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A novel account of fatigue using the tools of computational, cognitive neuroscience

Principal Supervisor: Dr Matthew Apps

Secondary Supervisor(s): Dr Katja Kornysheva / Dr Paul Muhle-Karbe 

University of Registration: University of Birmingham

BBSRC Research Themes: Understanding the Rules of Life (Neuroscience and Behaviour)

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Deadline: 4 January, 2024


Project Outline

Most daily tasks require the exertion of effort over an extended period of time. From a workout at the gym to deciding whether to persist with a mentally challenging task at work, much of our activities require us to keep deciding that effort is ‘worth it’. People differ widely in how able they are to persist, often attributing failure to ‘fatigue’. Fatigue is also one of the most common symptoms across all physical and mental health disorders. However, theories of fatigue across psychology and neuroscience have been unable to characterise one of its most fundamental features: it can transfer from one task to another. As a result there is no understanding why the fatigue caused by one effortful task (e.g. studying for an exam) can make you less motivated to exert effort into another (e.g. go for a run).

This project will develop and test a new theory of fatigue based on concepts from artificial intelligence and neural network modelling, that place emphasis on the similarity of task representations. The aim of the PhD will be to test if this can explain how fatigue transfers across behaviours in the motor and cognitive domains. The student will get training in computational and neural network modelling, brain imaging (fMRI/MEG), representational similarity analysis, and use these tools to develop and test a new theory of the brain mechanisms underlying fatigue. This will pave the way for a new understanding of fatigue across health and disease in the future.

This project brings together supervisory expertise in fatigue, computational modelling and brain imaging (Dr. Matthew Apps), motor control and imaging (Dr. Katja Kornysheva) and the use of neural network modelling and representational similarity analysis (Dr. Paul Muhle-Karbe). The student would get training in methods from psychology, neuroimaging and computational science, allowing them to develop a wide range of skills useful in academia or data science.

The aim of the Motivation and Social Neuroscience (MSN) lab is to understand the biological and psychological mechanisms of human motivation. MSN is currently funded by over £4m of grants from the BBSRC, Wellcome Trust and European Research Council. The lab is situated in the Centre for Human Brain Health, in the University of Birmingham, which has world-class facilities including MRI, MEG, Optically pumped MEG, Focused Ultrasound Stimulation, Transcranial Magnetic Stimulation, Functional Near Infrared Spectroscopy, and EEG. Interested candidates are strongly encouraged to get in contact ( ) for informal discussion about projects before submitting an application.

Relevant papers

(See: https://tinyurl.com/y6883xsg for more MSN papers)

  1. Müller, T., Klein-Flügge, M. C., Manohar, S., Husain, M. & Apps, M. Neural and computational mechanisms of momentary fatigue and persistence in effort-based choice. Nature Communications (2021).
  2. Yewbrey, R, Mantziara, M & Kornysheva, K. Cortical Patterns Shift from Sequence Feature Separation during Planning to Integration during Motor Execution. J. Neurosci. 43, 1742 (2023).
  3. Muhle-Karbe, P.S et al. Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex. Neuron (in press) doi:10.1101/2023.01.12.523762.
  4. Marcora, S. M., Staiano, W. & Manning, V. Mental fatigue impairs physical performance in humans. J Appl Physiol 106, 857 (2009).

Techniques

  • novel experimental design across cognitive and motor control domains
  • Programming (MATLAB/R/Pyhton)
  • computational modelling
  • neural network modelling
  • fMRI
  • MEG