Towards generative AI-based fMRI paradigms: reinforcement learning via real-time brain feedback

COEX 

In traditional human neuroimaging experiments, researchers create experimental paradigms with a psychological/behavioral validity to infer the corresponding neural correlates. Here, we introduce a novel approach called Reinforcement Learning via Brain Feedback (RLBF), that inverts the direction of inference; it seeks for the optimal stimulation or paradigm to maximize (or minimize) response in predefined brain regions or networks (fig.1). The stimulation/paradigm is found via a reinforcement learning algorithm (Kaelbling et al., 1996) that is rewarded based on real-time fMRI (Sulzer et al., 2013) data. Specifically, the reinforcement learning agent manipulates the paradigm space (e.g. via generative AI) to drive neural activity in a specific direction. Then, rewarded by measured brain responses, the agent gradually learns to adjust its choices to converge towards an optimal solution. Here, we present the results of a proof of concept study that aimed to confirm the viability of the proposed approach with simulated and empirical real-time fMRI data.