Neuroadaptive Bayesian optimization for cognitive neuroscientists

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.