K., Barrett, F. S. & Griffiths, R. R. Psychological flexibility mediates the relations between acute psychedelic effects and subjective decreases in depression and anxiety. J. Contextual Behav. Sci. 15, 39–45 (2020).

    Article  PubMed  Google Scholar 

  26. Doss, M. K. et al. Psilocybin therapy increases cognitive and neural flexibility in patients with major depressive disorder. Transl. Psychiatry 11, 574 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Torrado Pacheco, A., Olson, R. J., Garza, G. & Moghaddam, B. Acute psilocybin enhances cognitive flexibility in rats. Neuropsychopharmacology 48, 1011–1020 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Conn, K. et al. Psilocybin restrains activity-based anorexia in female rats by enhancing cognitive flexibility: contributions from 5-HT1A and 5-HT2A receptor mechanisms. Mol. Psychiatry 29, 3291–3304 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Price, R. B. & Duman, R. Neuroplasticity in cognitive and psychological mechanisms of depression: an integrative model. Mol. Psychiatry 25, 530–543 (2020).

    Article  PubMed  Google Scholar 

  30. Zhukovsky, P. et al. Perseveration in a spatial-discrimination serial reversal learning task is differentially affected by MAO-A and MAO-B inhibition and associated with reduced anxiety and peripheral serotonin levels. Psychopharmacology 234, 1557–1571 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jacobs, D. S., Bogachuk, A. P., Le Moing, C. L. & Moghaddam, B. Effects of psilocybin on uncertain punishment learning. Neurobiol. Learn. Mem. 213, 107954 (2024).

    Article  CAS  PubMed  Google Scholar 

  32. Vargas, M. V. et al. Psychedelics promote neuroplasticity through the activation of intracellular 5-HT2A receptors. Science 379, 700–706 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Moliner, R. et al. Psychedelics promote plasticity by directly binding to BDNF receptor TrkB. Nat. Neurosci. 26, 1032–1041 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Carhart-Harris, R. L. & Friston, K. J. REBUS and the anarchic brain: toward a unified model of the brain action of psychedelics. Pharmacol. Rev. 71, 316–344 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Schmitz, G. P. et al. Psychedelic compounds directly excite 5-HT2A layer 5 pyramidal neurons in the prefrontal cortex through a 5-HT2A Gq-mediated activation mechanism. Preprint at bioRxiv https://doi.org/10.1101/2022.11.15.516655 (2022).

  36. Alonso, J. F., Romero, S., Mañanas, M. À. & Riba, J. Serotonergic psychedelics temporarily modify information transfer in humans. Int. J. Neuropsychopharmacol. 18, pyv039 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ly, C. et al. Psychedelics promote structural and functional neural plasticity. Cell Rep. 23, 3170–3182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ly, C. et al. Transient stimulation with psychoplastogens is sufficient to initiate neuronal growth. ACS Pharmacol. Transl. Sci. 4, 452–460 (2021).

    Article  CAS  PubMed  Google Scholar 

  39. Olson, D. E. Psychoplastogens: a promising class of plasticity-promoting neurotherapeutics. J. Exp. Neurosci. 12, 1179069518800508 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Shao, L.-X. et al. Psilocybin induces rapid and persistent growth of dendritic spines in frontal cortex in vivo. Neuron 109, 2535–2544 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Du, Y. et al. Psilocybin facilitates fear extinction in mice by promoting hippocampal neuroplasticity. Chin. Med. J. 136, 2983–2992 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Vann, S. D., Aggleton, J. P. & Maguire, E. A. What does the retrosplenial cortex do? Nat. Rev. Neurosci. 10, 792–802 (2009).

    Article  CAS  PubMed  Google Scholar 

  43. Summerfield, J. J., Hassabis, D. & Maguire, E. A. Cortical midline involvement in autobiographical memory. Neuroimage 44, 1188–1200 (2009).

    Article  PubMed  Google Scholar 

  44. Maguire, E. A. Neuroimaging studies of autobiographical event memory. Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 1441–1451 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Todd, T. P. & Bucci, D. J. Retrosplenial cortex and long-term memory: molecules to behavior. Neural Plast. 2015, 414173 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Todd, T. P., DeAngeli, N. E., Jiang, M. Y. & Bucci, D. J. Retrograde amnesia of contextual fear conditioning: evidence for retrosplenial cortex involvement in configural processing. Behav. Neurosci. 135, 453–461 (2021).

    Article  PubMed  Google Scholar 

  47. Miller, A. M. P., Serrichio, A. C. & Smith, D. M. Dual-factor representation of the environmental context in the retrosplenial cortex. Cereb. Cortex 31, 2720–2728 (2021).

    Article  PubMed  Google Scholar 

  48. Trask, S., Ferrara, N. C., Jasnow, A. M. & Kwapis, J. L. Contributions of the rodent cingulate-retrosplenial cortical axis to associative learning and memory: a proposed circuit for persistent memory maintenance. Neurosci. Biobehav. Rev. 130, 178–184 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Trask, S. & Helmstetter, F. J. Unique roles for the anterior and posterior retrosplenial cortices in encoding and retrieval of memory for context. Cereb. Cortex 32, 3602–3610 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Sousa, A. Fde et al. Optogenetic reactivation of memory ensembles in the retrosplenial cortex induces systems consolidation. Proc. Natl Acad. Sci. USA 116, 8576–8581 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Hattori, R. & Komiyama, T. Context-dependent persistency as a coding mechanism for robust and widely distributed value coding. Neuron 110, 502–515 (2022).

    Article  CAS  PubMed  Google Scholar 

  52. Sun, W. et al. Context value updating and multidimensional neuronal encoding in the retrosplenial cortex. Nat. Commun. 12, 6045 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mitchell, A. S., Czajkowski, R., Zhang, N., Jeffery, K. & Nelson, A. J. D. Retrosplenial cortex and its role in spatial cognition. Brain Neurosci. Adv. 2, 2398212818757098 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Brennan, E. K. W. et al. Thalamus and claustrum control parallel layer 1 circuits in retrosplenial cortex. eLife 10, e62207 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Cheng, N. et al. Egocentric processing of items in spines, dendrites, and somas in the retrosplenial cortex. Neuron 112, 646–660 (2023).

    Article  PubMed  Google Scholar 

  56. Carhart-Harris, R. L. et al. Neural correlates of the LSD experience revealed by multimodal neuroimaging. Proc. Natl Acad. Sci. USA 113, 4853–4858 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wang, G. et al. Switching from fear to no fear by different neural ensembles in mouse retrosplenial cortex. Cereb. Cortex 29, 5085–5097 (2019).

    Article  PubMed  Google Scholar 

  58. Zhang, K. et al. The sexually divergent cFos activation map of fear extinction. Heliyon 10, e23748 (2024).

    Article  CAS  PubMed  Google Scholar 

  59. Davoudian, P. A., Shao, L.-X. & Kwan, A. C. Shared and distinct brain regions targeted for immediate early gene expression by ketamine and psilocybin. ACS Chem. Neurosci. 14, 468–480 (2023).

    Article  CAS  PubMed  Google Scholar 

  60. Kometer, M., Schmidt, A., Jäncke, L. & Vollenweider, F. X. Activation of serotonin 2A receptors underlies the psilocybin-induced effects on α oscillations, N170 visual-evoked potentials, and visual hallucinations. J. Neurosci. 33, 10544–10551 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Liu, S., Bubar, M. J., Lanfranco, M. F., Hillman, G. R. & Cunningham, K. A. Serotonin2C receptor (5-HT2CR) localization in GABA neurons of the rat medial prefrontal cortex: implications for understanding the neurobiology of addiction. Neuroscience 146, 1677–1688 (2007).

    Article  CAS  PubMed  Google Scholar 

  62. Pompeiano, M., Palacios, J. M. & Mengod, G. Distribution of the serotonin 5-HT2 receptor family mRNAs: comparison between 5-HT2A and 5-HT2C receptors. Mol. Brain. Res. 23, 163–178 (1994).

    Article  CAS  PubMed  Google Scholar 

  63. Zhang, Y. et al. Resting-state functional connectivity of the raphe nuclei in major depressive disorder: a multi-site study. NeuroImage Clin. 37, 103359 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Corcoran, K. A. et al. NMDA receptors in retrosplenial cortex are necessary for retrieval of recent and remote context fear memory. J. Neurosci. 31, 11655–11659 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Corcoran, K. A., Frick, B. J., Radulovic, J. & Kay, L. M. Analysis of coherent activity between retrosplenial cortex, hippocampus, thalamus, and anterior cingulate cortex during retrieval of recent and remote context fear memory. Neurobiol. Lear. Mem. 127, 93–101 (2016).

    Article  Google Scholar 

  66. Auguste, A. et al. Distinct brain networks for remote episodic memory depending on content and emotional experience. Prog. Neurobiol. 223, 102422 (2023).

    Article  PubMed  Google Scholar 

  67. Todd, T. P., Mehlman, M. L., Keene, C. S., DeAngeli, N. E. & Bucci, D. J. Retrosplenial cortex is required for the retrieval of remote memory for auditory cues. Learn. Mem. 23, 278–288 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Fournier, D. I. et al. Retrosplenial cortex inactivation during retrieval, but not encoding, impairs remotely acquired auditory fear conditioning in male rats. Neurobiol. Learn. Mem. 185, 107517 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Kwapis, J. L., Jarome, T. J., Lee, J. L., Gilmartin, M. R. & Helmstetter, F. J. Extinguishing trace fear engages the retrosplenial cortex rather than the amygdala. Neurobiol. Learn. Mem. 113, 41–54 (2014).

    Article  PubMed  Google Scholar 

  70. Trask, S., Ferrara, N. C., Grisales, K. & Helmstetter, F. J. Optogenetic inhibition of either the anterior or posterior retrosplenial cortex disrupts retrieval of a trace, but not delay, fear memory. Neurobiol. Learn. Mem. 185, 107530 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Han, C. J. et al. Trace but not delay fear conditioning requires attention and the anterior cingulate cortex. Proc. Natl. Acad. Sci. USA 100, 13087–13092 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Cowansage, K. K. et al. Direct reactivation of a coherent neocortical memory of context. Neuron 84, 432–441 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Raybuck, J. D. & Lattal, K. M. Bridging the interval: theory and neurobiology of trace conditioning. Behav. Process. 101, 103–111 (2014).

    Article  Google Scholar 

  74. Catlow, B. J., Song, S., Paredes, D. A., Kirstein, C. L. & Sanchez-Ramos, J. Effects of psilocybin on hippocampal neurogenesis and extinction of trace fear conditioning. Exp. Brain Res. 228, 481–491 (2013).

    Article  CAS  PubMed  Google Scholar 

  75. Williams, A. H. et al. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron 98, 1099–1115 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Pennington, Z. T. et al. ezTrack: an open-source video analysis pipeline for the investigation of animal behavior. Sci. Rep. 9, 19979 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Ji, G., Yakhnitsa, V., Kiritoshi, T., Presto, P. & Neugebauer, V. Fear extinction learning ability predicts neuropathic pain behaviors and amygdala activity in male rats. Mol. Pain 14, 1744806918804441 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  78. King, G., Scott, E., Graham, B. M. & Richardson, R. Individual differences in fear extinction and anxiety-like behavior. Learn. Mem. 24, 182–190 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Masella, G. et al. The amygdala NT3-TrkC pathway underlies inter-individual differences in fear extinction and related synaptic plasticity. Mol. Psychiatry 29, 1322–1337 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Presto, P., Ji, G., Junell, R., Griffin, Z. & Neugebauer, V. Fear extinction-based inter-individual and sex differences in pain-related vocalizations and anxiety-like behaviors but not nocifensive reflexes. Brain Sci. 11, 1339 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Russo, A. S. & Parsons, R. G. Neural activity in afferent projections to the infralimbic cortex is associated with individual differences in the recall of fear extinction. Sci. Rep. 12, 13703 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Shumake, J., Furgeson-Moreira, S. & Monfils, M. H. Predictability and heritability of individual differences in fear learning. Anim. Cogn. 17, 1207–1221 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Park, Y. & Geffen, M. N. A circuit model of auditory cortex. PLoS Comput. Biol. 16, e1008016 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Vohryzek, J. et al. Brain dynamics predictive of response to psilocybin for treatment-resistant depression. Brain Commun. 6, fcae049 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Woodburn, S. C., Levitt, C. M., Koester, A. M. & Kwan, A. C. Psilocybin facilitates fear extinction: importance of dose, context, and serotonin receptors. ACS Chem. Neurosci. 15, 3034–3043 (2024).

    Article  CAS  PubMed  Google Scholar 

  86. Ekins, T. G. et al. Cellular rules underlying psychedelic control of prefrontal pyramidal neurons. Preprint at bioRxiv https://doi.org/10.1101/2023.10.20.563334 (2023).

  87. Effinger, D. P. et al. Sex-specific effects of psychedelic drug exposure on central amygdala reactivity and behavioral responding. Transl. Psychiatry 13, 119 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Tomé, D. F. et al. Dynamic and selective engrams emerge with memory consolidation. Nat. Neurosci. 27, 561–572 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Kwan, A. C., Olson, D. E., Preller, K. H. & Roth, B. L. The neural basis of psychedelic action. Nat. Neurosci. 25, 1407–1419 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Rogers, S. A., Heller, E. A. & Corder, G. Psilocybin-enhanced fear extinction linked to bidirectional modulation of cortical ensembles. Preprint at bioRxiv https://doi.org/10.1101/2024.02.04.578811 (2024).

  91. Oswell, C. S. et al. Mimicking opioid analgesia in cortical pain circuits. Preprint at bioRxiv https://doi.org/10.1101/2024.04.26.591113 (2025).

Download references

Acknowledgements

This work was funded by the National Institute of Health National Institute of General Medical Sciences (DP2GM140923 awarded to G.C.), and by the National Institute of Mental Health (R01MH126027 awarded to E.A.H). We thank the University Laboratory Animal Resources group at the University of Pennsylvania for assistance with rodent husbandry and veterinary support, including all faculty stationed at both the Translational Research Laboratory. Thanks to M. Geffen (University of Pennsylvania) for advice on model construction. We would also like to thank other members of the Corder Lab, A. Jo (University of Pennsylvania) and R.A.S. Ortega (University of Pennsylvania), for critical discussions and advice on behavioral analysis, data visualization and analysis validation. We would also like to thank C. Mackey for assistance in customizing various Python packages. Finally, we would like to thank the faculty of the Cold Spring Harbor Laboratory course in Neural Data Analysis for critical input on the analysis approach.

Author information

Authors and Affiliations

  1. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Sophie A. Rogers & Gregory Corder

  2. Department of Neuroscience, Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Sophie A. Rogers & Gregory Corder

  3. Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Sophie A. Rogers & Gregory Corder

  4. Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Elizabeth A. Heller

Authors
  1. Sophie A. Rogers
    View author publications

    Search author on:PubMed Google Scholar

  2. Elizabeth A. Heller
    View author publications

    Search author on:PubMed Google Scholar

  3. Gregory Corder
    View author publications

    Search author on:PubMed Google Scholar

Contributions

S.A.R. and G.C. conceptualized and designed the study. E.A.H. provided key resources, including psilocybin, and assisted with experimental design and behavioral analysis. S.A.R. performed all data collection, analysis and writing. G.C. acquired funding, performed data visualization along with S.A.R., and edited and revised the manuscript.

Corresponding author

Correspondence to Gregory Corder.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Benjamin Grewe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Behavior and longitudinally registered neurons in implanted mice.

a, Center and bottom of implant tracts of all included mice from anterior (left) to posterior (right) granular RSC. b, Individual freezing from data in Fig. 2f with all multiple comparisons shown. From top to bottom: Habituation, Acquisition, Extinction 1, Extinction 2, Extinction 3 (Acquisition: F(trial period)(6,126) = 137.9, P < 0.0001; Extinction 1: F(trial period)(6, 126) = 7.239, P < 0.0001, Extinction 3: F(interaction)(18,126) = 2.582, P = 0.0011, F(trial period)(6,126) = 4.345, P = 0.0005, F(group)(3,21) = 20.53, P < 0.0001; two-way RM ANOVA with Sidak correction; Supplementary Table 1 (rows 6–10)). c, Normalized cross-correlation (NCC) scores of longitudinally registered ROIs with respect to Habituation. Left: heatmap of NCC scores for each ROI across days. Right: histogram of average NCC scores. d, Centroid distances of longitudinally registered ROIs with respect to Habituation. Left, heatmap of centroid distances for each cell across days. Right: histogram of average centroid distances. e,f, Fraction of tone- (e) and trace-responsive (f) cells that are responsive for 1–5 days in each animal. g, Average freezing encoding (auROC) in longitudinally registered neurons across days (F(session)(4,84) = 9.337, P < 0.0001; two-way RM ANOVA with Sidak correction; Supplementary Table 1 (row 17). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ****P ≤ 0.0001.

Source data

Extended Data Fig. 2 GMM validation.

a, Smoothed distributions (Gaussian, window of 4 for nonimplanted and 3 for implanted mice) of percentage freezing during the trace period in late Extinction 3. Dashed red line represents the inferred threshold from a GMM of both saline and psilocybin datasets. b, Cartoons depicting hypothesized sub-distributions composing saline and psilocybin distributions. c, Probability of assignment to low-freezing groups of mice across 10,000 iterations of GMMs using the leave-one-out method for each animal in each iteration. d, Effect of model choice on key result. Percent freezing during the trace period in early Extinction 3 was compared using thresholds from GMMs trained on only saline mice, psilocybin mice, both or the average threshold of each treatment’s GMM. (F(treatment)(1,10) = 9.774, P = 0.0108; mixed effects RM model with Sidak correction; Supplementary Table 2 (rows 1). e. Accuracy of animal classification across session periods. Dashed line is chance (50%).

Extended Data Fig. 3 All recorded RSC neurons.

a, Mean fraction of tone-responsive neurons on each day. Insets are proportions of neurons with suppressed, recruited and stable responses (two-way ANOVA, Supplementary Table 3 (rows 1 and 5)). b, Heatmaps displaying significant correlations (Pearson’s ρ, P < 0.05) between proportions of total (Tot), suppressed (Sup), recruited (Rec) and stable (Sta) tone-responsive neurons on each day and percentage freezing during the early (E) and late (L) halves of each session (gray rows = Hab freezing and gray columns = fractions of neurons during Hab, red = Acq, yellow = Ext1, green = Ext2, blue = Ext3). c,e,g, Same as a for trace- (c), tone-and-trace (e) and shock-responsive (g) neurons (two-way ANOVA with Sidak correction; Supplementary Table 3 (rows 1 and 5)). d,f,h, Same as b for trace- (d), tone-and-trace (f) and shock-responsive (h) neurons. Data are represented as mean ± s.e.m.

Source data

Extended Data Fig. 4 Effect of psilocybin on locomotion behavior and encoding.

a, Diagram of experimental protocol. N = 3 GRIN lens-implanted mice were placed in dark open-field arenas and recorded with infrared cameras and Miniscope. Fifteen minutes into the session, mice were injected with psilocybin. Data for 2–14 minutes pre-injection and 10–42 minutes postinjection were used. b, Immobility bouts per minute pre-injection and postinjection (p = 0.5515, paired t test; Supplementary Table 4 (row 23)). c, Median bout length (p = 0.0459, paired t test; Supplementary Table 5 (row 24)). d, Total time immobile (p = 0.1081, paired t test; Supplementary Table 4 (row 25)). e, Mean ± s.e.m. distance between immobility-on and immobility-off trajectories in PC space. f, Single-cell discriminability of immobility vs. motion (median d′) pre-injection and postinjection (p = 0.8208, paired t test; Supplementary Table 4 (row 26)). g, Population discriminability of immobility vs. motion (mean distance in PC space) pre-injection and postinjection (p = 0.2882, paired t test; Supplementary Table 4 (row 27)). *p ≤ 0.05.

Source data

Extended Data Fig. 5 TCA model construction.

a, Normalized temporal factor weights for each component, averaged within groups (Acquisition: F(time)(3,63) = 6.452, p = 0.0007; two-way RM ANOVA; Supplementary Table 5 (rows 4–9)). b, Average activity over trials during Acquisition (left), Extinction 1 (middle) and Extinction 3 (right) in the highest-weighted neuron from each ensemble (top to bottom) identified by rank 5 TCA in a representative animal. Weights of arrows indicate the weight of each neuron in the representative animal’s TCA model in the Acq-dominant (top), Ext1-dominant (middle) and Ext3-dominant (bottom) components. c, Example neuron reconstructions from the TCA model of the Acq-only neuron during Acquisition, the Ext1-only neuron during Extinction 1 and the Ext3-only neuron during Extinction 1. d, Correlations between reconstructed neuron activity and real neuron activity in each session for this mouse. *p ≤ 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Extended Data Fig. 6 TCA model rank selection.

a, Session discriminability as a function of model rank-choice in each animal and on average (cyan). Red dashed line indicates the chosen rank. b, Correlations of the temporal (top), neuron (middle) and trial factors (bottom) between the Acq-dominant (left), Ext1-dominant (middle) and Ext3-dominant (right) components of models of ranks 1–10, averaged over animals. Correlations of p > 0.05 were set to 0.

Source data

Extended Data Fig. 7 Nonshock controls do not exhibit conditioning-associated dynamics.

a, Schematic of nonshock protocol. Three Miniscope-implanted mice underwent an identical 5-day paradigm to all other mice, with the exception that they received no shock during Acquisition or drug treatment. b, Half-session freezing in nonshock mice (one-way RM ANOVA with Sidak correction; Supplementary Table 7 (row 1)). c, Number of longitudinally registered neurons in nonshock mice. d, Sum of session discriminability index. Because roughly half the number of neurons were recorded in nonshock mice as in the other two groups, pooled tensors from psilocybin responders, nonresponders and saline mice were subsampled to a different, random set of 160 neurons in each of 100 iterations of TCA (F(3,297) = 6694, p < 0.0001, one-way RM ANOVA with Sidak correction; Supplementary Table 7 (row 2). e–g, Overlap of the (e) day 2-dominant ensemble with (f) day 3- and (g) day 5-dominant ensembles in nonshock mice. Bar graphs display the median fraction overlaps. Dots are individual animals. Insets are pie charts displaying total overlap. Stars indicate comparison to low-freezing saline distribution (Acq-dominant: chi square = 10.84, p = 0.0126; Ext1-dominant: chi square = 16.04, p = 0.0011; Ext3-dominant: chi square = 30.50, p < 0.0001; chi-square test; Supplementary Table 7 (rows 3–5)). h–j, Average z score with respect to day 2 of (h) day 2-, (i) day 3- and (j) day 5-dominant ensembles, respectively, during day 3 and 5 in nonshock mice (turquoise) compared to conditioned, saline-administered mice (black; Acq-dominant: F(group)(1,202) = 9.329, p = 0.0026; Ext3-dominant: F(interaction)(1,240) = 5.787, p = 0.0169; F(session)(1,240) = 23.06, p < 0.0001, F(group)(1,240) = 4.534, p = 0.0342; two-way RM ANOVA with Sidak correction; Supplementary Table 7 (rows 4–6)). Data are represented as mean ± s.e.m. *p ≤ 0.05, **p < 0.01, ****p < 0.0001.

Source data

Extended Data Fig. 8 Results are robust to changes in factor loading thresholds.

a, Change in activity in mean ± s.e.m. from Acquisition in Acq-dominant neurons as a function of factor loading thresholds varying between w = 0-2 during Extinction 1 (left) and Extinction 3 (right). b, Same as a for Ext1-dominant neurons. c, Same as a for Ext3-dominant neurons. d, Peri-stimulus time histogram (PSTH) of an example simulated neuron to determine the null hypothesis factor loading threshold. Tensors of t × c × T size, where c is the number of neurons recorded in a given animal, were created with identically behaving neurons to determine the factor loading threshold in a hypothetical population in which each neuron equally contributes to dynamics, or the null hypothesis factor loading threshold for that animal. e, Reconstruction error and model similarity of varying model ranks for populations of identical neurons. A model of rank 1 yields 0 error in this case. f, Representative rank 1 TCA of a simulated dataset with n = 46 neurons, the median number of neurons recorded in this study. Because variances across trials and neurons were clamped at 0, only the temporal factor varies. g, Data in Fig. 4a plotted as a function of number of neurons recorded. Mean weight of neuron factors across 100 iterations of TCA at the number of cells recorded in each animal. h, Change in activity in mean ± s.e.m. from Acquisition during Extinctions 1 and 3 in Acq-dominant (left), Ext1-dominant (middle) and Ext3-dominant (right) using ensembles determined with the null hypothesis factor loading for each animal. (Acq-dominant: F(group)(3,523) = 8.886, p < 0.0001; Ext1-dominant: F(group)(3,539) = 6.838, p = 0.0002; Ext3-dominant: F(session)(1,523) = 11.12, p < 0.0001; F(group)(3,523) = 8.886, p < 0.0001; two-way RM ANOVA; Supplementary Table 7 (rows 9–11)). *p ≤ 0.05, **p < 0.01, ****p < 0.0001.

Source data

Extended Data Fig. 9 Psilocybin bidirectionally modulates neural ensembles driving RSC dynamics during TFC in responders.

a, Overlaps of ensembles within individual animals comprising the mean values in Fig. 4b for the Acq- (top), Ext1- (middle) and Ext3-dominant (bottom) ensembles. b, Fisher decoder performance on Acquisition activity in functionally defined ensembles of cells to distinguish psilocybin groups (black), low-freezing psilocybin vs. saline (light purple) and high-freezing psilocybin vs. low-freezing saline mice (dark purple). Hundred iterations for each comparison. Shuffled values are behind real values. c,d, Three-way Fisher decoder performance low-freezing psilocybin vs. high-freezing psilocybin vs. low-freezing saline mice trained on activity during Extinction 1 (c) and Extinction 3 (d). e–g, Fisher decoder performance classifying psilocybin low- vs. saline low-freezing mice (e), psilocybin high- vs. saline low-freezing mice (f) and psilocybin groups (g) based on ensemble activity during Extinction 1 (top) and 3 (bottom) activity during freezing (salmon), motion (turquoise) and both (black). h–n, Average z-score activity in ensemble neurons in the Acq-only (h), Ext1-only (i), Ext3-only (j), Acq/Ext1 (k), Acq/Ext3 (l), Ext1/Ext3 (m), and Acq/Ext1/Ext3 (n) ensembles from Acquisition during freezing and motion in Extinction 1 (top) and Extinction 3 (bottom; multiple paired t tests for each ensemble within session, FDR = 0.01; Supplementary Table 9 (rows 18–31)). Stars indicate discoveries. Data displayed as mean ± s.e.m.

Source data

Extended Data Fig. 10 Shock-responsive neurons are unstable in the RSC.

a, Trial diagram for Acquisition. b, Fraction of significantly shock-responsive neurons during shock across all 25 mice (for each neuron, Wilcoxon rank-sumshock-baseline p < 0.01). c, Heatmap of the average fraction of overlap in shock-up neurons between each trial of Acquisition. Average overlap between trials ranges from 16% to 49%. d, Persistence of the response properties of shock-up neurons over the session. Each point y is the fraction of neurons upregulated in response to the shock for x number of trials. Data are represented as mean ± s.e.m. over all 21 mice.

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data

Source Data Fig. 5

Statistical source data

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rogers, S.A., Heller, E.A. & Corder, G. Psilocybin-enhanced fear extinction linked to bidirectional modulation of cortical ensembles. Nat Neurosci 28, 1311–1326 (2025). https://doi.org/10.1038/s41593-025-01964-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-025-01964-9

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing