5 Computational neuropsychiatry

Abstract

Professor of Neuropsychiatry and Director of the Wellcome Trust Centre for Neuroimaging, UCL, London. Ray Dolan is Mary Kinross Professor of Neuropsychiatry and Director of the Wellcome Trust Centre for Neuroimaging, at UCL. Prof Dolan’s research addresses the neurobiology of emotion and decision making, how emotion impacts on cognition and its aberrant expression in disease. He has published over 500 peer reviewed papers is among the most cited scientist in the world in the field of Neuroscience and Behaviour. He has received numerous awards including the Alexander von Humboldt Research Award (2004), the Minerva Foundation Golden Brain Award (2006), the International Max Planck Research Award (2007) and the Zulch Prize (2013). Between 2010–2014 he has been Visiting Einstein Fellow to the Humboldt University, Berlin. He was elected Fellow of the Academy of Medical Sciences (FMedSci) in 2000, Fellow of the Royal Society (FRS) in 2010 and an External Member of the Max Planck Society (MPS) in 2012. He recently established the first Centre for Computational Psychiatry at UCL, a joint venture with the Max Planck Society (MPS). Computational psychiatry encapsulates a set of approaches to the study of psychiatric conditions that are rooted in computer science. Two broad approaches can be identified. The first of these is an atheoretical and with an emphasis that is data driven. This usually involves the application of machine learning (ML) methods in an attempt to refine disease classification, predict treatment response and improve treatment outcomes. This approach can be applied to a wide class of data, including the analysis of brain imaging data. The second approach is theoretical and grounded within developments that derive from theoretical neuroscience. My focus in this talk will be entirely on the latter where I will provide examples of its utility in deepening our understanding of (i) adaptive learning and risk behaviour (ii) providing a quantitative basis for measuring and predicting subjective states (iii) providing neurobiologically inspired models that can be used to inform mechanistic interpretations of large scale data. These examples will serve to highlight how in computational psychiatry can open new avenues of investigation that provide mechanistic insights into disease processes with implications for classification and treatment. Computational psychiatry also enables an easier dialogue between advances in neuroscience and the challenges of providing a biologically informed understating of psychopathology.