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- Clinical Training Fellow/ NIHR Clinical Lecturer
- Division of Psychiatry
- Faculty of Brain Sciences
I did my undergraduate medical training and a Neuroscience BSc at Cambridge University and my clinical medical training at University College London. I worked full-time as a medical and then psychiatric doctor from 2004-10, obtaining my MRCP and MRCPsych. From 2009-10 I did a part time MSc in the Philosophy of Mental Disorder at King's College London. From 2010-14, I did my PhD at the Wellcome Trust Centre for Neuroimaging at UCL, under the supervision of Karl Friston. I am now an NIHR Clinical Lecturer in Psychiatry at UCL (Division of Psychiatry and Institute of Cognitive Neuroscience), in Jon Roiser's group.
My research interest is in using the techniques of Computational Psychiatry to understand schizophrenia and psychosis. Understanding the brain at a computational level allows us to link biological, social and psychological accounts of mental function and dysfunction in a mathematically rigorous way (Adams et al., 2015, JNNP).
There is evidence for at least three types of biological pathology in schizophrenia:
i) a loss of synaptic gain in higher regions in the brain's hierarchy, e.g. prefrontal cortex and hippocampus;
ii) an increase in synaptic gain in sensory areas;
iii) hyperactivity of dopamine 2 receptors in the striatum (probably due to increased pre-synaptic synthesis of dopamine).
I use models of brain function to understand how these changes in neurotransmission might cause a variety of phenomena in schizophrenia and psychosis. Examples include:
i) Modelling perception and action:
Recent influential accounts of brain function propose that it performs Bayesian inference on the causes of its sensory data (perhaps using predictive coding, in which top-down predictions are constantly updated by prediction errors from below). The changes in synaptic gain described above could result in a relative loss of precision (increase in uncertainty) of the brain's prior beliefs. This would lead to subtle problems in perceiving visual illusions, attenuating self-caused sensations, and following moving targets with the eyes, all of which are found in schizophrenia.
ii) Modelling cognitive tasks:
A loss of precision at higher hierarchical levels could also affect belief formation and decision-making. Chance events may seem more 'salient' and cause unwarranted updating of beliefs, for example, and decision-making or working memory might become noisier. Models of cognitive tasks (e.g. the ‘beads’ task) can shed light on why subjects with schizophrenia form or maintain unusual beliefs.
iii) Modelling electrophysiological responses:
The changes in synaptic gain highlighted earlier also affect brain responses. Paradigms like the ‘mismatch negativity’ (MMN) use sequences of tones to assess how differently the brain responds (measured with EEG or MEG) to predicted or unpredicted stimuli. We can use biophysical models of these EEG or MEG responses to estimate the synaptic gain in different brain areas, using a technique called dynamic causal modelling (DCM). DCM studies have shown differences in synaptic gain between subjects with and without schizophrenia.
iv) Mapping behavioural models on to the brain:
Modelling behaviour and brain responses separately is very useful, but ultimately we wish to know whether our models of behaviour are instantiated in the brain. We can discover this by testing whether parameters obtained from behavioural models correlate with estimates of synaptic gain from EEG/MEG data or dopamine 2 receptor availability from PET data, for example.
I have co-organised the UCL Computational Psychiatry course (with lead organiser Xiaosi Gu): a new course at UCL which is intended to provide an introduction to the use of computational methods in psychiatric research.
|01-MAR-2014 – 01-MAY-2017||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|