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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

Citation

BibTex format

@inproceedings{Rosas:2021:10.1109/itw46852.2021.9457579,
author = {Rosas, FE and Mediano, PAM and Gastpar, M},
doi = {10.1109/itw46852.2021.9457579},
pages = {1--5},
publisher = {IEEE},
title = {Learning, compression, and leakage: Minimising classification error via meta-universal compression principles},
url = {http://dx.doi.org/10.1109/itw46852.2021.9457579},
year = {2021}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - 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.
AU - Rosas,FE
AU - Mediano,PAM
AU - Gastpar,M
DO - 10.1109/itw46852.2021.9457579
EP - 5
PB - IEEE
PY - 2021///
SP - 1
TI - Learning, compression, and leakage: Minimising classification error via meta-universal compression principles
UR - http://dx.doi.org/10.1109/itw46852.2021.9457579
UR - https://ieeexplore.ieee.org/document/9457579
UR - http://hdl.handle.net/10044/1/90016
ER -