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Abstract #1740

Evaluating cerebrovascular reactivity dynamics through a Bayesian inference approach

Joana Pinto1, Martin Craig2,3, Paula Croal2,3, Nicholas P. Blockley2,4, Michael A. Chappell2,3, and Daniel P. Bulte1
1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, 2Sir Peter Mansfield Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 3Radiological Sciences, Mental Health & Clinical Neurosciences, School of Medicine, Nottingham, United Kingdom, 4School of Life Sciences, University of Nottingham, Nottingham, United Kingdom


Cerebrovascular reactivity (CVR) has been shown to be an important parameter that is altered in certain pathologies. Analysis strategies using MRI data typically assume similar responses across the brain, neglecting the need and potential of modelling temporal features. In this work, we propose and test a novel method for the evaluation of CVR dynamics based on a variational Bayesian approach. Although this method yields similar CVR results to more standard approaches, it is more time efficient and has the potential to improve modelling by incorporating non-linear and biophysically informed models.

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