Automatic design and optimization of MRI-based neurochemical sensors via reinforcement learning.

Magnetic resonance imaging (MRI) is a cornerstone of medical imaging, celebrated for its non-invasiveness, high spatial and temporal resolution, and exceptional soft tissue contrast, with over 100 million clinical procedures performed annually worldwide. In this field, MRI-based nanosensors have garnered significant interest in biomedical research due to their tunable sensing mechanisms, high permeability, rapid kinetics, and surface functionality. Extensive studies in the field have reported the use of superparamagnetic iron oxide nanoparticles (SPIONs) and proteins as a proof-of-concept for sensing critical neurochemicals via MRI. However, the signal change ratio and response rate of our SPION-protein-based in vitro dopamine and in vivo calcium sensors need to be further enhanced to detect the subtle and transient fluctuations in neurochemical levels associated with neural activities, starting from in vitro diagnostics. In this paper, we present an advanced reinforcement-learning-based computational model that treats sensor design as an optimal decision-making problem by choosing sensor performance as a weighted reward objective function. The adjustments of the SPION's and protein's three-dimensional configuration and magnetic moment establish a set of actions that can autonomously maximize the cumulative reward in the computational environment. Our new model first elucidates the sensor's conformation alteration behind the increment in T