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A Bayesian optimisation approach for rapidly mapping residual network function in stroke

View ORCID ProfileRomy Lorenz, Michelle Johal, Frederic Dick, Adam Hampshire, Robert Leech, Fatemeh Geranmayeh
doi: https://doi.org/10.1101/2020.07.03.186197
Romy Lorenz
1MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
2Stanford University, 450 Serra Mall, Stanford, CA 94305, US
3Max-Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04303, Germany
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  • For correspondence: romy.lorenz@mrc-cbu.cam.ac.uk fatemeh.geranmayeh00@imperial.ac.uk
Michelle Johal
4Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London W12 0NN, UK
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Frederic Dick
5Birkbeck/UCL Centre for Neuroimaging, Birkbeck University, London WC1H 0AP, UK
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Adam Hampshire
4Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London W12 0NN, UK
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Robert Leech
5Birkbeck/UCL Centre for Neuroimaging, Birkbeck University, London WC1H 0AP, UK
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Fatemeh Geranmayeh
4Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London W12 0NN, UK
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  • For correspondence: romy.lorenz@mrc-cbu.cam.ac.uk fatemeh.geranmayeh00@imperial.ac.uk
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Abstract

Post-stroke cognitive and linguistic impairments are debilitating conditions, with current therapies only showing small improvements. Domain-general brain networks seem to play a critical role in stroke recovery and characterising their residual function with functional neuroimaging (fMRI) has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is currently challenging in patients as such detailed characterisation requires too many different cognitive tasks. Here, we use neuroadaptive Bayesian optimisation to overcome this problem, an approach combining real-time fMRI with machine-learning. By intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. This approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting diverse neurological and psychiatric conditions.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 04, 2020.
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A Bayesian optimisation approach for rapidly mapping residual network function in stroke
Romy Lorenz, Michelle Johal, Frederic Dick, Adam Hampshire, Robert Leech, Fatemeh Geranmayeh
bioRxiv 2020.07.03.186197; doi: https://doi.org/10.1101/2020.07.03.186197
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A Bayesian optimisation approach for rapidly mapping residual network function in stroke
Romy Lorenz, Michelle Johal, Frederic Dick, Adam Hampshire, Robert Leech, Fatemeh Geranmayeh
bioRxiv 2020.07.03.186197; doi: https://doi.org/10.1101/2020.07.03.186197

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