Professor Karl J. Friston MB, BS, MA, MAE, MRCPsych, FMedSci, FRSB, FRS
Scientific Director: Wellcome Centre for Human Neuroimaging
Institute of Neurology, UCL
12 Queen Square
London. WC1N 3AR UK
Karl Friston is a theoretical neuroscientist and authority on brain imaging. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). These contributions were motivated by schizophrenia research and theoretical studies of value-learning, formulated as the dysconnection hypothesis of schizophrenia. Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference).
Friston received the first Young Investigators Award in Human Brain Mapping (1996) and was elected a Fellow of the Academy of Medical Sciences (1999). In 2000 he was President of the international Organization of Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006. In 2008 he received a Medal, College de France and an Honorary Doctorate from the University of York in 2011. He became of Fellow of the Royal Society of Biology in 2012, received the Weldon Memorial prize and Medal in 2013 for contributions to mathematical biology and was elected as a member of EMBO (excellence in the life sciences) in 2014 and the Academia Europaea in (2015). He was the 2016 recipient of the Charles Branch Award for unparalleled breakthroughs in Brain Research and the Glass Brain Award, a lifetime achievement award in the field of human brain mapping. He holds Honorary Doctorates from the University of Zurich and Radboud University.
Synoptopathy, dysconnections and the Bayesian brain
This talk considers formal or computational approaches to psychopathology. I will use schizophrenia to offer a case study of computational psychiatry. We first review the basic phenomenology and pathophysiological theories of schizophrenia, with a special focus on synoptopathy and functional dysconnections; i.e., network disorders. These motivate the choice of a formal or computational framework within which to understand psychopathology; particularly, in terms of false beliefs or inference. This framework in the Bayesian brain. We will focus on the (neuromodulatory) encoding of uncertainty or precision within predictive coding implementations of active inference – to demonstrate computational approaches to pathogenesis in neuropsychyatric disorders. The endpoint of this analysis is the key role of neuromodulation in selecting those aspects of the sensorium that underwrite our belief updating – and making sens of our sensations. This speaks to (i) the key role of modulatory neurotransmitters (and psychopharmacological drugs) in shaping our inferences about the lived world, and (ii) non-invasive measurements of context sensitive connectivity or synaptic efficacy (e.g., with dynamic causal modelling).
Key words: active inference – dysconnection – psychosis – Bayesian – precision – false inference – predictive coding – effective connectivity – dynamic causal modeling