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Dr Rick Adams
3rd Floor, Dept of Engineering
Centre for Medical Image Computing, Dept of Computer Science
Malet Place
-- UK --
  • Research Fellow
  • Dept of Computer Science
  • Faculty of Engineering Science

I did a Medicine and Neuroscience BSc at Cambridge University and Clinical Medicine at UCL. 

I worked as a medical and psychiatric doctor from 2004-10, obtaining my MRCP and MRCPsych. 

From 2009-10 I did an MSc in the Philosophy of Mental Disorder at KCL. 

From 2010-14, I did my PhD at the Wellcome Centre for Human Neuroimaging at UCL, supervised by Prof Karl Friston. 

From 2014-18, I was an NIHR Clinical Lecturer in Psychiatry at UCL (Division of Psychiatry and Institute of Cognitive Neuroscience), in Prof Jon Roiser's group. 

In 2016 I took up a Bogue Fellowship to study at Yale University in Dr Alan Anticevic's group. I've also enjoyed fruitful collaborations with Prof Oliver Howes (KCL, Imperial), Prof Neil Burgess (UCL) and Prof Mark Edwards (St. George's). 

I am now a Consultant General Adult Psychiatrist and MRC Skills Development Fellow in Prof Janaina Mourao-Miranda's group in the Dept of Computer Science and Max Planck UCL Centre for Computational Psychiatry and Ageing Research. 

Since 2016 I have been a member of the Gatsby/Wellcome Neuroscience Project, whose goal is to update the neuroscience content of the training curriculum for psychiatrists in the UK.  

Since 2014 I have co-organised (with Dr Xiaosi Gu – now at Mount Sinai, NYC) the UCL Computational Psychiatry Course: the first such course in the world. It provides a 2 day introduction to the computational modelling of behaviour in psychiatric research, and the slides and audio of past lectures are online here: 

Research Summary

I use Computational Psychiatry to understand schizophrenia and the psychosis spectrum. Computational models can link biological, social and psychological accounts of mental function and dysfunction in a mathematically rigorous way (Adams et al., 2016, JNNP).

There is evidence for at least two biological pathologies in schizophrenia: 

i) dysfunction of NMDA receptors, especially those located on inhibitory interneurons. This may lead to an increase in the ratio of excitatory:inhibitory transmission (E/I) and a loss of 'signal-to-noise' in higher hierarchical areas such as prefrontal cortex and hippocampus.

ii) increased synthesis and release of dopamine in the striatum.

I use models of brain function to understand how these changes might contribute to schizophrenia and psychosis. Examples include:

i) Modelling perception, action & cognition: 

The brain may perform (or approximate) Bayesian inference on the causes of its sensory data (e.g. by updating top-down predictions with bottom-up prediction errors). Increased E/I could make the brain's prior beliefs more noisy, causing subtle problems in perceiving visual illusions, attenuating self-caused sensations, and oculomotor pursuit, which are all seen in schizophrenia (Adams et al., 2013, Front Psychiatry). Increased E/I at higher hierarchical levels could also affect belief stability and 'noise' in decision-making.  Models of cognitive tasks (e.g. the ‘beads’ task) can demonstrate this (Adams et al., 2018, J Neurosci).

ii) Modelling imaging data:

Dynamic causal modelling (DCM) uses biophysical models of imaging data to estimate e.g. E/I in different brain areas. We found increased E/I in prefrontal cortex in schizophrenia (Ranlund, Adams, et al., 2016, Hum Brain Mapp).

iii) Mapping behavioural models on to the brain:

Are our models of behaviour instantiated in the brain? We have tested whether behavioural model parameters correlate with E/I parameters from M/EEG data (Adams et al., 2016, Neuroimage) or dopamine 2/3 receptor availability from PET data (Adams et al., in prep). 

iv) Discovering new psychosis phenotypes:

I'm now collaborating with Dr Alan Anticevic (Yale) to use DCM and machine learning on large functional imaging datasets to try to discover neurobiologically-interpretable phenotypes in the psychosis spectrum.  

Google Scholar: https://bit.ly/2INFezE

Teaching Summary

As of 2018, I have supervised 3 visiting scholar projects, 2 BSc projects, 4 MSc projects, 1 MRes project and 1 PhD project at UCL. 4 of the bachelor/masters projects were awarded distinctions. If you are interested in undertaking a Computational Psychiatry PhD at UCL, you are welcome to email me but note that most PhD students come from the following schemes:

UCL-Birkbeck MRC Doctoral Training Programme: 


UCL-Wellcome Neuroscience PhD: 


UCL Institute of Neurology PhD programme:


UCL Institute of Mental Health PhD programme:


UCL-NIMH Joint Doctoral Training Program in Neuroscience:


Leverhulme Doctoral Training Programme for the Ecological Study of the Brain:


London Interdisciplinary Doctoral Programme (LIDo, BBSRC)


Gatsby UCL PhD Programme:


UCL Centre for Computation, Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX) PhD programme:


If you are interested in learning more about Computational Psychiatry, I highly recommend the following resources:





31-JUL-2018 – 31-JUL-2021 MRC Skills Development Fellow CMIC, Dept of Computer Science UCL, United Kingdom
01-APR-2018 – 31-JUL-2021 Affiliate Member Wellcome Centre for Human Neuroimaging Institute of Neurology, UCL, United Kingdom
01-APR-2018 – 31-JUL-2018 UCLH BRC Postgraduate Research Fellow Division of Psychiatry UCL, United Kingdom
01-MAR-2014 – 01-MAR-2018 NIHR Clinical Lecturer in Psychiatry Institute of Cognitive Neuroscience & Division of Psychiatry University College London, United Kingdom
13-SEP-2010 – 28-FEB-2014 Clinical Research Associate Wellcome Trust Centre for Neuroimaging Institute of Neurology, United Kingdom
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