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Neuroadaptive Bayesian optimization for cognitive neuroscientists

Dr. Romy Lorenz
University of Cambridge ~ MRC Cognition and Brain Sciences Unit

Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow experimental designs by considering only a small subset of possible experimental conditions. This limits the generalizability and reproducibility of many research findings. In this talk, I present an alternative approach that resolves these problems by combining real-time functional magnetic resonance imaging (fMRI) with a branch of machine learning, Bayesian optimization. Neuroadaptive Bayesian optimization is a non-parametric active sampling approach using Gaussian process regression. The approach allows to intelligently search through large experiment spaces with the aim to optimize a human subject’s brain response. It thus provides a powerful strategy to efficiently explore many more experimental conditions than is currently possible with standard neuroimaging methodology. In this talk, I will present results from three different studies where we applied the method to: (1) better understand the functional role of frontoparietal networks in healthy individuals, (2) map cognitive dysfunction in aphasic stroke patients, and (3) tailor non-invasive brain stimulation parameters to a particular research question. I will conclude my talk in discussing how Bayesian optimization can be combined with study preregistration to cover exploration, mitigating researcher bias more broadly and improving reproducibility.



functional neuroimaging
cognitive neuroscience
machine learning


Bayesian Modeling
Study design
Cognitive Neuromodeling
efficiency? Last updated 3 months ago

hi Romy! Nice talk! I was wondering whether you would be able to say how much more efficient this method is compared to simply trying out different tasks? Also, what were the tasks that you found to relate to the frontoparietal network?

Marieke Van Vugt 0 comments