A Bayesian deep learning framework for brain dynamic functional connectivity linked with cognition.

Recent neuroimaging studies have revealed that brain functional networks exhibit temporal fluctuations during cognitive tasks, sparking growing interest in dynamic functional connectivity (FC). However, few existing methods effectively incorporate temporal dynamics when examining the relationship between brain connectivity and cognition. In this work, we propose a Bayesian deep learning framework that decomposes FC into dynamic and stationary components, thereby disentangling transient fluctuations from stable network structures. By leveraging deep kernel learning, the model flexibly represents transient functions as continuous processes, allowing the data to guide the discovery of meaningful temporal patterns beyond conventional assumptions. Building on this representation, the dynamic component is modeled via a thresholded Gaussian process, which captures smooth temporal variations while inducing sparsity to highlight significant temporal signals. Simultaneously, the stationary component is modeled using a deep neural network with an integrated thresholding layer, enabling the learning of complex nonlinear relationships and enhancing interpretability by emphasizing key connections. This integrative approach balances model flexibility, temporal resolution, and interpretability, providing a powerful tool for predicting cognitive outcomes from functional neuroimaging data. We demonstrate that our method outperforms existing approaches in both prediction accuracy and feature selection through extensive simulations. Applied to data from the Adolescent Brain Cognitive Development study, our framework uncovers dynamic brain signals during cognitive tasks that significantly contribute to variations in cognitive performance.