Enhancing fMRI Decoded Neurofeedback with Co-adaptive Training: Simulation and Proof-of-principle Evidence.

A significant challenge for neurofeedback training research and related clinical applications, is participants' difficulty in learning to induce specific brain patterns during training. Here, we address this issue in the context of fMRI-based decoded neurofeedback (DecNef). Arguably, discrepancies between the data used to construct the decoder and the data used for neurofeedback training, such as differences in data distributions and experimental contexts, neural and non-neural noise, are likely the cause of the difficulties of the aforementioned participants. Here, we developed a co-adaptation procedure using standard machine learning algorithms. The procedure involves an adaptive decoder algorithm that is updated in real time based on its predictions across neurofeedback trials. First, we tested the procedure via simulations using a previous DecNef dataset and showed that decoder co-adaptation can improve performance during neurofeedback training. Importantly, a drift analysis demonstrated the stability of the co-adapted decoder throughout the neurofeedback training sessions. We then collected real time fMRI data in a DecNef training procedure to provide proof of concept evidence that co-adaptation enhances participant's ability to induce the target state during training. Thus, personalized decoders through co-adaptation can improve the precision and reliability of DecNef training protocols to target specific brain representations, with ramifications in translational research. The tools are made openly available to the scientific community.