Keywords: Brain Connectivity, fMRI (resting state)We describe a generalizable, modular and explainable model for individualized representation learning of resting-state fMRI. The model consists of a “deep” base which learns representations that are unique to each individual brain through self-supervised learning, and “shallow” adds-on which are trained with supervised learning for different tasks of behavior prediction. The model is scalable to allow some add-on modules to be trainable without affecting others, and is explainable to identify brain structures responsible for individualized behavioral prediction.
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