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Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization

View ORCID ProfileRomy Lorenz, View ORCID ProfileInes R. Violante, Ricardo Pio Monti, Giovanni Montana, Adam Hampshire, View ORCID ProfileRobert Leech
doi: https://doi.org/10.1101/128678
Romy Lorenz
1Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Medicine, Imperial College London, London W12 0NN, UK
2Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
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Ines R. Violante
1Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Medicine, Imperial College London, London W12 0NN, UK
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Ricardo Pio Monti
3Department of Mathematics, Imperial College London, London SW7 2AZ, UK
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Giovanni Montana
3Department of Mathematics, Imperial College London, London SW7 2AZ, UK
4Department of Biomedical Engineering, King’s College London, St Thomas’ Hospital, London SE1 7EH, UK
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Adam Hampshire
1Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Medicine, Imperial College London, London W12 0NN, UK
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Robert Leech
1Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Medicine, Imperial College London, London W12 0NN, UK
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Abstract

Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because different FPNs spatially overlap and are co-activated for diverse tasks. In order to characterize these networks involves studying how they activate across many different cognitive tasks, which previously has only been possible with meta-analyses. Here, building upon meta-analyses as a starting point, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that dissociate ventral and dorsal FPN activity from a large pool of tasks. We identify and subsequently refine two cognitive tasks (Deductive Reasoning and Tower of London) that are optimal for dissociating the FPNs. The identified cognitive tasks are not those predicted by meta-analysis, highlighting a different mapping between cognitive tasks and FPNs than expected. The optimization approach converged on a similar neural dissociation independently for the two different tasks, suggesting a possible common underlying functional mechanism and the need for neurally-derived cognitive taxonomies.

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Posted April 19, 2017.
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Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization
Romy Lorenz, Ines R. Violante, Ricardo Pio Monti, Giovanni Montana, Adam Hampshire, Robert Leech
bioRxiv 128678; doi: https://doi.org/10.1101/128678
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Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization
Romy Lorenz, Ines R. Violante, Ricardo Pio Monti, Giovanni Montana, Adam Hampshire, Robert Leech
bioRxiv 128678; doi: https://doi.org/10.1101/128678

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