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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
    Dekkers G, Rosas F, van Waterschoot T, Vanrumste B, Karsmakers Pet al., 2022,

    Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities

    , Information Fusion, Vol: 77, Pages: 196-210, ISSN: 1566-2535

    For the past decades there has been a rising interest for wireless sensor networks to obtain information about an environment. One interesting modality is that of audio, as it is highly informative for numerous applications including speech recognition, urban scene classification, city monitoring, machine listening and classifying domestic activities. However, as they operate at prohibitively high energy consumption, commercialisation of battery-powered wireless acoustic sensor networks has been limited. To increase the network's lifetime, this paper explores the joint use of decision-level fusion and dynamic sensor activation. Hereby adopting a topology where processing – including feature extraction and classification – is performed on a dynamic set of sensor nodes that communicate classification outputs which are fused centrally. The main contribution of this paper is the comparison of decision-level fusion with different dynamic sensor activation strategies on the use case of automatically classifying domestic activities. Results indicate that using vector quantisation to encode the classification output, computed at each sensor node, can reduce the communication per classification output to 8 bit without loss of significant performance. As the cost for communication is reduced, local processing tends to dominate the overall energy budget. It is indicated that dynamic sensor activation, using a centralised approach, can reduce the average time a sensor node is active up to 20% by leveraging redundant information in the network. In terms of energy consumption, this resulted in an energy reduction of up to 80% as the cost for computation dominates the overall energy budget.

  • Journal article
    Vermeulen T, Reynders B, Rosas FE, Verhelst M, Pollin Set al., 2021,

    Performance analysis of in-band collision detection for dense wireless networks

    , Eurasip Journal on Wireless Communications and Networking, Vol: 2021, Pages: 1-23, ISSN: 1687-1472

    With the massive growth of wireless networks comes a bigger impact of collisions and interference, which has a negative effect on throughput and energy efficiency. To deal with this problem, we propose an in-band wireless collision and interference detection scheme based on full-duplex technology. To study its performance, we compare its throughput and energy efficiency with the performance of traditional half-duplex and symmetric in-band full-duplex transmissions. Our analysis considers a realistic protocol and overhead modeling, and a measurement-based self-interference model. Our results indicate that our proposed collision detection scheme can provide significant gains in terms of throughput and energy efficiency in large wireless networks. Moreover, when compared to half-duplex and symmetric full-duplex, our analysis shows that this scheme allows up to 45% more nodes in the network for the same energy consumption per bit. These results suggest that this could be an enabling technology towards efficient, dense wireless networks.

  • Journal article
    Kettlun F, Rosas F, Oberli C, 2021,

    A low-complexity channel training method for efficient SVD beamforming over MIMO channels

    , Eurasip Journal on Wireless Communications and Networking, Vol: 2021, Pages: 1-22, ISSN: 1687-1472

    Singular value decomposition (SVD) beamforming is an attractive tool for reducing the energy consumption of data transmissions in wireless sensor networks whose nodes are equipped with multiple antennas. However, this method is often not practical due to two important shortcomings: it requires channel state information at the transmitter and the computation of the SVD of the channel matrix is generally too complex. To deal with these issues, we propose a method for establishing an SVD beamforming link without requiring feedback of actual channel or SVD coefficients to the transmitter. Concretely, our method takes advantage of channel reciprocity and a power iteration algorithm (PIA) for determining the precoding and decoding singular vectors from received preamble sequences. A low-complexity version that performs no iterations is proposed and shown to have a signal-to-noise-ratio (SNR) loss within 1 dB of the bit error rate of SVD beamforming with least squares channel estimates. The low-complexity method significantly outperforms maximum ratio combining diversity and Alamouti coding. We also show that the computational cost of the proposed PIA-based method is less than the one of using the Golub–Reinsch algorithm for obtaining the SVD. The number of computations of the low-complexity version is an order of magnitude smaller than with Golub–Reinsch. This difference grows further with antenna array size.

  • Conference paper
    Rosas FE, Mediano PAM, Gastpar M, 2021,

    Learning, compression, and leakage: Minimising classification error via meta-universal compression principles

    , 2020 IEEE Information Theory Workshop (ITW), Publisher: IEEE, Pages: 1-5

    Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas. A promising group of compression techniques for learning scenarios is normalised maximum likelihood (NML) coding, which provides strong guarantees for compression of small datasets — in contrast with more popular estimators whose guarantees hold only in the asymptotic limit. Here we consider a NMLbased decision strategy for supervised classification problems, and show that it attains heuristic PAC learning when applied to a wide variety of models. Furthermore, we show that the misclassification rate of our method is upper bounded by the maximal leakage, a recently proposed metric to quantify the potential of data leakage in privacy-sensitive scenarios.

  • Journal article
    Gatica M, Cofré R, Mediano PAM, Rosas FE, Orio P, Diez I, Swinnen SP, Cortes JMet al., 2021,

    High-Order Interdependencies in the Aging Brain.

    , Brain Connect

    Background: Brain interdependencies can be studied from either a structural/anatomical perspective ("structural connectivity") or by considering statistical interdependencies ("functional connectivity" [FC]). Interestingly, while structural connectivity is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables. Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age. Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies compared with younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a "redundancy core" constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied. Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive, and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available.

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