Biography
Ben Seymour is a Wellcome Senior Clinical Fellow at Oxford University, working jointly at the Wellcome Centre for Integrative Neuroimaging and the Oxford Institute for Biomedical Engineering; and a visiting researcher at the Center for Information and Neural Networks (National Institute of Information and Communications Technology) in Osaka, and ATR labs (Kyoto).
Awards and Prizes
- Turing Fellow
- Fellow of the Royal Society of Arts
Most Recent Publications
Computational and neural mechanisms of statistical pain learning.
Mancini F, Zhang S & Seymour B (2022), Nature communications, 13(1), 6613
Confidence of probabilistic predictions modulates the cortical response to pain
Mulders D, Seymour B, Mouraux A & Mancini F (2022)
Neurofeedback Training without Explicit Phantom Hand Movements and Hand-Like Visual Feedback to Modulate Pain: A Randomized Crossover Feasibility Trial.
Yanagisawa T, Fukuma R, Seymour B, Tanaka M, Yamashita O et al. (2022), The journal of pain, S1526-5900(22)00368-6
Endogenous modulation of pain relief: evidence for dopaminergic but not opioidergic involvement
Desch S, Schweinhardt P, Seymour B, Flor H & Becker S (2022)
Learning the statistics of pain: computational and neural mechanisms
Mancini F, Zhang S & Seymour B (2021)
Research Interests
The lab addresses the computational and systems neuroscience of pain. This research is part theoretical: building realistic models of neuronal information processes to understand processes of pain perception and behaviour, and part experimental: testing these theories using a range of experimental methodologies, especially fMRI. The research aims to develop new technology-based therapies for treating pain in clinical populations, and looks to develop broader applications of brain inspired ‘safe learning systems’ in AI and robotics.
Current Projects
• UKRI Neurotechnology Network for Chronic pain (2022-2025). With colleagues from Glasgow, Cardiff, Plymouth and Exeter, this project lays a foundation for the development of state-of-the-art clinical neuroengineering and neurotechnology for pain and associated symptoms of fatigue and disability.
• Versus/MRC The Role of Learning in Chronic Musculoskeletal Pain (2022-2025). With colleagues in Cambridge, this project develops digital technologies for understanding how maladaptive cognitive and motor learning play a role in the pathogenesis of chronic pain.
• Wellcome Neural Mechanisms of Endogenous Analgesia (2020-2025). This project aims to identify how the brain switches off pain in various situations, using computational models of pain, and neuroimaging.
• IITP/KAIST Developing Safe AI systems (2018-2022). With colleagues in KAIST (South Korea), this project explores how to design adaptive autonomous control systems that prioritize safety.
• Versus/NICT Pain, Action, Movement Network (2017-2022). With colleagues in Japan, this project builds a collaborative network for studying the role of movement in pain.
Research Groups
Related Academics
Most Recent Publications
Computational and neural mechanisms of statistical pain learning.
Mancini F, Zhang S & Seymour B (2022), Nature communications, 13(1), 6613
Confidence of probabilistic predictions modulates the cortical response to pain
Mulders D, Seymour B, Mouraux A & Mancini F (2022)
Neurofeedback Training without Explicit Phantom Hand Movements and Hand-Like Visual Feedback to Modulate Pain: A Randomized Crossover Feasibility Trial.
Yanagisawa T, Fukuma R, Seymour B, Tanaka M, Yamashita O et al. (2022), The journal of pain, S1526-5900(22)00368-6
Endogenous modulation of pain relief: evidence for dopaminergic but not opioidergic involvement
Desch S, Schweinhardt P, Seymour B, Flor H & Becker S (2022)
Learning the statistics of pain: computational and neural mechanisms
Mancini F, Zhang S & Seymour B (2021)
Publications
Most Recent Publications
Computational and neural mechanisms of statistical pain learning.
Mancini F, Zhang S & Seymour B (2022), Nature communications, 13(1), 6613
Confidence of probabilistic predictions modulates the cortical response to pain
Mulders D, Seymour B, Mouraux A & Mancini F (2022)
Neurofeedback Training without Explicit Phantom Hand Movements and Hand-Like Visual Feedback to Modulate Pain: A Randomized Crossover Feasibility Trial.
Yanagisawa T, Fukuma R, Seymour B, Tanaka M, Yamashita O et al. (2022), The journal of pain, S1526-5900(22)00368-6
Endogenous modulation of pain relief: evidence for dopaminergic but not opioidergic involvement
Desch S, Schweinhardt P, Seymour B, Flor H & Becker S (2022)
Learning the statistics of pain: computational and neural mechanisms
Mancini F, Zhang S & Seymour B (2021)
