A Real-Time Functional Localization Method Based on Dynamic Connectivity for Coupled Brain Regions

In the rapidly growing field of dynamic connectivity (dyC)-based real-time functional magnetic resonance imaging (rtfMRI) neurofeedback training, participants receive feedback on the dynamical coupling of brain nodes based on real-time dynamic connectivity measurements between multiple regions-of-interest. Given the fundamental role of regions-of-interest in this context, their signal-to-noise-ratio is critical. In closely related activity-based rtfMRI neurofeedback, region-of-interest selection is guided by functional localization methods informed by the BOLD-signal during a functional localizer experiment conducted prior to neurofeedback. However, methods that account for dynamic coupling of multiple brain regions remain lacking. Here, we develop a method based on dynamic connectivity between two brain areas that aligns with conventional activity-based localization approaches and is applicable for rtfMRI-NF. The proposed pipeline adapts processing steps from activity-based methods and was successfully implemented in a dyC-informed neurofeedback study. We observed a significant increase in dyC signal-to-noise-ratio during the localizer experiment and the first three blocks of neurofeedback training. Importantly, dyC values also correlated with anxiodepressive symptom levels, serving as proof of sensitivity, as changes in dyC reflect not only task-related neural dynamics but also individual differences in anxiodepressive traits. Our findings demonstrate that functional localization methods can be extended to dynamic connectivity, improving rtfMRI feedback accuracy, and with evidence to serve as sensitive measure for emotional states. This approach may enable precise targeting of coupled brain regions in neurofeedback and holds potential for personalized clinical applications, with broader implications beyond neurofeedback discussed.