One of the primary goals of ‘explainable AI’ is the identification of a small subset of explanatory variables in an attempt to understand interesting phenomena. The Markov blanket constitutes one such subset, essential for tasks involving causal interpretation, prediction, and robustness. In medical imaging, identifying such variables is particularly important for achieving generalization across sites and mitigating domain shifts induced by scanner or population biases. Existing approaches based on the model-X knockoffs framework (Barber & Candès, 2015) provide finite-sample control of the false discovery rate (FDR) under the IID assumption. However, longitudinal data violate this assumption and exhibit temporal dependencies, non-stationarity, making the standard knockoff constructions invalid. In this work, we explore a principled extension of knockoff-based variable selection to time-varying systems by leveraging ideas from betting games and e-processes in sequential hypothesis testing. We explore its applicability to both synthetic datasets as well as test it on real-world longitudinal neuro-imaging data from ADNI.