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Pioneering research

In the last decade, a number of research groups in Europe and the Americas have conducted studies into the safety and effectiveness of psychedelics for conditions such as depression and post-traumatic stress disorder (PTSD), but the Imperial Centre for Psychedelic Research is the first to gain this level of stature within a major academic institution.

When delivered safely and professionally, psychedelic therapy holds a great deal of promise for treating some very serious mental health conditions.

Dr Robin Carhart-Harris

Head of the Centre for Psychedelic Research

Ours was the first Centre in the world to investigate the brain effects of LSD using modern brain imaging and the first to study psilocybin – the active compound in magic mushrooms – for treating severe depression. These studies have laid the groundwork for larger trials that are now taking place around the world. Other pioneering work from the group includes breakthrough neuroimaging research with psilocybin, MDMA and DMT (the psychoactive compounds found in ecstasy and ayahuasca respectively).

Earlier this year the group began a new trial directly comparing psilocybin therapy with a conventional antidepressant drug in patients with depression – a study for which they are still recruiting volunteers. Building on this, they also plan to begin another new trial next year to explore the safety and feasibility of psilocybin for treating patients with anorexia.

Dr Carhart-Harris adds: “It may take a few years for psychedelic therapy to be available for patients, but research so far has been very encouraging. Early stage clinical research has shown that when delivered safely and professionally, psychedelic therapy holds a great deal of promise for treating some very serious mental health conditions and may one day offer new hope to vulnerable people with limited treatment options.”


If you are a student interested in conducting research with our Centre, please see the page join our research team.

Research publications

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  • Journal article
    Rosas FE, Mediano PAM, Rassouli B, Barrett ABet al., 2020,

    An operational information decomposition via synergistic disclosure

    , Journal of Physics A: Mathematical and Theoretical, Vol: 53, Pages: 485001-485001, ISSN: 1751-8113

    Multivariate information decompositions hold promise to yield insight into complex systems, and stand out for their ability to identify synergistic phenomena. However, the adoption of these approaches has been hindered by there being multiple possible decompositions, and no precise guidance for preferring one over the others. At the heart of this disagreement lies the absence of a clear operational interpretation of what synergistic information is. Here we fill this gap by proposing a new information decomposition based on a novel operationalisation of informational synergy, which leverages recent developments in the literature of data privacy. Our decomposition is defined for any number of information sources, and its atoms can be calculated using elementary optimisation techniques. The decomposition provides a natural coarse-graining that scales gracefully with the system's size, and is applicable in a wide range of scenarios of practical interest.

  • Journal article
    Andersen MM, Schjoedt U, Price H, Rosas FE, Scrivner C, Clasen Met al., 2020,

    Playing with fear: a field study in recreational horror

    , Psychological Science, Pages: 1-14, ISSN: 0956-7976

    Haunted attractions are illustrative examples of recreational fear in which people voluntarily seek out frightening experiences in pursuit of enjoyment. We present findings from a field study at a haunted-house attraction where visitors between the ages of 12 and 57 years (N = 110) were equipped with heart rate monitors, video-recorded at peak scare points during the attraction, and asked to report on their experience. Our results show that enjoyment has an inverted-U-shaped relationship with fear across repeated self-reported measures. Moreover, results from physiological data demonstrate that the experience of being frightened is a linear function of large-scale heart rate fluctuations, whereas there is an inverted-U-shaped relationship between participant enjoyment and small-scale heart rate fluctuations. These results suggest that enjoyment is related to forms of arousal dynamics that are “just right.” These findings shed light on how fear and enjoyment can coexist in recreational horror.

  • Journal article
    Herzog R, Mediano PAM, Rosas FE, Carhart-Harris R, Perl YS, Tagliazucchi E, Cofre Ret al., 2020,

    A mechanistic model of the neural entropy increase elicited by psychedelic drugs

    , Scientific Reports, Vol: 10, ISSN: 2045-2322

    Psychedelic drugs, including lysergic acid diethylamide and other agonists of the serotonin 2A receptor (5HT2A-R), induce drastic changes in subjective experience, and provide a unique opportunity to study the neurobiological basis of consciousness. One of the most notable neurophysiological signatures of psychedelics, increased entropy in spontaneous neural activity, is thought to be of relevance to the psychedelic experience, mediating both acute alterations in consciousness and long-term effects. However, no clear mechanistic explanation for this entropy increase has been put forward so far. We sought to do this here by building upon a recent whole-brain model of serotonergic neuromodulation, to study the entropic effects of 5HT2A-R activation. Our results reproduce the overall entropy increase observed in previous experiments in vivo, providing the first model-based explanation for this phenomenon. We also found that entropy changes were not uniform across the brain: entropy increased in some regions and decreased in others, suggesting a topographical reconfiguration mediated by 5HT2A-R activation. Interestingly, at the whole-brain level, this reconfiguration was not well explained by 5HT2A-R density, but related closely to the topological properties of the brain's anatomical connectivity. These results help us understand the mechanisms underlying the psychedelic state and, more generally, the pharmacological modulation of whole-brain activity.

  • Journal article
    Cofré R, Herzog R, Mediano PAM, Piccinini J, Rosas FE, Sanz Perl Y, Tagliazucchi Eet al., 2020,

    Whole-brain models to explore altered states of consciousness from the bottom up

    , Brain Sciences, Vol: 10, ISSN: 2076-3425

    The scope of human consciousness includes states departing from what most of us experience as ordinary wakefulness. These altered states of consciousness constitute a prime opportunity to study how global changes in brain activity relate to different varieties of subjective experience. We consider the problem of explaining how global signatures of altered consciousness arise from the interplay between large-scale connectivity and local dynamical rules that can be traced to known properties of neural tissue. For this purpose, we advocate a research program aimed at bridging the gap between bottom-up generative models of whole-brain activity and the top-down signatures proposed by theories of consciousness. Throughout this paper, we define altered states of consciousness, discuss relevant signatures of consciousness observed in brain activity, and introduce whole-brain models to explore the biophysics of altered consciousness from the bottom-up. We discuss the potential of our proposal in view of the current state of the art, give specific examples of how this research agenda might play out, and emphasize how a systematic investigation of altered states of consciousness via bottom-up modeling may help us better understand the biophysical, informational, and dynamical underpinnings of consciousness.

  • Journal article
    Chen J, Wang Z, Zhu T, Rosas FEet al., 2020,

    Recommendation algorithm in double-layer network based on vector dynamic evolution clustering and attention mechanism

    , Complexity, Vol: 2020, Pages: 1-19, ISSN: 1076-2787

    The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

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