Neuro-RDM: An Explainable Neural Network Landscape of Reaction-Diffusion Model for Cognitive Task Recognition

Abstract

AbstractThe functional neural imaging technology sheds new light on characterizing the neural activity at each brain region and the information exchange from region to region. However, it is challenging to reverse-engineer the working mechanism of brain function and behavior through the evolving functional neuroimages. In this work, we conceptualize that the ensemble of evolving neuronal synapses forms a dynamic system of functional connectivity, where the system behavior (aka. Brain state) of such a reaction-diffusion process can be formulated by a set of trainable PDEs (partial differential equations). To that end, we first introduce a PDE-based reaction-diffusion model (RDM) to jointly characterize the propagation of brain states throughout the functional brain network as well as the non-linear interaction between brain state and neural activity manifested in the BOLD (blood-oxygen-level-dependent) signals. Next, we translate the diffusion and reaction processes formulated in the PDE into a graph neural network, where the driving force is to establish the mapping from the evolution of brain states to the known cognitive tasks. By doing so, the layer-by-layer learning scenario allows us to not only fine-tune the RDM model for predicting cognitive tasks but also explain how brain functions support cognitive status with a great neuroscience insight. We have evaluated our proof-of-concept approach on both simulated data and functional neuroimages from HCP (Human Connectome Project) database. Since our neuro-RDM combines the power of deep learning and insight of dynamic systems, our method has significantly improved recognition performance in terms of accuracy and robustness to the non-neuronal noise, compared with the conventional deep learning models.KeywordsPartial differential equationRecurrent neural networkFunctional MRIBrain state recognition