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Great expectations: building a computational neuropsychiatry for understanding the human brain in health and disease

Morten Kringelbach ( University of Oxford )

The study of human brain networks with in vivo neuroimaging has given
rise to the field of connectomics, furthered by advances in network
science and graph theory which have started to inform our understanding
of the topological and functional features of the healthy brain. Here,
our focus is on the disruption of human brain networks in
neuropsychiatric disorders and how the ability to understand the
underlying causal mechanisms requires whole-brain computational models
that can generate and predict the dynamical interactions and
consequences of structural and functional network over many timescales.
We review the methods and emerging results of combining connectomics
with generative whole-brain computational models to understand
neuropsychiatric disorders. This nascent field has shown remarkable
accuracy in mapping and predicting the spontaneous dynamics of the
healthy brain. Computational models can also shed light on task-based
brain dynamics and in particular the reinforcement reward learning and
prediction errors that play a key role in promoting our survival. The
subsequent breakdown of these in anhedonia has been proposed to be a
common problem in neuropsychiatric disorders which has raised great
expectations that computational models may be able describe the
underlying causal mechanisms and thus come to provide novel, more
effective therapeutic interventions to rebalance the diseased brain.
Future challenges for this emerging field include modelling the complex
interactions between genetics and epigenetics during development – and
generating sufficiently robust results to predict effective brain
interventions such as drug discovery and new targets for deep brain

Speaker bio

Professor Morten L Kringelbach initially trained as computer scientist but became interested in how to reverse engineer the human brain. His research uses a range of behavioural, neuroimaging, neurosurgical and computational methods for understanding the brain mechanisms of pleasure. Apart from being a lot of fun, his prizewinning research is important since it may offer novel and more effective ways to treat anhedonia, the lack of pleasure, which is a major component of affective disorders. Professor Kringelbach is a Fellow of the Association for Psychological Science and a member of the advisory board of Scientific American. He has published thirteen books, and over 250 scientific papers, chapters and other articles.

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