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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
    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.

  • Conference paper
    Rosas De Andraca FE, Azari M, Arani A, 2020,

    Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits

    , IEEE Globecom Workshops 2020
  • Journal article
    Rajpal H, Rosas De Andraca FE, Jensen HJ, 2019,

    Tangled worldview model of opinion dynamics

    , Frontiers in Physics, Vol: 7, ISSN: 2296-424X

    We study the joint evolution of worldviews by proposing a model of opinion dynamics, which is inspired in notions fromevolutionary ecology. Agents update their opinion on a specific issue based on their propensity to change – asserted by thesocial neighbours – weighted by their mutual similarity on other issues. Agents are, therefore, more influenced by neighbourswith similar worldviews (set of opinions on various issues), resulting in a complex co-evolution of each opinion. Simulationsshow that the worldview evolution exhibits events of intermittent polarization when the social network is scale-free. This, in turn,triggers extreme crashes and surges in the popularity of various opinions. Using the proposed model, we highlight the role ofnetwork structure, bounded rationality of agents, and the role of key influential agents in causing polarization and intermittentreformation of worldviews on scale-free networks.

  • Journal article
    Cofré R, Herzog R, Corcoran D, Rosas FEet al., 2019,

    A comparison of the maximum entropy principle across biological spatial scales

    , Entropy: international and interdisciplinary journal of entropy and information studies, Vol: 21, Pages: 1-20, ISSN: 1099-4300

    Despite their differences, biological systems at different spatial scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are often hard to grasp due to the highly specialized nature of modern science and the parcelled terminology employed by various scientific sub-disciplines. To explore these common organizational features, this paper provides a comparative study of diverse applications of the maximum entropy principle, which has found many uses at different biological spatial scales ranging from amino acids up to societies. By presenting these studies under a common approach and language, this paper aims to establish a unified view over these seemingly highly heterogeneous scenarios.

  • Journal article
    Cofré R, Videla L, Rosas F, 2019,

    An introduction to the non-equilibrium steady states of maximum entropy spike trains

    , Entropy, Vol: 21, Pages: 1-28, ISSN: 1099-4300

    Although most biological processes are characterized by a strong temporal asymmetry, several popular mathematical models neglect this issue. Maximum entropy methods provide a principled way of addressing time irreversibility, which leverages powerful results and ideas from the literature of non-equilibrium statistical mechanics. This tutorial provides a comprehensive overview of these issues, with a focus in the case of spike train statistics. We provide a detailed account of the mathematical foundations and work out examples to illustrate the key concepts and results from non-equilibrium statistical mechanics.

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